2018

Bíl, M., Dobiáš, M., Andrášik, R., Bílová, M., Hejna, P., 2018. Cycling Fatalities: When A Helmet is Useless and when it Might Save Your Life. Safety Science 105C, 71–76. https://doi.org/10.1016/j.ssci.2018.02.005

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0925753517302059

| Download

Autopsy reports of 119 cyclists who died in two Czech regions between 1995 and 2013 as a result of traffic crashes were studied. In all the study cases, pathologists analyzed whether a helmet could have helped the cyclists survive the crash or not. The crash circumstances from the police reports were then evaluated. The results indicate that helmets could have helped the most in cases of single-vehicle crashes when cyclists fell off their bicycles or hit obstacles and in certain cases when an intracranial injury was the primary cause of death. Altogether 44 cyclists (37%) from this study could have survived if they had been wearing helmets during the crashes. Helmets would not have helped cyclists in most high-energetic crashes, especially when motor-vehicles or trains were involved. Some rear-end crashes outside urban areas also resulted in injuries when a helmet would not have helped. This study concludes that cyclists should wear helmets, but they should also be aware that it cannot protect them in particular situations. These facts should be incorporated into safety campaigns to prevent cyclists from feeling protected in such situations when helmets cannot help. Our results also support the building of cycling paths separate from traffic, particularly outside of urban areas.

2016

Bíl, M., Bílová, M., Dobiáš, M., Andrášik, R., 2016. Circumstances and Causes of Fatal Cycling Accidents in the Czech Republic. Traffic Injury Prevention 17 (4), 394–399. doi:10.1080/15389588.2015.1094183. https://doi.org/10.1080/15389588.2015.1094183

Show abstract |

http://www.tandfonline.com/doi/full/10.1080/15389588.2015.1094183

Objectives: The circumstances and causes of death of 129 cyclists registered in the Olomouc and the Zlín regions, the Czech Republic, between 2005 and 2013 were the subject of this study. Methods: We analyzed the autopsy reports, where the principal cause of death was stated, and obtained a detailed description of the circumstances recorded by the police officers. Results: Eighty-three cases (64.3% of the set) were collisions involving a motor vehicle. The driver was the guilty party in 57 cases (68.7%) and the cyclist in the remaining 26 cases (31.3%). The most frequent cause of the crash was connected with right of way (29 cases). Cars were involved in 52 cases; heavy vehicles, including buses, in 26 cases; and motorcycles in 5 cases. Single-vehicle crashes consisted of 43 (33.3%) cases. We divided this group into 3 subgroups based on whether the particular case could be attributed to a cyclist having lost control of the bicycle (31 cases) or to other particular causes. Sixty-eight cases (52.7%) of fatal outcomes were directly linked to intracranial injuries. Multiple injuries were the principal cause of death in 19 cases (14.7%), followed by hemorrhagic traumatic shock (12 cases, 9.3%). Seventy-two (55.8%) cyclists died immediately after the crash and 23 (17.8%) cyclists died within a day of the accident. Conclusions: Trucks were more dangerous to cyclists than cars at intersections, whereas cars were more dangerous on straight sections. The most important pattern was identified as a motor vehicle hitting a cyclist from behind on a straight road section. We identified a strong underestimation of natural death as a cause of cycling fatalities in the official police reports.

2015

Bíl, M., Andrášik, R., Kubeček, J., 2015. How comfortable are your cycling tracks? A new method for objective bicycle vibration measurement. Transportation Research Part C: Emerging Technologies 56, 415–425. https://doi.org/10.1016/j.trc.2015.05.007

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0968090X15001795

Cycling comfort consists of several factors. Their relevant values are important in the process of bicycle facility planning. Poor surface pavement quality manifests itself in terms of vibrations of a bicycle. This strongly influences the perception of a cycle track, general cycling comfort and the route choice as well. We introduce dynamic comfort index (DCI) which is capable of objectively describing the vibration properties of surface pavement on a track. The DCI is derived from data gathered when riding a bicycle equipped with a GPS device and an accelerometer. The most common types of devices were selected to make the DCI widely applicable. We tested DCI values on various bicycles and surface pavements. DCI values on individual cycling tracks were compared with the subjective feelings of 43 cyclists via questionnaires. A strong correlation (−0.94) was obtained between the objectively measured DCI values and the subjectively assessed evaluations. This makes the DCI approach transferable to any other environment. This method has been applied to an entire road network within the historical center of the city of Olomouc (Czech Republic). It can further be used by bicycle track administrators to monitor surface quality, by planners to obtain relevant surface pavement values, and by individual cyclists for optimal route choice.

2012

Bíl, M., Bílová, M., Kubeček, J., 2012. Unified GIS database on cycle tourism infrastructure. Tourism Management 33, 1554–1561. https://doi.org/10.1016/j.tourman.2012.03.002

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0261517712000477

Bicycles are used in the Czech Republic for commuting to work and for leisure time activities. This is reflected in the cycle trail administrators' offer to make the existing network denser, design new routes, mark their courses and install a complementary cycle infrastructure. However, extensive growth of these activities in the last years has led to the loss of overview information on the overall cycle trail network. That is the reason for producing a methodology of capturing and representing the information. A unified GIS database on the cycle infrastructure (UDCI) was created and includes a data collection system with the use of GPS, the coding of descriptive information on cycle trail segments and the administration of GIS layers in a topologic data model. The methodology of the UDCI application is demonstrated with a specific example of a cycle trail network in the South Bohemian region.

2010

Bíl, M., Bílová, M., Müller, I., 2010. Critical factors in fatal collisions of adult cyclists with automobiles. Accident Analysis and Prevention 42, 1632–1636. https://doi.org/10.1016/j.aap.2010.04.001

Show abstract |

http://www.sciencedirect.com/science/article/pii/S000145751000103X

This article evaluates, by means of multivariate regression, critical factors influencing the collisions of motor vehicles with adult (over 17 years) cyclists that result in fatal injury of cyclists. The analysis is based on the database of the Traffic Police of Czech Republic from the time period 1995–2007. The results suggest that the most consequential categories of factors under study are: inappropriate driving speed of automobile; the head-on crash; and night-time traffic in places without streetlights. The cyclists’ faults are of most serious consequence on crossroads when cyclists deny the right of way. Males are more likely to suffer a fatal injury due to a collision with a car than females. The most vulnerable age group are cyclists above 65 years. A fatal injury of a cyclist is more often driver's fault than cyclist's (598 vs. 370). In order to reduce the fatal risk, it is recommended to separate the road traffic of motor vehicles from bicyclists in critical road-sections; or, at least, to reduce speed limits there.

2022

Vodák, R., Křivánková, Z., Bíl, M. 2022. Identification of road links with the gravest network impacts when blocked concurrently. European Journal of Transport and Infrastructure Research, 22(1), 63–92.

Show abstract |

https://doi.org/10.18757/ejtir.2022.22.1.5657

The identification of a combination of links which can cause the gravest impact on network performance, when interrupted simultaneously, is of great practical importance. The links can be spatially dispersed due to an event such as an earthquake or due to a combination of accidents and disasters. This task is, thus, extremely computationally demanding when applied to real-world networks. We focused on approaches which are both capable of accomplishing this task and where the computational time is acceptable for application in practice. We tested three algorithms based on known heuristic methods: Simulated Annealing (SA), Guided Local Search (GLS) and Variable Neighborhood Search (VNS). The algorithms were modified in the sense of adjusting the searching neighborhood. All the algorithms were subsequently applied to four actual road networks in order to evaluate the impacts of complete simultaneous blockage of four and ten links. The results suggest that the modified SA algorithm identified scenarios with worse consequences than the algorithms based on GLS and VNS. The SA results, for the setting with four interrupted links, were even comparable with those obtained from a deterministic algorithm (which evaluates the entire state space). The algorithm based on SA was also performing best for situations with ten concurrently blocked links. The approach based on SA is thus suitable when modeling the potential impacts of events where a large number of concurrently blocked links is expected. Network managers will thus be able to monitor the immediate state of the network and potential risks related to network disintegration.

2019

Vodák, R., Bíl, M., Svoboda, T., Křivánková, Z., Kubeček, J., Rebok, T., Hliněný, P., 2019. A Deterministic Approach for Rapid Identification of the Critical Links in Networks. PLoS ONE 14(7): e0219658. https://doi.org/10.1371/journal.pone.0219658

Show abstract |

https://doi.org/10.1371/journal.pone.0219658

We introduce a rapid deterministic algorithm for identification of the most critical links which are capable of causing network disruptions. The algorithm is based on searching for the shortest cycles in the network and provides a significant time improvement compared with a common brute-force algorithm which scans the entire network. We used a simple measure, based on standard deviation, as a vulnerability measure. It takes into account the importance of nodes in particular network components. We demonstrate this approach on a real network with 734 nodes and 990 links. We found the worst scenarios for the cases with and without people living in the nodes. The evaluation of all network breakups can provide transportation planners and administrators with plenty of data for further statistical analyses. The presented approach provides an alternative approach to the recent research assessing the impacts of simultaneous interruptions of multiple links.

Bíl, M., Andrášik, R., Sedoník, J., 2019. Which curves are dangerous? A network-wide analysis of traffic crash and infrastructure data. Transportation Research Part A 120C, 252–260. https://doi.org/10.1016/j.tra.2019.01.001

Show abstract |

https://www.sciencedirect.com/science/article/pii/S096585641830819X

We conducted spatial analyses of traffic crashes, which took place in Czechia over 2010–2016, with respect to the road geometry data. The aim of the work was to identify hazardous road sub-segments where higher than expected numbers of traffic crashes occur.The entire Czech road network (58,200 km) was segmented at intersections into 39,074 between-intersection segments of varying lengths. Each road segment was further automatically sectioned, according to its horizontal alignment, into geometry-homogenous units (horizontal curves and tangents). Overall, 257,101 curves, defined as curved sections with radii below 2100 m, and 136,388 tangents, were identified. Subsequently, traffic crashes were joined to the respective geometrical units to determine their hazardousness. The degree of hazardousness was determined relatively, on a segment-by-segment basis, in order to eliminate the lack of precise traffic exposure data. In addition, the exact binomial test and Bayesian inference were used to identify the most hazardous horizontal curves.
It was found that, in general, the curves with a higher crash risk have lower radii than the other curves. We identified the geographical locations of all curves with a high crash hazard. We also ranked the curves according to the crash hazard. Approximately ten percent of road segments contained at least one hazardous horizontal curve. 6943 significantly hazardous curves were identified by the use of the exact binomial test. The Bayesian inference reduced this number to 1395 (0.31% of the entire road network) and ranked them according to the Bayes factor. The most hazardous curve was 45 m long and contained 8.7 traffic crashes per year. Its hazard rate accounted for 37.4. This state-wide analysis of primary data was conducted over an extremely short time (up to 3 days) as the result of an application of an efficient algorithm for automatic road curvature determination.

2018

Bíl, M., Andrášik, R., Sedoník, J., Cícha, V., 2018. ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation. PLoS ONE 13(12): e0208407. https://doi.org/10.1371/journal.pone.0208407

Show abstract |

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208407&type=printable

| Download

We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website.

Vodák, R., Bíl, M., Křivánková, Z., 2018. A modified ant colony optimization algorithm to increase the speed of the road network recovery process. International Journal of Disaster Risk Reduction 31C, 1092–1106. https://doi.org/10.1016/j.ijdrr.2018.04.004

Show abstract |

https://www.sciencedirect.com/science/article/pii/S2212420917304120

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When a disaster strikes many roads are blocked and the affected network may break up into a number of isolated parts. The reconnection of the network is therefore necessary for both relief distribution and planning of construction work. Shortening the time during which the road network is separated into isolated parts helps decrease indirect losses from disasters. The obstacles usually faced during the process of reconstruction include both the large number of blocked links and extensive affected areas (road networks). A reduction of the network into a much smaller complete graph and metaheuristic based on an ant colony optimization has been introduced to overcome this issue. We demonstrate that, for small networks, the metaheuristic produces the same results as other deterministic algorithms. We further show that the method is still a viable approach for large networks (723 nodes and 974 links, where we artificially blocked 46 links) when the NP-hard nature of this problem began to affect the computational time of the deterministic algorithms. We demonstrate how the various scenarios can be included into the algorithm. We finally introduce a new ranking of feasible solutions which enables the algorithm to minimize the time of reconstructions for all repair units. Reasonable results were obtained after five minutes of computation. There is nevertheless an up-to-38% improvement of the initial solution. The algorithm can also be used for both relief distribution, when no roads were damaged, and for planning of construction work when damaged roads occur.

2017

Bíl, M., Andrášik. R., Nezval, V., Bílová, M., 2017. Identifying Locations along Railway Networks with the Highest Tree Fall Hazard. Applied Geography 87, 45–53. https://doi.org/10.1016/j.apgeog.2017.07.012

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0143622817301819

Disruptions of railway traffic have many reasons. Tree falls onto railway tracks or overhead lines rank among the most common causes of disruptions of a natural origin. 2039 tree-fall events, containing up to 70 individual trees per event, were registered on the Czech railway network between 2012 and 2015. 32% of them were directly caused by 14 weather extremes during which more than 20 concurrent tree-fall events were registered. Moreover, 12 train derailments due to fallen trees were registered on Czech railways within the same period. We combined land use data along railway tracks and data on tree falls. Land use and railway tracks data were obtained from a freely available Open Street Map database. The tree fall hazard was then computed using empirical data, data on land use and a generalized rule of succession. The clustering approach was also applied to focus on localities where tree falls were concentrated regardless of the resulting segment hazard. There were 59 rail track segments (out of 2960) with the highest tree fall hazard and 267 clusters were finally identified. The clusters and the most hazardous railway segments will be among the first in the process of line side vegetation monitoring in order to minimize potential losses from tree fall. The presented method can be widely applicable elsewhere.

2016

Andrášik, R.; Bíl, M., 2016. Efficient Road Geometry Identification from Digital Vector Data. Journal of Geographical Systems 18(3), 249–264. https://doi.org/10.1007/s10109-016-0230-1

Show abstract |

https://link.springer.com/article/10.1007/s10109-016-0230-1

A new method for the automatic identification of road geometry from digital vector data is presented. The method is capable of efficiently identifying circular curves with their radii and tangents (straight sections). The average error of identification ranged from 0.01 to 1.30 % for precisely drawn data and 4.81 % in the case of actual road data with noise in the location of vertices. The results demonstrate that the proposed method is faster and more precise than commonly used techniques. This approach can be used by road administrators to complete their databases with information concerning the geometry of roads. It can also be utilized by transport engineers or traffic safety analysts to investigate the possible dependence of traffic accidents on road geometries. The method presented is applicable as well to railroads and rivers or other line features.

2015

Bíl, M., Andrášik, R., Kubeček, J., 2015. How comfortable are your cycling tracks? A new method for objective bicycle vibration measurement. Transportation Research Part C: Emerging Technologies 56, 415–425. https://doi.org/10.1016/j.trc.2015.05.007

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0968090X15001795

Cycling comfort consists of several factors. Their relevant values are important in the process of bicycle facility planning. Poor surface pavement quality manifests itself in terms of vibrations of a bicycle. This strongly influences the perception of a cycle track, general cycling comfort and the route choice as well. We introduce dynamic comfort index (DCI) which is capable of objectively describing the vibration properties of surface pavement on a track. The DCI is derived from data gathered when riding a bicycle equipped with a GPS device and an accelerometer. The most common types of devices were selected to make the DCI widely applicable. We tested DCI values on various bicycles and surface pavements. DCI values on individual cycling tracks were compared with the subjective feelings of 43 cyclists via questionnaires. A strong correlation (−0.94) was obtained between the objectively measured DCI values and the subjectively assessed evaluations. This makes the DCI approach transferable to any other environment. This method has been applied to an entire road network within the historical center of the city of Olomouc (Czech Republic). It can further be used by bicycle track administrators to monitor surface quality, by planners to obtain relevant surface pavement values, and by individual cyclists for optimal route choice.

Bíl, M., Vodák, R., Kubeček, J., Bílová, M., Sedoník, J., 2015. Evaluating Road Network Damage Caused by Natural Disasters in the Czech Republic between 1997 and 2010. Transportation Research Part A: Policy and Practice 80, 90–103. https://doi.org/10.1016/j.tra.2015.07.006

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0965856415001883

Road networks play a vital role in maintaining a functioning modern society. Many events perceptibly affect the transport supply along these networks, especially natural disasters such as floods, landslides, and earthquakes. Contrary to more common disruptions of traffic from accidents, or maintenance closures, natural disasters are capable of destroying large numbers of roads and usually cover vast areas. When evaluating network damage no single measure alone is able to describe the full extent of network destruction. In this study, we investigated six highly damaging natural disasters, which occurred in the Czech Republic between 1997 and 2010. They were all induced by extreme rainfall or by rapid snowmelt and resulted in floods and landslides. Their impacts are evaluated with respect to the damage to road networks and decreased serviceability. For mutual comparison of the impacts and their analysis we used several criteria, described in the paper, related to economic impacts, physical harm to individuals and infrastructures, and the effects on connectivity and serviceability. We also introduced a new measure based on the network efficiency index which takes into account the importance of nodes based on their population. Moreover, we provide a detailed analysis of one such event in July 1997 that significantly affected the road network of the Zlín region.

Vodák, R., Bíl, M., Sedoník, J., 2015. Network robustness and random processes. Physica A 428, 368–382. https://doi.org/10.1016/j.physa.2015.01.056

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0378437115000758

We introduce two new measures of network robustness and apply them to four different strategies. The measures are independent from the number of nodes in the network and have the strong potential to cover a large portfolio of applications. Using the Monte-Carlo methods, we demonstrate how to approximate the measures. The methods are based on random interruption of links with suitable constraints which represent the above-mentioned strategies. We introduce two networks with obvious varying robustness to demonstrate the measures. We also demonstrate how to employ the measures in order to improve the robustness of the networks by adding one new link. We further indicate that the measures are able to identify the infrequently connected parts of the network and suggest the most appropriate improvement. We also discuss the consequences of the obtained results and the possible applications of the measures.

2014

Bíl, M., Kubeček, J., Andrášik, R., 2014. An Epidemiological Approach to Determining the Risk of Road Damage due to Landslides. Nat Haz. 73 (4), 1323–1335. https://doi.org/10.1007/s11069-014-1141-4

Show abstract |

https://link.springer.com/article/10.1007/s11069-014-1141-4

Disruption of segments of roads can have a significant impact on the vulnerability of the entire network. Natural disasters are frequent causes of disruptions of this kind. This article focuses on determining the risk of road disruptions due to landslides. Our approach is based on methodology widely used in the field of epidemiology. We had available data on the location of the landslides, the road network and a list of the disrupted road segments. With the use of a 2 × 2 table, we determined the relationship between landslide data and road segment disruptions and derived the risk coefficient based on the number of landslides in the vicinity of the road and its length. The result is a disruption risk map with risk coefficients ranging from 0 to 47.94. In order to distinguish the most risky segments, we calculated a threshold of 12.40 with the use of a risk breakdown in a group of segments without damage. Nineteen percentage (402 km) of the road network in the Zlín region (Czech Republic), where the methodology was applied, is located beyond this threshold. The benefits of this approach stem from its speed and potential to define the most risky areas on which a detailed geomorphologic analysis can be focused.

2012

Bíl, M., Bílová, M., Kubeček, J., 2012. Unified GIS database on cycle tourism infrastructure. Tourism Management 33, 1554–1561. https://doi.org/10.1016/j.tourman.2012.03.002

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0261517712000477

Bicycles are used in the Czech Republic for commuting to work and for leisure time activities. This is reflected in the cycle trail administrators' offer to make the existing network denser, design new routes, mark their courses and install a complementary cycle infrastructure. However, extensive growth of these activities in the last years has led to the loss of overview information on the overall cycle trail network. That is the reason for producing a methodology of capturing and representing the information. A unified GIS database on the cycle infrastructure (UDCI) was created and includes a data collection system with the use of GPS, the coding of descriptive information on cycle trail segments and the administration of GIS layers in a topologic data model. The methodology of the UDCI application is demonstrated with a specific example of a cycle trail network in the South Bohemian region.

2019

Keken, Z., Sedoník, J., Kušta, T., Andrášik, R., Bíl, M., 2019. Roadside vegetation influences clustering of ungulate vehicle collisions. Transportation Research Part D: Transport and Environment 73, 381–390. https://doi.org/10.1016/j.trd.2019.07.013

Show abstract |

https://www.sciencedirect.com/science/article/pii/S1361920918311659

The aim of this study was to identify landscape-related factors which could explain the concentration of traffic crashes with large ungulates in a forest environment. We worked with ungulate-vehicle collisions which took place on the Czech road network in the period 2014–2016 using the application Srazenazver.cz. With the KDE+ method, we chose the most significant hotspots with linkage to forest. For comparison we randomly selected control localities outside the KDE+ hotspots (i.e., with very low level of ungulate-vehicle collisions) but still in the forest area. A set of photos were taken at each hotspot and control locality (2 orthophotos and 4 driver views). Each set of images was then visually analysed by two independent evaluators. They did not know which set of images were for hotspots and which set of images were for control localities. From the point of view of the overall score, it cannot be said that these two groups of locations differ (p-value 0.3575, Kolmogorov-Smirnov test). The only attribute that demonstrated a difference between hotspots and randomly selected locations was “attractiveness (quality food source and cover) of the immediate vicinity of the transport infrastructure for the ungulates” (p-value 0.0016, Kolmogorov-Smirnov test). Results confirm the importance of landscape management in the surroundings of transport infrastructure, especially in its immediate vicinity. However, they did not confirm the possibility that the most dangerous locations from the UVC point of view could be identified on the basis of landscape composition and the overall state of vegetation around transport infrastructure.

Bíl, M., Andrášik, R., Sedoník, J., 2019. A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography 107, 82–90. https://doi.org/10.1016/j.apgeog.2019.04.008

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0143622818309081

A number of traffic crash databases at present contain the precise positions and dates of these events. This feature allows for detailed spatiotemporal analysis of traffic crash patterns.We applied a clustering method for identification of traffic crash hotspots to the rural parts of primary roads in the Czech road network (3,933 km) where 55,296 traffic crashes occurred over 2010 – 2018. The data were analyzed using a 3-year time window which moved forward with a one-day step as an elementary temporal resolution. The spatiotemporal behavior of hotspots could therefore be analyzed in great detail.All the identified hotspots, during the monitored nine-year period, covered between 6.8% and 8.2% of the entire road network length in question. The percentage of traffic crashes within the hotspots remained stable over time at approximately 50%. Three elementary types of hotspots were identified when analyzing spatiotemporal crash patterns: hotspot emergence, stability and disappearance. Only 100 hotspots were stable (remained in approximately the same position) over the entire nine-year period. This approach can be applied to any traffic-crash time series when the precise positions and date of crashes are available.

Bíl, M., Andrášik, R., Duľa, M., Sedoník, J., 2019. On reliable identification of factors influencing wildlife-vehicle collisions along roads. Journal of Environmental Management 237C, 297–304. https://doi.org/10.1016/j.jenvman.2019.02.076

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0301479719302270

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

Bíl, M., Andrášik, R., Sedoník, J., 2019. Which curves are dangerous? A network-wide analysis of traffic crash and infrastructure data. Transportation Research Part A 120C, 252–260. https://doi.org/10.1016/j.tra.2019.01.001

Show abstract |

https://www.sciencedirect.com/science/article/pii/S096585641830819X

We conducted spatial analyses of traffic crashes, which took place in Czechia over 2010–2016, with respect to the road geometry data. The aim of the work was to identify hazardous road sub-segments where higher than expected numbers of traffic crashes occur.The entire Czech road network (58,200 km) was segmented at intersections into 39,074 between-intersection segments of varying lengths. Each road segment was further automatically sectioned, according to its horizontal alignment, into geometry-homogenous units (horizontal curves and tangents). Overall, 257,101 curves, defined as curved sections with radii below 2100 m, and 136,388 tangents, were identified. Subsequently, traffic crashes were joined to the respective geometrical units to determine their hazardousness. The degree of hazardousness was determined relatively, on a segment-by-segment basis, in order to eliminate the lack of precise traffic exposure data. In addition, the exact binomial test and Bayesian inference were used to identify the most hazardous horizontal curves.
It was found that, in general, the curves with a higher crash risk have lower radii than the other curves. We identified the geographical locations of all curves with a high crash hazard. We also ranked the curves according to the crash hazard. Approximately ten percent of road segments contained at least one hazardous horizontal curve. 6943 significantly hazardous curves were identified by the use of the exact binomial test. The Bayesian inference reduced this number to 1395 (0.31% of the entire road network) and ranked them according to the Bayes factor. The most hazardous curve was 45 m long and contained 8.7 traffic crashes per year. Its hazard rate accounted for 37.4. This state-wide analysis of primary data was conducted over an extremely short time (up to 3 days) as the result of an application of an efficient algorithm for automatic road curvature determination.

2018

Bíl, M., Andrášik, R., Sedoník, J., Cícha, V., 2018. ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation. PLoS ONE 13(12): e0208407. https://doi.org/10.1371/journal.pone.0208407

Show abstract |

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208407&type=printable

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We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website.

Favilli, F., Bíl, M., Sedoník, J., Andrášik, R., Kasal, P., Agreiter, A., Streifeneder, T., 2018. Application of KDE+ software to identify collective risk hotspots of ungulate-vehicle collisions in South Tyrol, Northern Italy. European Journal of Wildlife Research 64:59. https://doi.org/10.1007/s10344-018-1214-x

Show abstract |

https://link.springer.com/article/10.1007/s10344-018-1214-x

This paper is the first dealing with animal-vehicle collisions (AVC) with red and roe deer in South Tyrol, Northern Italy. The Autonomous Province of Bolzano (South Tyrol) has been collecting AVC data since 2012 on the entire provincial road network. Each year, AVC data accounted for more than 700 cases per year, with several socioeconomic and ecological implications. The aim of this research is to identify the locations where AVC occur more frequently than expected (hotspots) and better outline subsequent implementation of mitigation measures. For an effective identification of AVC hotspots, we applied a combined methodology of temporal and spatial analysis on AVC data collected on the South Tyrol road network in the years 2012–2014. AVC data enabled the identification of the temporal patterns, which showed different behaviors of the two target species in close proximity of the road network and throughout the 12 months. The KDE+ software applied to the 2012–2014 AVC database allowed for spatial analysis and the identification of hotspots, i.e., the road sections having the highest risk for drivers. The integration of the results, coming from the abovementioned methodologies, contributes to a detailed assessment of roads that would allow the identification of the local contributing factors and a base-line of potential problematic areas that will highlight the need for further investigation to assess whether the risk-rank is accurate and allocate effectively limited resources to a feasible number of identified hotspots and reduce the current degree of AVC in the South Tyrolean road network.

Bartonička, T., Andrášik, R., Duľa, M., Sedoník, J., Bíl, M., 2018. Identification of Local Factors Causing Clustering of Animal-Vehicle Collisions. Journal of Wildlife Management 82, 940–947. https://doi.org/10.1002/jwmg.21467

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https://onlinelibrary.wiley.com/doi/epdf/10.1002/jwmg.21467

Effective measures reducing risk of animal-vehicle collisions (AVC) require defining high-risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individua l AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non-random (clustered) occurrenc e of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE þ methods) identified factors causing local clustering of AVCs. Distance from forest ( < 350 m) or linear vegetation were important factors for estimating presence of clusters of AVCs; in open areas, AVC clusters were absent. Further research on effectiveness of measures reducing risk of AVC should focus on clusters of AVCs, not on the individua l AVC. We recommend that state transportati on agencies focus mitigation actions in forested areas.

Bíl, M., Dobiáš, M., Andrášik, R., Bílová, M., Hejna, P., 2018. Cycling Fatalities: When A Helmet is Useless and when it Might Save Your Life. Safety Science 105C, 71–76. https://doi.org/10.1016/j.ssci.2018.02.005

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https://www.sciencedirect.com/science/article/pii/S0925753517302059

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Autopsy reports of 119 cyclists who died in two Czech regions between 1995 and 2013 as a result of traffic crashes were studied. In all the study cases, pathologists analyzed whether a helmet could have helped the cyclists survive the crash or not. The crash circumstances from the police reports were then evaluated. The results indicate that helmets could have helped the most in cases of single-vehicle crashes when cyclists fell off their bicycles or hit obstacles and in certain cases when an intracranial injury was the primary cause of death. Altogether 44 cyclists (37%) from this study could have survived if they had been wearing helmets during the crashes. Helmets would not have helped cyclists in most high-energetic crashes, especially when motor-vehicles or trains were involved. Some rear-end crashes outside urban areas also resulted in injuries when a helmet would not have helped. This study concludes that cyclists should wear helmets, but they should also be aware that it cannot protect them in particular situations. These facts should be incorporated into safety campaigns to prevent cyclists from feeling protected in such situations when helmets cannot help. Our results also support the building of cycling paths separate from traffic, particularly outside of urban areas.

Bíl, M., Andrášik, R., Bartonička, T., Křivánková, Z., Sedoník, J., 2018. An Evaluation of Odor Repellent Effectiveness in Prevention of Wildlife-Vehicle Collisions. Journal of Environmental Management 205C, 209–214. https://doi.org/10.1016/j.jenvman.2017.09.081

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0301479717309568

Wildlife-vehicle collisions (WVC) amount to 11 % of all registered traffic crashes in the Czech Republic causing, apart from numerous deaths and serious injuries to animals, property damage and injuries to car passengers. Odor repellents have the potential to lower the overall number of WVC and allow animals to cross roads at the same time. We tested the effectiveness of odor repellent preparation in prevention of WVC. 18 places were selected on the Czech road network where WVC were concentrated on the basis of traffic crash data. Control sections on the same road segments were also delimited in order to keep the traffic intensities constant. We applied a Before-After-Control-Impact (BACI) study design to control not only the effect of the measures but also the expected natural variations in wildlife populations over time. Data were compared before and after odor repellent installations. Wildlife carcass gathering was carried out during the spring and autumn. We also used the police crash database to supplement carcass data when no field works were carried out. 201 killed mammals (roe deer and wild boars) were identified in total over 47 months. We applied a Bayesian approach as only a limited numbers of WVC were available. A WVC decrease between 26 – 43 % can be expected on the treated road sections. These numbers are, however, up to three-times lower than those claimed by producers of odor preparations.

2017

Bíl, M., Kubeček, J., Sedoník, J., Andrášik. R., 2017. Srazenazver.cz: A system for evidence of animal-vehicle collisions along transportation networks. Biological Conservation 213PA, 167–174. https://doi.org/10.1016/j.biocon.2017.07.012

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http://www.sciencedirect.com/science/article/pii/S000632071730263X

We present a system for state-wide evidence of animal-vehicle collisions (AVC). The primary part of this system is a geographic database which is connected to a web-map application. AVC data come from the Police via an online system of traffic incidents (JSDI) and from volunteers through a web or mobile interface. Data are processed using automatic scripts which identify data errors and perform spatial analyses. The application automatically computes AVC hotspots every midnight and crash densities along road sections. Hunter area administrators consequently have an overview of their areas. More than 40,000 records are currently included in this database. 50% of them were added over the last two years when it was launched. The majority of data (90%) came from JSDI. The species is known for 44% of JSDI records. The majority of the identified species were roe deer (75%), followed by wild boar (15%). Roe deer crashes occur most frequently in May within 2 h after sunset. 32.5% (56.4%) of these crashes occur within 1 h (2 h) before or after sunset or sunrise. For wild boar, the values are less distinctive (19.4% and 37.7%). Approximately 1800 AVC hotspots, which cover 0.5% of the Czech road network, were detected and visualized on a map.

2016

Bíl, M., Bílová, M., Dobiáš, M., Andrášik, R., 2016. Circumstances and Causes of Fatal Cycling Accidents in the Czech Republic. Traffic Injury Prevention 17 (4), 394–399. doi:10.1080/15389588.2015.1094183. https://doi.org/10.1080/15389588.2015.1094183

Show abstract |

http://www.tandfonline.com/doi/full/10.1080/15389588.2015.1094183

Objectives: The circumstances and causes of death of 129 cyclists registered in the Olomouc and the Zlín regions, the Czech Republic, between 2005 and 2013 were the subject of this study. Methods: We analyzed the autopsy reports, where the principal cause of death was stated, and obtained a detailed description of the circumstances recorded by the police officers. Results: Eighty-three cases (64.3% of the set) were collisions involving a motor vehicle. The driver was the guilty party in 57 cases (68.7%) and the cyclist in the remaining 26 cases (31.3%). The most frequent cause of the crash was connected with right of way (29 cases). Cars were involved in 52 cases; heavy vehicles, including buses, in 26 cases; and motorcycles in 5 cases. Single-vehicle crashes consisted of 43 (33.3%) cases. We divided this group into 3 subgroups based on whether the particular case could be attributed to a cyclist having lost control of the bicycle (31 cases) or to other particular causes. Sixty-eight cases (52.7%) of fatal outcomes were directly linked to intracranial injuries. Multiple injuries were the principal cause of death in 19 cases (14.7%), followed by hemorrhagic traumatic shock (12 cases, 9.3%). Seventy-two (55.8%) cyclists died immediately after the crash and 23 (17.8%) cyclists died within a day of the accident. Conclusions: Trucks were more dangerous to cyclists than cars at intersections, whereas cars were more dangerous on straight sections. The most important pattern was identified as a motor vehicle hitting a cyclist from behind on a straight road section. We identified a strong underestimation of natural death as a cause of cycling fatalities in the official police reports.

Bíl, M., Andrášik, R., Svoboda, T., Sedoník, J., 2016. The KDE+ software: a tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology 31, 231–237. https://doi.org/10.1007/s10980-015-0265-6

Show abstract |

https://link.springer.com/article/10.1007/s10980-015-0265-6

Context: Objective identification of locations on transportation networks, where animal-vehicle collisions (AVC) occur more frequently than expected (hotspots), is an important step for the effective application of mitigation measures. Objectives: We introduce the KDE+ software which is a programmed version of the KDE+ method for effective identification of traffic accident hotspots. The software can be used in order to analyze animal-vehicle collision data. Methods: The KDE+ method is based on principles of Kernel Density Estimation (KDE). The symbol ‘+’ indicates that the method allows for the objective selection of significant clusters and for the ranking of the hotspots. It is also simultaneously applicable to an unlimited number of road segments. Results: We applied the KDE+ method to the entire Czech road network. The hotspots were ranked according to their significance. The resulting hotspots represent a short overall road length which should require a more detailed assessment in the field. The 100 most important clusters of AVC represent, for example, only 19.7 km of the entire road network (37,469 km). Conclusions: We present an objective method for hotspots identification which can be used for AVC data. This method is unique because it determines the significance level of hotspots in an objective way. The prioritization of hotspots allows a transportation manager to effectively allocate resources to a feasible number of identified hotspots. We describe the software, data preparation and present the KDE+ application to AVC data.

2013

Bíl, M., Andrášik, R., Janoška, Z., 2013. Identification of Hazardous Road Locations of Traffic Accidents by means of Kernel Density Estimation and Cluster Significance Evaluation. Accident Analysis and Prevention 55, 265–273. https://doi.org/10.1016/j.aap.2013.03.003

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0001457513000912

This paper proposes a procedure which evaluates clusters of traffic accident and organizes them according to their significance. The standard kernel density estimation was extended by statistical significance testing of the resulting clusters of the traffic accidents. This allowed us to identify the most important clusters within each section. They represent places where the kernel density function exceeds the significance level corresponding to the 95th percentile level, which is estimated using the Monte Carlo simulations. To show only the most important clusters within a set of sections, we introduced the cluster strength and cluster stability evaluation procedures. The method was applied in the Southern Moravia Region of the Czech Republic.

2010

Bíl, M., Bílová, M., Müller, I., 2010. Critical factors in fatal collisions of adult cyclists with automobiles. Accident Analysis and Prevention 42, 1632–1636. https://doi.org/10.1016/j.aap.2010.04.001

Show abstract |

http://www.sciencedirect.com/science/article/pii/S000145751000103X

This article evaluates, by means of multivariate regression, critical factors influencing the collisions of motor vehicles with adult (over 17 years) cyclists that result in fatal injury of cyclists. The analysis is based on the database of the Traffic Police of Czech Republic from the time period 1995–2007. The results suggest that the most consequential categories of factors under study are: inappropriate driving speed of automobile; the head-on crash; and night-time traffic in places without streetlights. The cyclists’ faults are of most serious consequence on crossroads when cyclists deny the right of way. Males are more likely to suffer a fatal injury due to a collision with a car than females. The most vulnerable age group are cyclists above 65 years. A fatal injury of a cyclist is more often driver's fault than cyclist's (598 vs. 370). In order to reduce the fatal risk, it is recommended to separate the road traffic of motor vehicles from bicyclists in critical road-sections; or, at least, to reduce speed limits there.

2023

Bíl, M., Andrášik, R., Kušta, T. Bartonička, T. (2023) Ungulate-vehicle crashes peak a month earlier than 38 years ago due to global warming. Climatic Change 176, 84.

Show abstract |

https://doi.org/10.1007/s10584-023-03558-5

Cunneyworth, P. M. K., Andrášik, R., & Bíl, M. (2023). An effect of canopy bridges on monkey vehicle collision hotspots: Spatial and spatiotemporal analyses. American Journal of Primatology, e23492.

Show abstract |

https://doi.org/10.1002/ajp.23492

Almost one‐quarter of primate species are reported to be involved in vehicle collisions. To mitigate these collisions, canopy bridges are used though their effectiveness is not broadly substantiated. We studied bridge impact on 23 years of vehicle collisions (2000–2022: N = 765) with colobus (Colobus angolensis palliatus), Sykes' (Cercopithecus mitis albogularis), and vervet (Chlorocebus pygerythrus hilgerti) monkeys in Diani, Kenya. Along a 9 km road, collisions did not decrease over the study duration, although bridges increased from 8 to 30. Using the kernel density estimation plus (KDE+) method, collisions appeared highly concentrated at some locations. These concentrations, called hotspots, represent hazardous road segments, though the hotspots for all three species overlapped for only 3% of the road length. We then inspected the collision hotspots over time, using the spatiotemporal extension of the KDE+ method. We compared hotspot presence in the 3 years before and after bridge installation to determine if bridges mitigated these hotspots. Hotspots disappeared for ~60% of bridges postinstallation, suggesting that bridges effectively reduce some collisions. However, of the bridges installed in locations that were not hotspots, 13% had hotspots emerge. Surprisingly, regardless of preinstallation hotspot occurrence, almost one‐fifth of bridges had postinstallation hotspots. To understand the extent to which bridges mitigate collisions, other factors need consideration, including species attributes and crossing behavior, and road features and vehicle volume. We used the novel analytical method because it best suited our data set, given the challenges of determining the bridge impact zone and the low collision frequency.

2022

Nezval, V., Andrášik, R., Bíl, M. 2022. Vegetation fires along the Czech rail network. Fire Ecology, 18, 15.

Show abstract |

https://fireecology.springeropen.com/articles/10.1186/s42408-022-00141-8

In the past, fires around railways were often associated with steam locomotives. Although steam locomotives have disappeared from everyday rail traffic, fires still occur. A vegetation fire near Bzenec (Czech Republic) on 21 June 2018 affected, for example, 124,110 m2 of forest and grassland. The investigation revealed that the fire was caused by a spark from a passing train. In this study, we analyzed vegetation fires that occurred near Czech railway lines between 2011 and 2019 to investigate their temporal pattern and relation to weather conditions or to identify the most hazardous locations.

2021

Bíl, M., Raška, P., Dolák, L., Kubeček, J., 2021. CHILDA – Czech Historical Landslide Database, Nat. Hazards Earth Syst. Sci., 21, 2581–2596.

Show abstract |

https://doi.org/10.5194/nhess-21-2581-2021

National and regional historical landslide databases are increasingly viewed as providing empirical evidence for the geomorphic effects of ongoing environmental change and for supporting adaptive territorial planning. In this work, we present the design and current content of the Czech Historical Landslide Database (CHILDA), the first of its kind for the territory of Czechia (the Czech Republic). We outline the CHILDA system, its functionality, and technical solution. The database was established by merging and extending the fragmented regional datasets for highly landslide-prone areas in Czechia. Currently, the database includes 699 records (619 landslides, 75 rockfalls, and 5 other movement types) encompassing the period from the oldest determined records (1132) up to 1989, which represents an important cultural, political, and socioeconomic divide.

2019

Chow, C., Andrášik, R., Fischer, B., Keiler, M. (2019): Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events, Journal of Environmental Management 246, 85–100. https://doi.org/10.1016/j.jenvman.2019.05.084

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0301479719307042

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydro-meteorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community.

2018

Zahradníček, P., Münster, P., Bíl, M., Skalák, P., Štěpánek, P., Farda, A., Panský, M., Brzezina, J., Bílová, M., Kubeček, J., 2018. The December 2014 glaze event in the Czech Republic: predictability and impacts. Weather 73, 375–382. https://doi.org/10.1002/wea.3199

Show abstract |

https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/wea.3199

Vodák, R., Bíl, M., Křivánková, Z., 2018. A modified ant colony optimization algorithm to increase the speed of the road network recovery process. International Journal of Disaster Risk Reduction 31C, 1092–1106. https://doi.org/10.1016/j.ijdrr.2018.04.004

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https://www.sciencedirect.com/science/article/pii/S2212420917304120

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When a disaster strikes many roads are blocked and the affected network may break up into a number of isolated parts. The reconnection of the network is therefore necessary for both relief distribution and planning of construction work. Shortening the time during which the road network is separated into isolated parts helps decrease indirect losses from disasters. The obstacles usually faced during the process of reconstruction include both the large number of blocked links and extensive affected areas (road networks). A reduction of the network into a much smaller complete graph and metaheuristic based on an ant colony optimization has been introduced to overcome this issue. We demonstrate that, for small networks, the metaheuristic produces the same results as other deterministic algorithms. We further show that the method is still a viable approach for large networks (723 nodes and 974 links, where we artificially blocked 46 links) when the NP-hard nature of this problem began to affect the computational time of the deterministic algorithms. We demonstrate how the various scenarios can be included into the algorithm. We finally introduce a new ranking of feasible solutions which enables the algorithm to minimize the time of reconstructions for all repair units. Reasonable results were obtained after five minutes of computation. There is nevertheless an up-to-38% improvement of the initial solution. The algorithm can also be used for both relief distribution, when no roads were damaged, and for planning of construction work when damaged roads occur.

2017

Bíl, M., Andrášik. R., Nezval, V., Bílová, M., 2017. Identifying Locations along Railway Networks with the Highest Tree Fall Hazard. Applied Geography 87, 45–53. https://doi.org/10.1016/j.apgeog.2017.07.012

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0143622817301819

Disruptions of railway traffic have many reasons. Tree falls onto railway tracks or overhead lines rank among the most common causes of disruptions of a natural origin. 2039 tree-fall events, containing up to 70 individual trees per event, were registered on the Czech railway network between 2012 and 2015. 32% of them were directly caused by 14 weather extremes during which more than 20 concurrent tree-fall events were registered. Moreover, 12 train derailments due to fallen trees were registered on Czech railways within the same period. We combined land use data along railway tracks and data on tree falls. Land use and railway tracks data were obtained from a freely available Open Street Map database. The tree fall hazard was then computed using empirical data, data on land use and a generalized rule of succession. The clustering approach was also applied to focus on localities where tree falls were concentrated regardless of the resulting segment hazard. There were 59 rail track segments (out of 2960) with the highest tree fall hazard and 267 clusters were finally identified. The clusters and the most hazardous railway segments will be among the first in the process of line side vegetation monitoring in order to minimize potential losses from tree fall. The presented method can be widely applicable elsewhere.

2016

Bíl, M., Andrášik, R., Zahradníček, P., Kubeček, J., Sedoník, J., Štěpánek, P., 2016. Total water content thresholds for shallow landslides, Outer Western Carpathians. Landslides 13, 337–347. https://doi.org/10.1007/s10346-015-0570-9

Show abstract |

https://link.springer.com/article/10.1007/s10346-015-0570-9

Shallow landslides are fairly frequent natural processes which emerge as a result of both rainfall and rapid snowmelt in the Flysch Belt of the Outer Western Carpathians. We estimated the total water content thresholds for the previously defined seven phases of increased landsliding which took place between 1939 and 2010 around the Napajedla meteorological station. The time series were reconstructed on the basis of data from surrounding stations. Rainfalls with the highest intensities (>1 mm/min) were removed from the set. Rainfall of such an intensity primarily causes overland flow and soil erosion and does not contribute to landslide threshold. The snow water equivalent was computed on the basis of the snow height, and possible errors were evaluated as interval estimations. An interval of 10 days before a landslide phase was selected for the total water content threshold. The resulting lower boundary (67.0 mm/10 days) and upper boundary (163.3 mm/10 days) thresholds of water infiltrated into soil during an event shall be part of the prepared online warning system in this area.

2015

Bíl, M., Vodák, R., Kubeček, J., Bílová, M., Sedoník, J., 2015. Evaluating Road Network Damage Caused by Natural Disasters in the Czech Republic between 1997 and 2010. Transportation Research Part A: Policy and Practice 80, 90–103. https://doi.org/10.1016/j.tra.2015.07.006

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0965856415001883

Road networks play a vital role in maintaining a functioning modern society. Many events perceptibly affect the transport supply along these networks, especially natural disasters such as floods, landslides, and earthquakes. Contrary to more common disruptions of traffic from accidents, or maintenance closures, natural disasters are capable of destroying large numbers of roads and usually cover vast areas. When evaluating network damage no single measure alone is able to describe the full extent of network destruction. In this study, we investigated six highly damaging natural disasters, which occurred in the Czech Republic between 1997 and 2010. They were all induced by extreme rainfall or by rapid snowmelt and resulted in floods and landslides. Their impacts are evaluated with respect to the damage to road networks and decreased serviceability. For mutual comparison of the impacts and their analysis we used several criteria, described in the paper, related to economic impacts, physical harm to individuals and infrastructures, and the effects on connectivity and serviceability. We also introduced a new measure based on the network efficiency index which takes into account the importance of nodes based on their population. Moreover, we provide a detailed analysis of one such event in July 1997 that significantly affected the road network of the Zlín region.

2014

Bíl, M., Krejčí, O., Bílová, M., Kubeček, J., Sedoník, J., Krejčí V., 2014. A Chronology of Landsliding and its Impacts on the Village of Halenkovice, Outer Western Carpathians, Geography 119 (4), 342–363. https://doi.org/10.37040/geografie2014119040342

Show abstract |

https://gis.cdvinfo.cz/file/a-chronology-of-landsliding-and-its-impacts-on-the-village-of-halenkovice-outer-western-carpathians/

The article is based on an investigation of landsliding chronology coducted at the village of Halenkovice. (Outer Western Carpathians, Czechia). On the basis of historical data, (chronicles and other archive sources, air photos, old maps), field mapping and interviews of eyewitnesses, we determined six major and seven minor phases of landsliding in the village and its immediate neighborhood for the period 1915–2010. Inactive and active landslides currently cover 20% of the Halenkovice cadastral area. Landslides have strongly affected the development of the village itself. Since 1941, at least 9 houses have been destroyed by landslides, with many other local buildings and roads suffering significant damage. We also documented two examples of periodic reactivation of landslides.

Baroň, I, Bíl, M., Bábek, O., Smolková, V., Pánek, T., Macur, L., 2014. Effect of slope failures on river-network pattern: A river piracy case study from the Flysch Belt of the Outer Western Carpathians. Geomorphology 214, 356–365. https://doi.org/10.1016/j.geomorph.2014.02.017

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0169555X14001007

Landslides are important geomorphic agents in various mountainous settings. We document here a case of river piracy from the upper part of the Malá Brodská Valley in the Vsetínské Mts., Czech Republic (Rača Unit of the flysch Magura Group of Nappes, flysch belt of the Outer Western Carpathians) controlled by mass movement processes. Based on the field geological, geomorphological and geophysical data, we found out that the landslide accumulations pushed the more active river of out of two subparallel river channels with different erosion activity westwards and forced intensive lateral erosion towards the recently abandoned valley. Apart from the landslide processes, the presence of the N-striking fault, accentuated by higher flow rates of the eastern channel as a result of its larger catchment area, were the most critical factors of the river piracy. As a consequence of the river piracy, intensive retrograde erosion in the elbow of capture and also within the upper portion of the western catchment occurred. Deposits of two landslide dams document recent minimum erosion rates to be 18.8 mm.ky− 1 in the western (captured) catchment, and 3.6 mm.ky− 1 in the eastern catchment respectively. The maximum age of the river piracy is estimated to be of the late Glacial and/or the early Holocene.

Bíl, M., Kubeček, J., Andrášik, R., 2014. An Epidemiological Approach to Determining the Risk of Road Damage due to Landslides. Nat Haz. 73 (4), 1323–1335. https://doi.org/10.1007/s11069-014-1141-4

Show abstract |

https://link.springer.com/article/10.1007/s11069-014-1141-4

Disruption of segments of roads can have a significant impact on the vulnerability of the entire network. Natural disasters are frequent causes of disruptions of this kind. This article focuses on determining the risk of road disruptions due to landslides. Our approach is based on methodology widely used in the field of epidemiology. We had available data on the location of the landslides, the road network and a list of the disrupted road segments. With the use of a 2 × 2 table, we determined the relationship between landslide data and road segment disruptions and derived the risk coefficient based on the number of landslides in the vicinity of the road and its length. The result is a disruption risk map with risk coefficients ranging from 0 to 47.94. In order to distinguish the most risky segments, we calculated a threshold of 12.40 with the use of a risk breakdown in a group of segments without damage. Nineteen percentage (402 km) of the road network in the Zlín region (Czech Republic), where the methodology was applied, is located beyond this threshold. The benefits of this approach stem from its speed and potential to define the most risky areas on which a detailed geomorphologic analysis can be focused.

2012

Bíl, M., Kubeček, J., 2012. Piping in loess-like and loess-derived soils: case study of Halenkovice site, Czech Republic. Annales Societatis Geologorum Poloniae 82, 45–50. https://geojournals.pgi.gov.pl/asgp/article/view/12560

Show abstract |

https://geojournals.pgi.gov.pl/asgp/article/view/12560

The soil piping that occurs on luvisols in the vicinity of the village of Halenkovice was studied for 5 years. These piping phenomena can only be found where arable land meets the forest or a belt of shrubbery. If there is a scarp in the locality, which usually changes from 6° in the field to approximately 30° in the forest, soil pipes are more likely to occur. Before the scarp, the slope flattens out and it is almost horizontal. This factor makes it possible for the overland flow to seep into the slope. This seepage results in soil piping, which is formed in loess loam and colluvial deposits. There are about 15 sites in the vicinity of the village of Halenkovice, where soil piping occurs. In one of them, Halenkovice 1 (an area of 900 m2) we closely studied 47 partial cavities. Their internal volume is 3.8 m3. The volume of the sink holes is 23 m3. There are two types of soil pipes – vertical, which on average tend to be shorter (40 cm) and lead the water under the surface, and soil pipes parallel with the slope, which are on average 81 cm long. Water flows through the pipes during a thaw or precipitation, which often takes away the top soil. The intensity of this process depends on the intensity of precipitation, which occurs outside the growing season, when there are no crops in the fields.

2021

Bíl, M., Raška, P., Dolák, L., Kubeček, J., 2021. CHILDA – Czech Historical Landslide Database, Nat. Hazards Earth Syst. Sci., 21, 2581–2596.

Show abstract |

https://doi.org/10.5194/nhess-21-2581-2021

National and regional historical landslide databases are increasingly viewed as providing empirical evidence for the geomorphic effects of ongoing environmental change and for supporting adaptive territorial planning. In this work, we present the design and current content of the Czech Historical Landslide Database (CHILDA), the first of its kind for the territory of Czechia (the Czech Republic). We outline the CHILDA system, its functionality, and technical solution. The database was established by merging and extending the fragmented regional datasets for highly landslide-prone areas in Czechia. Currently, the database includes 699 records (619 landslides, 75 rockfalls, and 5 other movement types) encompassing the period from the oldest determined records (1132) up to 1989, which represents an important cultural, political, and socioeconomic divide.

2020

Bíl, M., Krejčí, O., Dolák, L., Krejčí, V., Martínek, J., Svoboda, J., 2020. A chronology of landsliding based on archaeological and documentary data: Pavlovské vrchy Hills, Western Carpathian Flysch Belt. Sci Rep 10, 976. https://doi.org/10.1038/s41598-020-57551-4

Show abstract |

https://www.nature.com/articles/s41598-020-57551-4

Pavlovské vrchy Hills represent a distinctive elevation near the Czech-Austrian border where the active, dormant and relict landslides cover 12% of the area. Here we focused on the chronology of landsliding in this area using geological, archaeological and historical evidence. The earliest records of landsliding were determined in locations underlying the dated archaeological settlements. The Upper Paleolithic settlement complex dated between 37–24 ka cal BP, was originally deposited over these landslides. It was consequently destroyed in certain places by additional landslides preceding the last (Upper Pleniglacial) loess deposition (22 ka cal BP). These landslides took place before and after the Upper Paleolithic occupation of this area. This Pleistocene landslide event ranks among the oldest (albeit indirectly) dated landslide within the Czech part of the Western Carpathian Flysch Belt. The chronology of later, historical, landsliding was determined using written records (chronicles, official reports, archival evidence, etc.). Continuous records of landsliding were available as of the middle of the seventeenth century. The major concentration of landslides occurred at the beginning of the twentieth century (1910–1915). The 1663 landslide is currently the oldest landslide, in the Czech part of the Western Carpathian Flysch Belt, which was dated on the basis of documentary data.

2019

Chow, C., Andrášik, R., Fischer, B., Keiler, M. (2019): Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events, Journal of Environmental Management 246, 85–100. https://doi.org/10.1016/j.jenvman.2019.05.084

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0301479719307042

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydro-meteorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community.

2016

Bíl, M., Andrášik, R., Zahradníček, P., Kubeček, J., Sedoník, J., Štěpánek, P., 2016. Total water content thresholds for shallow landslides, Outer Western Carpathians. Landslides 13, 337–347. https://doi.org/10.1007/s10346-015-0570-9

Show abstract |

https://link.springer.com/article/10.1007/s10346-015-0570-9

Shallow landslides are fairly frequent natural processes which emerge as a result of both rainfall and rapid snowmelt in the Flysch Belt of the Outer Western Carpathians. We estimated the total water content thresholds for the previously defined seven phases of increased landsliding which took place between 1939 and 2010 around the Napajedla meteorological station. The time series were reconstructed on the basis of data from surrounding stations. Rainfalls with the highest intensities (>1 mm/min) were removed from the set. Rainfall of such an intensity primarily causes overland flow and soil erosion and does not contribute to landslide threshold. The snow water equivalent was computed on the basis of the snow height, and possible errors were evaluated as interval estimations. An interval of 10 days before a landslide phase was selected for the total water content threshold. The resulting lower boundary (67.0 mm/10 days) and upper boundary (163.3 mm/10 days) thresholds of water infiltrated into soil during an event shall be part of the prepared online warning system in this area.

2015

Bíl, M., Vodák, R., Kubeček, J., Bílová, M., Sedoník, J., 2015. Evaluating Road Network Damage Caused by Natural Disasters in the Czech Republic between 1997 and 2010. Transportation Research Part A: Policy and Practice 80, 90–103. https://doi.org/10.1016/j.tra.2015.07.006

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0965856415001883

Road networks play a vital role in maintaining a functioning modern society. Many events perceptibly affect the transport supply along these networks, especially natural disasters such as floods, landslides, and earthquakes. Contrary to more common disruptions of traffic from accidents, or maintenance closures, natural disasters are capable of destroying large numbers of roads and usually cover vast areas. When evaluating network damage no single measure alone is able to describe the full extent of network destruction. In this study, we investigated six highly damaging natural disasters, which occurred in the Czech Republic between 1997 and 2010. They were all induced by extreme rainfall or by rapid snowmelt and resulted in floods and landslides. Their impacts are evaluated with respect to the damage to road networks and decreased serviceability. For mutual comparison of the impacts and their analysis we used several criteria, described in the paper, related to economic impacts, physical harm to individuals and infrastructures, and the effects on connectivity and serviceability. We also introduced a new measure based on the network efficiency index which takes into account the importance of nodes based on their population. Moreover, we provide a detailed analysis of one such event in July 1997 that significantly affected the road network of the Zlín region.

2014

Bíl, M., Krejčí, O., Bílová, M., Kubeček, J., Sedoník, J., Krejčí V., 2014. A Chronology of Landsliding and its Impacts on the Village of Halenkovice, Outer Western Carpathians, Geography 119 (4), 342–363. https://doi.org/10.37040/geografie2014119040342

Show abstract |

https://gis.cdvinfo.cz/file/a-chronology-of-landsliding-and-its-impacts-on-the-village-of-halenkovice-outer-western-carpathians/

The article is based on an investigation of landsliding chronology coducted at the village of Halenkovice. (Outer Western Carpathians, Czechia). On the basis of historical data, (chronicles and other archive sources, air photos, old maps), field mapping and interviews of eyewitnesses, we determined six major and seven minor phases of landsliding in the village and its immediate neighborhood for the period 1915–2010. Inactive and active landslides currently cover 20% of the Halenkovice cadastral area. Landslides have strongly affected the development of the village itself. Since 1941, at least 9 houses have been destroyed by landslides, with many other local buildings and roads suffering significant damage. We also documented two examples of periodic reactivation of landslides.

Baroň, I, Bíl, M., Bábek, O., Smolková, V., Pánek, T., Macur, L., 2014. Effect of slope failures on river-network pattern: A river piracy case study from the Flysch Belt of the Outer Western Carpathians. Geomorphology 214, 356–365. https://doi.org/10.1016/j.geomorph.2014.02.017

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0169555X14001007

Landslides are important geomorphic agents in various mountainous settings. We document here a case of river piracy from the upper part of the Malá Brodská Valley in the Vsetínské Mts., Czech Republic (Rača Unit of the flysch Magura Group of Nappes, flysch belt of the Outer Western Carpathians) controlled by mass movement processes. Based on the field geological, geomorphological and geophysical data, we found out that the landslide accumulations pushed the more active river of out of two subparallel river channels with different erosion activity westwards and forced intensive lateral erosion towards the recently abandoned valley. Apart from the landslide processes, the presence of the N-striking fault, accentuated by higher flow rates of the eastern channel as a result of its larger catchment area, were the most critical factors of the river piracy. As a consequence of the river piracy, intensive retrograde erosion in the elbow of capture and also within the upper portion of the western catchment occurred. Deposits of two landslide dams document recent minimum erosion rates to be 18.8 mm.ky− 1 in the western (captured) catchment, and 3.6 mm.ky− 1 in the eastern catchment respectively. The maximum age of the river piracy is estimated to be of the late Glacial and/or the early Holocene.

Bíl, M., Kubeček, J., Andrášik, R., 2014. An Epidemiological Approach to Determining the Risk of Road Damage due to Landslides. Nat Haz. 73 (4), 1323–1335. https://doi.org/10.1007/s11069-014-1141-4

Show abstract |

https://link.springer.com/article/10.1007/s11069-014-1141-4

Disruption of segments of roads can have a significant impact on the vulnerability of the entire network. Natural disasters are frequent causes of disruptions of this kind. This article focuses on determining the risk of road disruptions due to landslides. Our approach is based on methodology widely used in the field of epidemiology. We had available data on the location of the landslides, the road network and a list of the disrupted road segments. With the use of a 2 × 2 table, we determined the relationship between landslide data and road segment disruptions and derived the risk coefficient based on the number of landslides in the vicinity of the road and its length. The result is a disruption risk map with risk coefficients ranging from 0 to 47.94. In order to distinguish the most risky segments, we calculated a threshold of 12.40 with the use of a risk breakdown in a group of segments without damage. Nineteen percentage (402 km) of the road network in the Zlín region (Czech Republic), where the methodology was applied, is located beyond this threshold. The benefits of this approach stem from its speed and potential to define the most risky areas on which a detailed geomorphologic analysis can be focused.

2012

Bíl, M., Kubeček, J., 2012. Piping in loess-like and loess-derived soils: case study of Halenkovice site, Czech Republic. Annales Societatis Geologorum Poloniae 82, 45–50. https://geojournals.pgi.gov.pl/asgp/article/view/12560

Show abstract |

https://geojournals.pgi.gov.pl/asgp/article/view/12560

The soil piping that occurs on luvisols in the vicinity of the village of Halenkovice was studied for 5 years. These piping phenomena can only be found where arable land meets the forest or a belt of shrubbery. If there is a scarp in the locality, which usually changes from 6° in the field to approximately 30° in the forest, soil pipes are more likely to occur. Before the scarp, the slope flattens out and it is almost horizontal. This factor makes it possible for the overland flow to seep into the slope. This seepage results in soil piping, which is formed in loess loam and colluvial deposits. There are about 15 sites in the vicinity of the village of Halenkovice, where soil piping occurs. In one of them, Halenkovice 1 (an area of 900 m2) we closely studied 47 partial cavities. Their internal volume is 3.8 m3. The volume of the sink holes is 23 m3. There are two types of soil pipes – vertical, which on average tend to be shorter (40 cm) and lead the water under the surface, and soil pipes parallel with the slope, which are on average 81 cm long. Water flows through the pipes during a thaw or precipitation, which often takes away the top soil. The intensity of this process depends on the intensity of precipitation, which occurs outside the growing season, when there are no crops in the fields.

2021

Bíl, M., Andrášik, R., Cícha, V., Arnon, A., Kruuse, M., Langbein, J., Náhlik, A., Niemi, M., Pokorny, B., Colino-Rabanal, V.J., Rolandsen, C. M., Seiler, A, 2021. COVID-19 related travel restrictions prevented numerous wildlife deaths on roads: A comparative analysis of results from 11 countries. Biological Conservation, 109076.

Show abstract |

https://doi.org/10.1016/j.biocon.2021.109076

Millions of wild animals are killed annually on roads worldwide. During spring 2020, the volume of road traffic was reduced globally as a consequence of the COVID-19 pandemic. We gathered data on wildlife-vehicle collisions (WVC) from Czechia, Estonia, Finland, Hungary, Israel, Norway, Slovenia, Spain, Sweden, and for Scotland and England within the United Kingdom. In all studied countries WVC statistics tend to be dominated by large mammals (various deer species and wild boar), while information on smaller mammals as well as birds are less well recorded. The expected number of WVC for 2020 was predicted on the basis of 2015–2019 WVC time series representing expected WVC numbers under normal traffic conditions. Then, the forecasted and reported WVC data were compared. The results indicate varying levels of WVC decrease between countries during the COVID-19 related traffic flow reduction (CRTR). While no significant change was determined in Sweden, where the state-wide response to COVID-19 was the least intensive, a decrease as marked as 37.4% was identified in Estonia. The greatest WVC decrease, more than 40%, was determined during the first weeks of CRTR for Estonia, Spain, Israel, and Czechia. Measures taken during spring 2020 allowed the survival of large numbers of wild animals which would have been killed under normal traffic conditions. The significant effects of even just a few weeks of reduced traffic, help to highlight the negative impacts of roads on wildlife mortality and the need to boost global efforts of wildlife conservation, including systematic gathering of roadkill data.

2020

Bíl, M., Kušta, T., Andrášik, R., Cícha, V., Brodská, H., Ježek, M., Keken, Z., 2020. No clear effect of odour repellents on roe deer behaviour in the vicinity of roads. Wildlife Biology 2020 (4).

Show abstract |

https://doi.org/10.2981/wlb.00744

Ungulate–vehicle collisions pose a traffic safety issue as well as wildlife-conservation issues in many countries. While fences are recommended as reliable safety measures for motorways and other high-traffic volume roads, no generally accepted measures of the same efficiency are available for secondary roads. Odour repellents are applied in many central European countries, but contradictory results are available concerning their efficiency. We tested the effect of odour repellents on both a crossing frequency and the presence near roads of six individuals of roe deer over a period of five months (April–August 2019). The odour repellents were installed along two secondary roads, and along two semi-open habitats (forest–meadow and forest–arable land) alternately, in several phases. Two hypotheses were tested. The first one focused on the change in animal presence close to the profiles where the odours were applied, while the second hypothesis concerned a change in the number of crossings of the same profiles. The results demonstrate that no clear effect of odour repellents on roe deer behaviour in both hypotheses were obtained. Apart from the obtained results, we discuss the importance of the methodology. We conclude that this kind of study design is extremely sensitive to a number of factors with a potentially negative influence on the course of the study design.

Bíl, M., Andrášik, R., 2020. The effect of wildlife carcass underreporting on KDE+ hotspots identification and importance. Journal of Environmental Management 275, 111254.

Show abstract |

https://doi.org/10.1016/j.jenvman.2020.111254

Many approaches have been developed in order to mitigate wildlife-vehicle collisions (WVC), their causes and consequences. Reliable data on the amount and location of killed animals along roads are therefore necessary. The existing WVC databases are usually, however, far from complete. This data underreporting causes problems when identifying the riskiest places along a transportation infrastructure. WVC data underreporting can distort the results of WVC hotspots determination.
In this work, we simulated WVC hotspots identification and stability under various rates of WVC data underreporting. Our aim was to investigate whether WVC hotspots can be found at the original locations even when data are strongly underreported. We applied the KDE + method for WVC hotspots identification. The KDE + method also allows for hotspots ranking according to cluster strength and collective risk. These two measures were then used for detection of diminishing hotspot signals with a rising level of underreporting. We further studied the effects of both spatial and temporal underreporting. Whereas temporal change of underreporting was not a problem in hotspots detection, spatial underreporting introduced significant errors producing both false positive and false negative results (hotspots). We conclude that both researchers and practitioners should be aware of the phenomenon of underreporting and should also try to maintain the same sampling effort of spatial reporting.

Nezval, V., Bíl, M., 2020. Spatial analysis of wildlife-train collisions on the Czech rail network. Applied Geography 125, 102304. https://doi.org/10.1016/j.apgeog.2020.102304

Show abstract |

https://doi.org/10.1016/j.apgeog.2020.102304

Wildlife-train collisions (WTCs), particularly those with large mammals, pose a threat to the safety and continuity of rail operations. In this study, we collected 1,909 WTCs which took place in the Czech Republic between 2011 and 2019, in order to identify the most hazardous WTC locations and their spatial pattern. 208 WTC hotspots were identified using the KDE+ method. They contained 782 collisions (41.2%) and accounted for 0.7% of the Czech rail network length. We also identified and ranked the most important WTC hotspots using a collective risk parameter. WTCs occurred more frequently in the vicinity of a forest or stream and were more distant from arable land or urban/industrial areas than from other places on the entire Czech rail network. The results can help in placing crash-safety measures as a large proportion of accidents only occurred on less than 1% of the rail network.

Bíl, M., Kubeček, J., Andrášik, R., 2020. Ungulate-vehicle collision risk and traffic volume on roads. European Journal of Wildlife Research 66, 59.

Show abstract |

https://doi.org/10.1007/s10344-020-01397-8

We analyzed data from traffic counters installed on 143 Czech roads (motorways and primary roads) which precisely indicated current traffic volume during occurrences of ungulate-vehicle collisions (UVC). 1,995 UVC were identified over the period 2009 – 2015 on these 143 road segments. The overall range of annual average daily traffic (AADT) values, for the respective roads, was between 1,547 and 78,320 AADT (vehicles/day). Almost 80 % of UVC took place at volume lower than 1,000 vehicles/hour. We demonstrate that traffic volume has a different distribution during the day when compared with UVC distribution. The highest relative risk of UVC was identified for traffic up to 750 vehicles/h. The risk of UVC varied over the course of the year as it was influenced by ungulate locomotory activity. We concluded that the AADT, representing average annual daily traffic, does not accurately represent the actual traffic volume which are present during the night hours, where the majority of UVC usually occur. Therefore, there is a danger that UVC risk modelling, relying on AADT, will be distorted.

Grilo, C., Koroleva, E., Andrášik, R., Bíl, M., Gonzalez-Suarez, M., 2020. Roadkill risk and vulnerability in European birds and mammals. Front Ecol Environ, Vol 18; issue 6.

Show abstract |

https://doi.org/10.1002/fee.2216

Roads represent a threat to biodiversity, primarily through increased mortality from collisions with vehicles. Although estimating roadkill rates is an important first step, how roads affect long-term population persistence must also be assessed. We developed a trait-based model to predict roadkill rates for terrestrial bird and mammalian species in Europe and used a generalized population model to estimate their long-term vulnerability to road mortality. We found that ~194 million birds and ~29 million mammals may be killed each year on European roads. The species that were predicted to experience the highest mortality rates due to roads were not necessarily the same as those whose long-term persistence was most vulnerable to road mortality. When evaluating which species or areas could be most affected by road development projects, failure to consider how roadkill affects populations may result in misidentifying appropriate targets for mitigation.

Bíl, M., Heigl, F., Janoška, Z., Vercayie, D., Perkins, S. E., 2020. Benefits and challenges of collaborating with volunteers: examples from National Wildlife Roadkill Reporting Systems in Europe. Journal for Nature Conservation 54C, 125798. https://doi.org/10.1016/j.jnc.2020.125798

Show abstract |

https://www.sciencedirect.com/science/article/pii/S1617138119303449?via%3Dihub

Daily, a large number of animals are killed on European roads due to collisions with vehicles. A high proportion of these events, however, are not documented, as those obliged to collect such data, only record a small proportion; the police only register collisions that lead to traffic accidents, and hunters only collect data on game wildlife. Such reports disproportionately under-records small vertebrates such as birds, small mammals, amphibians and reptiles. In the last decade, however, national wildlife roadkill reporting systems have been launched, largely working with citizen scientists to collect roadkill data on a national basis that could fill this data gap. The aim of this study is, therefore, to describe for the first time, existing projects in Europe, and the user groups that submit data to them. To give a deeper understanding of such projects, we describe exemplar scientific roadkill reporting systems that currently exist in Austria, Belgium, Czechia and the United Kingdom. We define groups of people who contribute to such citizen science activities, and report our experience and best practice with these volunteers. We conclude that volunteers contribute significantly to collecting data on species that are not typically recorded in official databases. To ensure citizen-science projects perpetuate, (I) volunteers need to be motivated by the organisers to participate on a long-term basis, (II) volunteers need support in identifying roadkill species where required, and (III) regular feedback is required on how their contribution is used to produce new scientific knowledge.

2019

Keken, Z., Sedoník, J., Kušta, T., Andrášik, R., Bíl, M., 2019. Roadside vegetation influences clustering of ungulate vehicle collisions. Transportation Research Part D: Transport and Environment 73, 381–390. https://doi.org/10.1016/j.trd.2019.07.013

Show abstract |

https://www.sciencedirect.com/science/article/pii/S1361920918311659

The aim of this study was to identify landscape-related factors which could explain the concentration of traffic crashes with large ungulates in a forest environment. We worked with ungulate-vehicle collisions which took place on the Czech road network in the period 2014–2016 using the application Srazenazver.cz. With the KDE+ method, we chose the most significant hotspots with linkage to forest. For comparison we randomly selected control localities outside the KDE+ hotspots (i.e., with very low level of ungulate-vehicle collisions) but still in the forest area. A set of photos were taken at each hotspot and control locality (2 orthophotos and 4 driver views). Each set of images was then visually analysed by two independent evaluators. They did not know which set of images were for hotspots and which set of images were for control localities. From the point of view of the overall score, it cannot be said that these two groups of locations differ (p-value 0.3575, Kolmogorov-Smirnov test). The only attribute that demonstrated a difference between hotspots and randomly selected locations was “attractiveness (quality food source and cover) of the immediate vicinity of the transport infrastructure for the ungulates” (p-value 0.0016, Kolmogorov-Smirnov test). Results confirm the importance of landscape management in the surroundings of transport infrastructure, especially in its immediate vicinity. However, they did not confirm the possibility that the most dangerous locations from the UVC point of view could be identified on the basis of landscape composition and the overall state of vegetation around transport infrastructure.

Bíl, M., Andrášik, R., Duľa, M., Sedoník, J., 2019. On reliable identification of factors influencing wildlife-vehicle collisions along roads. Journal of Environmental Management 237C, 297–304. https://doi.org/10.1016/j.jenvman.2019.02.076

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0301479719302270

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

2018

Favilli, F., Bíl, M., Sedoník, J., Andrášik, R., Kasal, P., Agreiter, A., Streifeneder, T., 2018. Application of KDE+ software to identify collective risk hotspots of ungulate-vehicle collisions in South Tyrol, Northern Italy. European Journal of Wildlife Research 64:59. https://doi.org/10.1007/s10344-018-1214-x

Show abstract |

https://link.springer.com/article/10.1007/s10344-018-1214-x

This paper is the first dealing with animal-vehicle collisions (AVC) with red and roe deer in South Tyrol, Northern Italy. The Autonomous Province of Bolzano (South Tyrol) has been collecting AVC data since 2012 on the entire provincial road network. Each year, AVC data accounted for more than 700 cases per year, with several socioeconomic and ecological implications. The aim of this research is to identify the locations where AVC occur more frequently than expected (hotspots) and better outline subsequent implementation of mitigation measures. For an effective identification of AVC hotspots, we applied a combined methodology of temporal and spatial analysis on AVC data collected on the South Tyrol road network in the years 2012–2014. AVC data enabled the identification of the temporal patterns, which showed different behaviors of the two target species in close proximity of the road network and throughout the 12 months. The KDE+ software applied to the 2012–2014 AVC database allowed for spatial analysis and the identification of hotspots, i.e., the road sections having the highest risk for drivers. The integration of the results, coming from the abovementioned methodologies, contributes to a detailed assessment of roads that would allow the identification of the local contributing factors and a base-line of potential problematic areas that will highlight the need for further investigation to assess whether the risk-rank is accurate and allocate effectively limited resources to a feasible number of identified hotspots and reduce the current degree of AVC in the South Tyrolean road network.

Bartonička, T., Andrášik, R., Duľa, M., Sedoník, J., Bíl, M., 2018. Identification of Local Factors Causing Clustering of Animal-Vehicle Collisions. Journal of Wildlife Management 82, 940–947. https://doi.org/10.1002/jwmg.21467

Show abstract |

https://onlinelibrary.wiley.com/doi/epdf/10.1002/jwmg.21467

Effective measures reducing risk of animal-vehicle collisions (AVC) require defining high-risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individua l AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non-random (clustered) occurrenc e of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE þ methods) identified factors causing local clustering of AVCs. Distance from forest ( < 350 m) or linear vegetation were important factors for estimating presence of clusters of AVCs; in open areas, AVC clusters were absent. Further research on effectiveness of measures reducing risk of AVC should focus on clusters of AVCs, not on the individua l AVC. We recommend that state transportati on agencies focus mitigation actions in forested areas.

Bíl, M., Andrášik, R., Bartonička, T., Křivánková, Z., Sedoník, J., 2018. An Evaluation of Odor Repellent Effectiveness in Prevention of Wildlife-Vehicle Collisions. Journal of Environmental Management 205C, 209–214. https://doi.org/10.1016/j.jenvman.2017.09.081

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0301479717309568

Wildlife-vehicle collisions (WVC) amount to 11 % of all registered traffic crashes in the Czech Republic causing, apart from numerous deaths and serious injuries to animals, property damage and injuries to car passengers. Odor repellents have the potential to lower the overall number of WVC and allow animals to cross roads at the same time. We tested the effectiveness of odor repellent preparation in prevention of WVC. 18 places were selected on the Czech road network where WVC were concentrated on the basis of traffic crash data. Control sections on the same road segments were also delimited in order to keep the traffic intensities constant. We applied a Before-After-Control-Impact (BACI) study design to control not only the effect of the measures but also the expected natural variations in wildlife populations over time. Data were compared before and after odor repellent installations. Wildlife carcass gathering was carried out during the spring and autumn. We also used the police crash database to supplement carcass data when no field works were carried out. 201 killed mammals (roe deer and wild boars) were identified in total over 47 months. We applied a Bayesian approach as only a limited numbers of WVC were available. A WVC decrease between 26 – 43 % can be expected on the treated road sections. These numbers are, however, up to three-times lower than those claimed by producers of odor preparations.

2017

Bíl, M., Kubeček, J., Sedoník, J., Andrášik. R., 2017. Srazenazver.cz: A system for evidence of animal-vehicle collisions along transportation networks. Biological Conservation 213PA, 167–174. https://doi.org/10.1016/j.biocon.2017.07.012

Show abstract |

http://www.sciencedirect.com/science/article/pii/S000632071730263X

We present a system for state-wide evidence of animal-vehicle collisions (AVC). The primary part of this system is a geographic database which is connected to a web-map application. AVC data come from the Police via an online system of traffic incidents (JSDI) and from volunteers through a web or mobile interface. Data are processed using automatic scripts which identify data errors and perform spatial analyses. The application automatically computes AVC hotspots every midnight and crash densities along road sections. Hunter area administrators consequently have an overview of their areas. More than 40,000 records are currently included in this database. 50% of them were added over the last two years when it was launched. The majority of data (90%) came from JSDI. The species is known for 44% of JSDI records. The majority of the identified species were roe deer (75%), followed by wild boar (15%). Roe deer crashes occur most frequently in May within 2 h after sunset. 32.5% (56.4%) of these crashes occur within 1 h (2 h) before or after sunset or sunrise. For wild boar, the values are less distinctive (19.4% and 37.7%). Approximately 1800 AVC hotspots, which cover 0.5% of the Czech road network, were detected and visualized on a map.

2016

Bíl, M., Andrášik, R., Svoboda, T., Sedoník, J., 2016. The KDE+ software: a tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology 31, 231–237. https://doi.org/10.1007/s10980-015-0265-6

Show abstract |

https://link.springer.com/article/10.1007/s10980-015-0265-6

Context: Objective identification of locations on transportation networks, where animal-vehicle collisions (AVC) occur more frequently than expected (hotspots), is an important step for the effective application of mitigation measures. Objectives: We introduce the KDE+ software which is a programmed version of the KDE+ method for effective identification of traffic accident hotspots. The software can be used in order to analyze animal-vehicle collision data. Methods: The KDE+ method is based on principles of Kernel Density Estimation (KDE). The symbol ‘+’ indicates that the method allows for the objective selection of significant clusters and for the ranking of the hotspots. It is also simultaneously applicable to an unlimited number of road segments. Results: We applied the KDE+ method to the entire Czech road network. The hotspots were ranked according to their significance. The resulting hotspots represent a short overall road length which should require a more detailed assessment in the field. The 100 most important clusters of AVC represent, for example, only 19.7 km of the entire road network (37,469 km). Conclusions: We present an objective method for hotspots identification which can be used for AVC data. This method is unique because it determines the significance level of hotspots in an objective way. The prioritization of hotspots allows a transportation manager to effectively allocate resources to a feasible number of identified hotspots. We describe the software, data preparation and present the KDE+ application to AVC data.

2023

Bíl, M., Andrášik, R., Kušta, T. Bartonička, T. (2023) Ungulate-vehicle crashes peak a month earlier than 38 years ago due to global warming. Climatic Change 176, 84.

Show abstract |

https://doi.org/10.1007/s10584-023-03558-5

Cunneyworth, P. M. K., Andrášik, R., & Bíl, M. (2023). An effect of canopy bridges on monkey vehicle collision hotspots: Spatial and spatiotemporal analyses. American Journal of Primatology, e23492.

Show abstract |

https://doi.org/10.1002/ajp.23492

Almost one‐quarter of primate species are reported to be involved in vehicle collisions. To mitigate these collisions, canopy bridges are used though their effectiveness is not broadly substantiated. We studied bridge impact on 23 years of vehicle collisions (2000–2022: N = 765) with colobus (Colobus angolensis palliatus), Sykes' (Cercopithecus mitis albogularis), and vervet (Chlorocebus pygerythrus hilgerti) monkeys in Diani, Kenya. Along a 9 km road, collisions did not decrease over the study duration, although bridges increased from 8 to 30. Using the kernel density estimation plus (KDE+) method, collisions appeared highly concentrated at some locations. These concentrations, called hotspots, represent hazardous road segments, though the hotspots for all three species overlapped for only 3% of the road length. We then inspected the collision hotspots over time, using the spatiotemporal extension of the KDE+ method. We compared hotspot presence in the 3 years before and after bridge installation to determine if bridges mitigated these hotspots. Hotspots disappeared for ~60% of bridges postinstallation, suggesting that bridges effectively reduce some collisions. However, of the bridges installed in locations that were not hotspots, 13% had hotspots emerge. Surprisingly, regardless of preinstallation hotspot occurrence, almost one‐fifth of bridges had postinstallation hotspots. To understand the extent to which bridges mitigate collisions, other factors need consideration, including species attributes and crossing behavior, and road features and vehicle volume. We used the novel analytical method because it best suited our data set, given the challenges of determining the bridge impact zone and the low collision frequency.

2022

Nezval, V., Andrášik, R., Bíl, M. 2022. Vegetation fires along the Czech rail network. Fire Ecology, 18, 15.

Show abstract |

https://fireecology.springeropen.com/articles/10.1186/s42408-022-00141-8

In the past, fires around railways were often associated with steam locomotives. Although steam locomotives have disappeared from everyday rail traffic, fires still occur. A vegetation fire near Bzenec (Czech Republic) on 21 June 2018 affected, for example, 124,110 m2 of forest and grassland. The investigation revealed that the fire was caused by a spark from a passing train. In this study, we analyzed vegetation fires that occurred near Czech railway lines between 2011 and 2019 to investigate their temporal pattern and relation to weather conditions or to identify the most hazardous locations.

2019

Bíl, M., Andrášik, R., Sedoník, J., 2019. A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography 107, 82–90. https://doi.org/10.1016/j.apgeog.2019.04.008

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0143622818309081

A number of traffic crash databases at present contain the precise positions and dates of these events. This feature allows for detailed spatiotemporal analysis of traffic crash patterns.We applied a clustering method for identification of traffic crash hotspots to the rural parts of primary roads in the Czech road network (3,933 km) where 55,296 traffic crashes occurred over 2010 – 2018. The data were analyzed using a 3-year time window which moved forward with a one-day step as an elementary temporal resolution. The spatiotemporal behavior of hotspots could therefore be analyzed in great detail.All the identified hotspots, during the monitored nine-year period, covered between 6.8% and 8.2% of the entire road network length in question. The percentage of traffic crashes within the hotspots remained stable over time at approximately 50%. Three elementary types of hotspots were identified when analyzing spatiotemporal crash patterns: hotspot emergence, stability and disappearance. Only 100 hotspots were stable (remained in approximately the same position) over the entire nine-year period. This approach can be applied to any traffic-crash time series when the precise positions and date of crashes are available.

Bíl, M., Andrášik, R., Duľa, M., Sedoník, J., 2019. On reliable identification of factors influencing wildlife-vehicle collisions along roads. Journal of Environmental Management 237C, 297–304. https://doi.org/10.1016/j.jenvman.2019.02.076

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0301479719302270

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

Bíl, M., Andrášik, R., Sedoník, J., 2019. Which curves are dangerous? A network-wide analysis of traffic crash and infrastructure data. Transportation Research Part A 120C, 252–260. https://doi.org/10.1016/j.tra.2019.01.001

Show abstract |

https://www.sciencedirect.com/science/article/pii/S096585641830819X

We conducted spatial analyses of traffic crashes, which took place in Czechia over 2010–2016, with respect to the road geometry data. The aim of the work was to identify hazardous road sub-segments where higher than expected numbers of traffic crashes occur.The entire Czech road network (58,200 km) was segmented at intersections into 39,074 between-intersection segments of varying lengths. Each road segment was further automatically sectioned, according to its horizontal alignment, into geometry-homogenous units (horizontal curves and tangents). Overall, 257,101 curves, defined as curved sections with radii below 2100 m, and 136,388 tangents, were identified. Subsequently, traffic crashes were joined to the respective geometrical units to determine their hazardousness. The degree of hazardousness was determined relatively, on a segment-by-segment basis, in order to eliminate the lack of precise traffic exposure data. In addition, the exact binomial test and Bayesian inference were used to identify the most hazardous horizontal curves.
It was found that, in general, the curves with a higher crash risk have lower radii than the other curves. We identified the geographical locations of all curves with a high crash hazard. We also ranked the curves according to the crash hazard. Approximately ten percent of road segments contained at least one hazardous horizontal curve. 6943 significantly hazardous curves were identified by the use of the exact binomial test. The Bayesian inference reduced this number to 1395 (0.31% of the entire road network) and ranked them according to the Bayes factor. The most hazardous curve was 45 m long and contained 8.7 traffic crashes per year. Its hazard rate accounted for 37.4. This state-wide analysis of primary data was conducted over an extremely short time (up to 3 days) as the result of an application of an efficient algorithm for automatic road curvature determination.

2018

Bíl, M., Andrášik, R., Sedoník, J., Cícha, V., 2018. ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation. PLoS ONE 13(12): e0208407. https://doi.org/10.1371/journal.pone.0208407

Show abstract |

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208407&type=printable

| Download

We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website.

Bartonička, T., Andrášik, R., Duľa, M., Sedoník, J., Bíl, M., 2018. Identification of Local Factors Causing Clustering of Animal-Vehicle Collisions. Journal of Wildlife Management 82, 940–947. https://doi.org/10.1002/jwmg.21467

Show abstract |

https://onlinelibrary.wiley.com/doi/epdf/10.1002/jwmg.21467

Effective measures reducing risk of animal-vehicle collisions (AVC) require defining high-risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individua l AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non-random (clustered) occurrenc e of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE þ methods) identified factors causing local clustering of AVCs. Distance from forest ( < 350 m) or linear vegetation were important factors for estimating presence of clusters of AVCs; in open areas, AVC clusters were absent. Further research on effectiveness of measures reducing risk of AVC should focus on clusters of AVCs, not on the individua l AVC. We recommend that state transportati on agencies focus mitigation actions in forested areas.

2017

Bíl, M., Andrášik. R., Nezval, V., Bílová, M., 2017. Identifying Locations along Railway Networks with the Highest Tree Fall Hazard. Applied Geography 87, 45–53. https://doi.org/10.1016/j.apgeog.2017.07.012

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0143622817301819

Disruptions of railway traffic have many reasons. Tree falls onto railway tracks or overhead lines rank among the most common causes of disruptions of a natural origin. 2039 tree-fall events, containing up to 70 individual trees per event, were registered on the Czech railway network between 2012 and 2015. 32% of them were directly caused by 14 weather extremes during which more than 20 concurrent tree-fall events were registered. Moreover, 12 train derailments due to fallen trees were registered on Czech railways within the same period. We combined land use data along railway tracks and data on tree falls. Land use and railway tracks data were obtained from a freely available Open Street Map database. The tree fall hazard was then computed using empirical data, data on land use and a generalized rule of succession. The clustering approach was also applied to focus on localities where tree falls were concentrated regardless of the resulting segment hazard. There were 59 rail track segments (out of 2960) with the highest tree fall hazard and 267 clusters were finally identified. The clusters and the most hazardous railway segments will be among the first in the process of line side vegetation monitoring in order to minimize potential losses from tree fall. The presented method can be widely applicable elsewhere.

Bíl, M., Kubeček, J., Sedoník, J., Andrášik. R., 2017. Srazenazver.cz: A system for evidence of animal-vehicle collisions along transportation networks. Biological Conservation 213PA, 167–174. https://doi.org/10.1016/j.biocon.2017.07.012

Show abstract |

http://www.sciencedirect.com/science/article/pii/S000632071730263X

We present a system for state-wide evidence of animal-vehicle collisions (AVC). The primary part of this system is a geographic database which is connected to a web-map application. AVC data come from the Police via an online system of traffic incidents (JSDI) and from volunteers through a web or mobile interface. Data are processed using automatic scripts which identify data errors and perform spatial analyses. The application automatically computes AVC hotspots every midnight and crash densities along road sections. Hunter area administrators consequently have an overview of their areas. More than 40,000 records are currently included in this database. 50% of them were added over the last two years when it was launched. The majority of data (90%) came from JSDI. The species is known for 44% of JSDI records. The majority of the identified species were roe deer (75%), followed by wild boar (15%). Roe deer crashes occur most frequently in May within 2 h after sunset. 32.5% (56.4%) of these crashes occur within 1 h (2 h) before or after sunset or sunrise. For wild boar, the values are less distinctive (19.4% and 37.7%). Approximately 1800 AVC hotspots, which cover 0.5% of the Czech road network, were detected and visualized on a map.

2016

Andrášik, R.; Bíl, M., 2016. Efficient Road Geometry Identification from Digital Vector Data. Journal of Geographical Systems 18(3), 249–264. https://doi.org/10.1007/s10109-016-0230-1

Show abstract |

https://link.springer.com/article/10.1007/s10109-016-0230-1

A new method for the automatic identification of road geometry from digital vector data is presented. The method is capable of efficiently identifying circular curves with their radii and tangents (straight sections). The average error of identification ranged from 0.01 to 1.30 % for precisely drawn data and 4.81 % in the case of actual road data with noise in the location of vertices. The results demonstrate that the proposed method is faster and more precise than commonly used techniques. This approach can be used by road administrators to complete their databases with information concerning the geometry of roads. It can also be utilized by transport engineers or traffic safety analysts to investigate the possible dependence of traffic accidents on road geometries. The method presented is applicable as well to railroads and rivers or other line features.

Bíl, M., Andrášik, R., Svoboda, T., Sedoník, J., 2016. The KDE+ software: a tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology 31, 231–237. https://doi.org/10.1007/s10980-015-0265-6

Show abstract |

https://link.springer.com/article/10.1007/s10980-015-0265-6

Context: Objective identification of locations on transportation networks, where animal-vehicle collisions (AVC) occur more frequently than expected (hotspots), is an important step for the effective application of mitigation measures. Objectives: We introduce the KDE+ software which is a programmed version of the KDE+ method for effective identification of traffic accident hotspots. The software can be used in order to analyze animal-vehicle collision data. Methods: The KDE+ method is based on principles of Kernel Density Estimation (KDE). The symbol ‘+’ indicates that the method allows for the objective selection of significant clusters and for the ranking of the hotspots. It is also simultaneously applicable to an unlimited number of road segments. Results: We applied the KDE+ method to the entire Czech road network. The hotspots were ranked according to their significance. The resulting hotspots represent a short overall road length which should require a more detailed assessment in the field. The 100 most important clusters of AVC represent, for example, only 19.7 km of the entire road network (37,469 km). Conclusions: We present an objective method for hotspots identification which can be used for AVC data. This method is unique because it determines the significance level of hotspots in an objective way. The prioritization of hotspots allows a transportation manager to effectively allocate resources to a feasible number of identified hotspots. We describe the software, data preparation and present the KDE+ application to AVC data.

2015

Bíl, M., Andrášik, R., Kubeček, J., 2015. How comfortable are your cycling tracks? A new method for objective bicycle vibration measurement. Transportation Research Part C: Emerging Technologies 56, 415–425. https://doi.org/10.1016/j.trc.2015.05.007

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0968090X15001795

Cycling comfort consists of several factors. Their relevant values are important in the process of bicycle facility planning. Poor surface pavement quality manifests itself in terms of vibrations of a bicycle. This strongly influences the perception of a cycle track, general cycling comfort and the route choice as well. We introduce dynamic comfort index (DCI) which is capable of objectively describing the vibration properties of surface pavement on a track. The DCI is derived from data gathered when riding a bicycle equipped with a GPS device and an accelerometer. The most common types of devices were selected to make the DCI widely applicable. We tested DCI values on various bicycles and surface pavements. DCI values on individual cycling tracks were compared with the subjective feelings of 43 cyclists via questionnaires. A strong correlation (−0.94) was obtained between the objectively measured DCI values and the subjectively assessed evaluations. This makes the DCI approach transferable to any other environment. This method has been applied to an entire road network within the historical center of the city of Olomouc (Czech Republic). It can further be used by bicycle track administrators to monitor surface quality, by planners to obtain relevant surface pavement values, and by individual cyclists for optimal route choice.

Bíl, M., Vodák, R., Kubeček, J., Bílová, M., Sedoník, J., 2015. Evaluating Road Network Damage Caused by Natural Disasters in the Czech Republic between 1997 and 2010. Transportation Research Part A: Policy and Practice 80, 90–103. https://doi.org/10.1016/j.tra.2015.07.006

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0965856415001883

Road networks play a vital role in maintaining a functioning modern society. Many events perceptibly affect the transport supply along these networks, especially natural disasters such as floods, landslides, and earthquakes. Contrary to more common disruptions of traffic from accidents, or maintenance closures, natural disasters are capable of destroying large numbers of roads and usually cover vast areas. When evaluating network damage no single measure alone is able to describe the full extent of network destruction. In this study, we investigated six highly damaging natural disasters, which occurred in the Czech Republic between 1997 and 2010. They were all induced by extreme rainfall or by rapid snowmelt and resulted in floods and landslides. Their impacts are evaluated with respect to the damage to road networks and decreased serviceability. For mutual comparison of the impacts and their analysis we used several criteria, described in the paper, related to economic impacts, physical harm to individuals and infrastructures, and the effects on connectivity and serviceability. We also introduced a new measure based on the network efficiency index which takes into account the importance of nodes based on their population. Moreover, we provide a detailed analysis of one such event in July 1997 that significantly affected the road network of the Zlín region.

2014

Bíl, M., Kubeček, J., Andrášik, R., 2014. An Epidemiological Approach to Determining the Risk of Road Damage due to Landslides. Nat Haz. 73 (4), 1323–1335. https://doi.org/10.1007/s11069-014-1141-4

Show abstract |

https://link.springer.com/article/10.1007/s11069-014-1141-4

Disruption of segments of roads can have a significant impact on the vulnerability of the entire network. Natural disasters are frequent causes of disruptions of this kind. This article focuses on determining the risk of road disruptions due to landslides. Our approach is based on methodology widely used in the field of epidemiology. We had available data on the location of the landslides, the road network and a list of the disrupted road segments. With the use of a 2 × 2 table, we determined the relationship between landslide data and road segment disruptions and derived the risk coefficient based on the number of landslides in the vicinity of the road and its length. The result is a disruption risk map with risk coefficients ranging from 0 to 47.94. In order to distinguish the most risky segments, we calculated a threshold of 12.40 with the use of a risk breakdown in a group of segments without damage. Nineteen percentage (402 km) of the road network in the Zlín region (Czech Republic), where the methodology was applied, is located beyond this threshold. The benefits of this approach stem from its speed and potential to define the most risky areas on which a detailed geomorphologic analysis can be focused.

2013

Bíl, M., Andrášik, R., Janoška, Z., 2013. Identification of Hazardous Road Locations of Traffic Accidents by means of Kernel Density Estimation and Cluster Significance Evaluation. Accident Analysis and Prevention 55, 265–273. https://doi.org/10.1016/j.aap.2013.03.003

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0001457513000912

This paper proposes a procedure which evaluates clusters of traffic accident and organizes them according to their significance. The standard kernel density estimation was extended by statistical significance testing of the resulting clusters of the traffic accidents. This allowed us to identify the most important clusters within each section. They represent places where the kernel density function exceeds the significance level corresponding to the 95th percentile level, which is estimated using the Monte Carlo simulations. To show only the most important clusters within a set of sections, we introduced the cluster strength and cluster stability evaluation procedures. The method was applied in the Southern Moravia Region of the Czech Republic.

2023

Bíl, M., Andrášik, R., Kušta, T. Bartonička, T. (2023) Ungulate-vehicle crashes peak a month earlier than 38 years ago due to global warming. Climatic Change 176, 84.

Show abstract |

https://doi.org/10.1007/s10584-023-03558-5

Cunneyworth, P. M. K., Andrášik, R., & Bíl, M. (2023). An effect of canopy bridges on monkey vehicle collision hotspots: Spatial and spatiotemporal analyses. American Journal of Primatology, e23492.

Show abstract |

https://doi.org/10.1002/ajp.23492

Almost one‐quarter of primate species are reported to be involved in vehicle collisions. To mitigate these collisions, canopy bridges are used though their effectiveness is not broadly substantiated. We studied bridge impact on 23 years of vehicle collisions (2000–2022: N = 765) with colobus (Colobus angolensis palliatus), Sykes' (Cercopithecus mitis albogularis), and vervet (Chlorocebus pygerythrus hilgerti) monkeys in Diani, Kenya. Along a 9 km road, collisions did not decrease over the study duration, although bridges increased from 8 to 30. Using the kernel density estimation plus (KDE+) method, collisions appeared highly concentrated at some locations. These concentrations, called hotspots, represent hazardous road segments, though the hotspots for all three species overlapped for only 3% of the road length. We then inspected the collision hotspots over time, using the spatiotemporal extension of the KDE+ method. We compared hotspot presence in the 3 years before and after bridge installation to determine if bridges mitigated these hotspots. Hotspots disappeared for ~60% of bridges postinstallation, suggesting that bridges effectively reduce some collisions. However, of the bridges installed in locations that were not hotspots, 13% had hotspots emerge. Surprisingly, regardless of preinstallation hotspot occurrence, almost one‐fifth of bridges had postinstallation hotspots. To understand the extent to which bridges mitigate collisions, other factors need consideration, including species attributes and crossing behavior, and road features and vehicle volume. We used the novel analytical method because it best suited our data set, given the challenges of determining the bridge impact zone and the low collision frequency.

2022

Nezval, V., Andrášik, R., Bíl, M. 2022. Vegetation fires along the Czech rail network. Fire Ecology, 18, 15.

Show abstract |

https://fireecology.springeropen.com/articles/10.1186/s42408-022-00141-8

In the past, fires around railways were often associated with steam locomotives. Although steam locomotives have disappeared from everyday rail traffic, fires still occur. A vegetation fire near Bzenec (Czech Republic) on 21 June 2018 affected, for example, 124,110 m2 of forest and grassland. The investigation revealed that the fire was caused by a spark from a passing train. In this study, we analyzed vegetation fires that occurred near Czech railway lines between 2011 and 2019 to investigate their temporal pattern and relation to weather conditions or to identify the most hazardous locations.

2019

Keken, Z., Sedoník, J., Kušta, T., Andrášik, R., Bíl, M., 2019. Roadside vegetation influences clustering of ungulate vehicle collisions. Transportation Research Part D: Transport and Environment 73, 381–390. https://doi.org/10.1016/j.trd.2019.07.013

Show abstract |

https://www.sciencedirect.com/science/article/pii/S1361920918311659

The aim of this study was to identify landscape-related factors which could explain the concentration of traffic crashes with large ungulates in a forest environment. We worked with ungulate-vehicle collisions which took place on the Czech road network in the period 2014–2016 using the application Srazenazver.cz. With the KDE+ method, we chose the most significant hotspots with linkage to forest. For comparison we randomly selected control localities outside the KDE+ hotspots (i.e., with very low level of ungulate-vehicle collisions) but still in the forest area. A set of photos were taken at each hotspot and control locality (2 orthophotos and 4 driver views). Each set of images was then visually analysed by two independent evaluators. They did not know which set of images were for hotspots and which set of images were for control localities. From the point of view of the overall score, it cannot be said that these two groups of locations differ (p-value 0.3575, Kolmogorov-Smirnov test). The only attribute that demonstrated a difference between hotspots and randomly selected locations was “attractiveness (quality food source and cover) of the immediate vicinity of the transport infrastructure for the ungulates” (p-value 0.0016, Kolmogorov-Smirnov test). Results confirm the importance of landscape management in the surroundings of transport infrastructure, especially in its immediate vicinity. However, they did not confirm the possibility that the most dangerous locations from the UVC point of view could be identified on the basis of landscape composition and the overall state of vegetation around transport infrastructure.

Bíl, M., Andrášik, R., Sedoník, J., 2019. A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography 107, 82–90. https://doi.org/10.1016/j.apgeog.2019.04.008

Show abstract |

https://www.sciencedirect.com/science/article/pii/S0143622818309081

A number of traffic crash databases at present contain the precise positions and dates of these events. This feature allows for detailed spatiotemporal analysis of traffic crash patterns.We applied a clustering method for identification of traffic crash hotspots to the rural parts of primary roads in the Czech road network (3,933 km) where 55,296 traffic crashes occurred over 2010 – 2018. The data were analyzed using a 3-year time window which moved forward with a one-day step as an elementary temporal resolution. The spatiotemporal behavior of hotspots could therefore be analyzed in great detail.All the identified hotspots, during the monitored nine-year period, covered between 6.8% and 8.2% of the entire road network length in question. The percentage of traffic crashes within the hotspots remained stable over time at approximately 50%. Three elementary types of hotspots were identified when analyzing spatiotemporal crash patterns: hotspot emergence, stability and disappearance. Only 100 hotspots were stable (remained in approximately the same position) over the entire nine-year period. This approach can be applied to any traffic-crash time series when the precise positions and date of crashes are available.

Bíl, M., Andrášik, R., Duľa, M., Sedoník, J., 2019. On reliable identification of factors influencing wildlife-vehicle collisions along roads. Journal of Environmental Management 237C, 297–304. https://doi.org/10.1016/j.jenvman.2019.02.076

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https://www.sciencedirect.com/science/article/pii/S0301479719302270

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

2018

Favilli, F., Bíl, M., Sedoník, J., Andrášik, R., Kasal, P., Agreiter, A., Streifeneder, T., 2018. Application of KDE+ software to identify collective risk hotspots of ungulate-vehicle collisions in South Tyrol, Northern Italy. European Journal of Wildlife Research 64:59. https://doi.org/10.1007/s10344-018-1214-x

Show abstract |

https://link.springer.com/article/10.1007/s10344-018-1214-x

This paper is the first dealing with animal-vehicle collisions (AVC) with red and roe deer in South Tyrol, Northern Italy. The Autonomous Province of Bolzano (South Tyrol) has been collecting AVC data since 2012 on the entire provincial road network. Each year, AVC data accounted for more than 700 cases per year, with several socioeconomic and ecological implications. The aim of this research is to identify the locations where AVC occur more frequently than expected (hotspots) and better outline subsequent implementation of mitigation measures. For an effective identification of AVC hotspots, we applied a combined methodology of temporal and spatial analysis on AVC data collected on the South Tyrol road network in the years 2012–2014. AVC data enabled the identification of the temporal patterns, which showed different behaviors of the two target species in close proximity of the road network and throughout the 12 months. The KDE+ software applied to the 2012–2014 AVC database allowed for spatial analysis and the identification of hotspots, i.e., the road sections having the highest risk for drivers. The integration of the results, coming from the abovementioned methodologies, contributes to a detailed assessment of roads that would allow the identification of the local contributing factors and a base-line of potential problematic areas that will highlight the need for further investigation to assess whether the risk-rank is accurate and allocate effectively limited resources to a feasible number of identified hotspots and reduce the current degree of AVC in the South Tyrolean road network.

Bartonička, T., Andrášik, R., Duľa, M., Sedoník, J., Bíl, M., 2018. Identification of Local Factors Causing Clustering of Animal-Vehicle Collisions. Journal of Wildlife Management 82, 940–947. https://doi.org/10.1002/jwmg.21467

Show abstract |

https://onlinelibrary.wiley.com/doi/epdf/10.1002/jwmg.21467

Effective measures reducing risk of animal-vehicle collisions (AVC) require defining high-risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individua l AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non-random (clustered) occurrenc e of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE þ methods) identified factors causing local clustering of AVCs. Distance from forest ( < 350 m) or linear vegetation were important factors for estimating presence of clusters of AVCs; in open areas, AVC clusters were absent. Further research on effectiveness of measures reducing risk of AVC should focus on clusters of AVCs, not on the individua l AVC. We recommend that state transportati on agencies focus mitigation actions in forested areas.

Bíl, M., Andrášik, R., Bartonička, T., Křivánková, Z., Sedoník, J., 2018. An Evaluation of Odor Repellent Effectiveness in Prevention of Wildlife-Vehicle Collisions. Journal of Environmental Management 205C, 209–214. https://doi.org/10.1016/j.jenvman.2017.09.081

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0301479717309568

Wildlife-vehicle collisions (WVC) amount to 11 % of all registered traffic crashes in the Czech Republic causing, apart from numerous deaths and serious injuries to animals, property damage and injuries to car passengers. Odor repellents have the potential to lower the overall number of WVC and allow animals to cross roads at the same time. We tested the effectiveness of odor repellent preparation in prevention of WVC. 18 places were selected on the Czech road network where WVC were concentrated on the basis of traffic crash data. Control sections on the same road segments were also delimited in order to keep the traffic intensities constant. We applied a Before-After-Control-Impact (BACI) study design to control not only the effect of the measures but also the expected natural variations in wildlife populations over time. Data were compared before and after odor repellent installations. Wildlife carcass gathering was carried out during the spring and autumn. We also used the police crash database to supplement carcass data when no field works were carried out. 201 killed mammals (roe deer and wild boars) were identified in total over 47 months. We applied a Bayesian approach as only a limited numbers of WVC were available. A WVC decrease between 26 – 43 % can be expected on the treated road sections. These numbers are, however, up to three-times lower than those claimed by producers of odor preparations.

2017

Bíl, M., Andrášik. R., Nezval, V., Bílová, M., 2017. Identifying Locations along Railway Networks with the Highest Tree Fall Hazard. Applied Geography 87, 45–53. https://doi.org/10.1016/j.apgeog.2017.07.012

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http://www.sciencedirect.com/science/article/pii/S0143622817301819

Disruptions of railway traffic have many reasons. Tree falls onto railway tracks or overhead lines rank among the most common causes of disruptions of a natural origin. 2039 tree-fall events, containing up to 70 individual trees per event, were registered on the Czech railway network between 2012 and 2015. 32% of them were directly caused by 14 weather extremes during which more than 20 concurrent tree-fall events were registered. Moreover, 12 train derailments due to fallen trees were registered on Czech railways within the same period. We combined land use data along railway tracks and data on tree falls. Land use and railway tracks data were obtained from a freely available Open Street Map database. The tree fall hazard was then computed using empirical data, data on land use and a generalized rule of succession. The clustering approach was also applied to focus on localities where tree falls were concentrated regardless of the resulting segment hazard. There were 59 rail track segments (out of 2960) with the highest tree fall hazard and 267 clusters were finally identified. The clusters and the most hazardous railway segments will be among the first in the process of line side vegetation monitoring in order to minimize potential losses from tree fall. The presented method can be widely applicable elsewhere.

Bíl, M., Kubeček, J., Sedoník, J., Andrášik. R., 2017. Srazenazver.cz: A system for evidence of animal-vehicle collisions along transportation networks. Biological Conservation 213PA, 167–174. https://doi.org/10.1016/j.biocon.2017.07.012

Show abstract |

http://www.sciencedirect.com/science/article/pii/S000632071730263X

We present a system for state-wide evidence of animal-vehicle collisions (AVC). The primary part of this system is a geographic database which is connected to a web-map application. AVC data come from the Police via an online system of traffic incidents (JSDI) and from volunteers through a web or mobile interface. Data are processed using automatic scripts which identify data errors and perform spatial analyses. The application automatically computes AVC hotspots every midnight and crash densities along road sections. Hunter area administrators consequently have an overview of their areas. More than 40,000 records are currently included in this database. 50% of them were added over the last two years when it was launched. The majority of data (90%) came from JSDI. The species is known for 44% of JSDI records. The majority of the identified species were roe deer (75%), followed by wild boar (15%). Roe deer crashes occur most frequently in May within 2 h after sunset. 32.5% (56.4%) of these crashes occur within 1 h (2 h) before or after sunset or sunrise. For wild boar, the values are less distinctive (19.4% and 37.7%). Approximately 1800 AVC hotspots, which cover 0.5% of the Czech road network, were detected and visualized on a map.

2016

Bíl, M., Andrášik, R., Svoboda, T., Sedoník, J., 2016. The KDE+ software: a tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology 31, 231–237. https://doi.org/10.1007/s10980-015-0265-6

Show abstract |

https://link.springer.com/article/10.1007/s10980-015-0265-6

Context: Objective identification of locations on transportation networks, where animal-vehicle collisions (AVC) occur more frequently than expected (hotspots), is an important step for the effective application of mitigation measures. Objectives: We introduce the KDE+ software which is a programmed version of the KDE+ method for effective identification of traffic accident hotspots. The software can be used in order to analyze animal-vehicle collision data. Methods: The KDE+ method is based on principles of Kernel Density Estimation (KDE). The symbol ‘+’ indicates that the method allows for the objective selection of significant clusters and for the ranking of the hotspots. It is also simultaneously applicable to an unlimited number of road segments. Results: We applied the KDE+ method to the entire Czech road network. The hotspots were ranked according to their significance. The resulting hotspots represent a short overall road length which should require a more detailed assessment in the field. The 100 most important clusters of AVC represent, for example, only 19.7 km of the entire road network (37,469 km). Conclusions: We present an objective method for hotspots identification which can be used for AVC data. This method is unique because it determines the significance level of hotspots in an objective way. The prioritization of hotspots allows a transportation manager to effectively allocate resources to a feasible number of identified hotspots. We describe the software, data preparation and present the KDE+ application to AVC data.

2013

Bíl, M., Andrášik, R., Janoška, Z., 2013. Identification of Hazardous Road Locations of Traffic Accidents by means of Kernel Density Estimation and Cluster Significance Evaluation. Accident Analysis and Prevention 55, 265–273. https://doi.org/10.1016/j.aap.2013.03.003

Show abstract |

http://www.sciencedirect.com/science/article/pii/S0001457513000912

This paper proposes a procedure which evaluates clusters of traffic accident and organizes them according to their significance. The standard kernel density estimation was extended by statistical significance testing of the resulting clusters of the traffic accidents. This allowed us to identify the most important clusters within each section. They represent places where the kernel density function exceeds the significance level corresponding to the 95th percentile level, which is estimated using the Monte Carlo simulations. To show only the most important clusters within a set of sections, we introduced the cluster strength and cluster stability evaluation procedures. The method was applied in the Southern Moravia Region of the Czech Republic.

2020

Bíl, M., Heigl, F., Janoška, Z., Vercayie, D., Perkins, S. E., 2020. Benefits and challenges of collaborating with volunteers: examples from National Wildlife Roadkill Reporting Systems in Europe. Journal for Nature Conservation 54C, 125798. https://doi.org/10.1016/j.jnc.2020.125798

Show abstract |

https://www.sciencedirect.com/science/article/pii/S1617138119303449?via%3Dihub

Daily, a large number of animals are killed on European roads due to collisions with vehicles. A high proportion of these events, however, are not documented, as those obliged to collect such data, only record a small proportion; the police only register collisions that lead to traffic accidents, and hunters only collect data on game wildlife. Such reports disproportionately under-records small vertebrates such as birds, small mammals, amphibians and reptiles. In the last decade, however, national wildlife roadkill reporting systems have been launched, largely working with citizen scientists to collect roadkill data on a national basis that could fill this data gap. The aim of this study is, therefore, to describe for the first time, existing projects in Europe, and the user groups that submit data to them. To give a deeper understanding of such projects, we describe exemplar scientific roadkill reporting systems that currently exist in Austria, Belgium, Czechia and the United Kingdom. We define groups of people who contribute to such citizen science activities, and report our experience and best practice with these volunteers. We conclude that volunteers contribute significantly to collecting data on species that are not typically recorded in official databases. To ensure citizen-science projects perpetuate, (I) volunteers need to be motivated by the organisers to participate on a long-term basis, (II) volunteers need support in identifying roadkill species where required, and (III) regular feedback is required on how their contribution is used to produce new scientific knowledge.

2018

Bíl, M., Andrášik, R., Sedoník, J., Cícha, V., 2018. ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation. PLoS ONE 13(12): e0208407. https://doi.org/10.1371/journal.pone.0208407

Show abstract |

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208407&type=printable

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We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website.

2016

Bíl, M., Andrášik, R., Svoboda, T., Sedoník, J., 2016. The KDE+ software: a tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology 31, 231–237. https://doi.org/10.1007/s10980-015-0265-6

Show abstract |

https://link.springer.com/article/10.1007/s10980-015-0265-6

Context: Objective identification of locations on transportation networks, where animal-vehicle collisions (AVC) occur more frequently than expected (hotspots), is an important step for the effective application of mitigation measures. Objectives: We introduce the KDE+ software which is a programmed version of the KDE+ method for effective identification of traffic accident hotspots. The software can be used in order to analyze animal-vehicle collision data. Methods: The KDE+ method is based on principles of Kernel Density Estimation (KDE). The symbol ‘+’ indicates that the method allows for the objective selection of significant clusters and for the ranking of the hotspots. It is also simultaneously applicable to an unlimited number of road segments. Results: We applied the KDE+ method to the entire Czech road network. The hotspots were ranked according to their significance. The resulting hotspots represent a short overall road length which should require a more detailed assessment in the field. The 100 most important clusters of AVC represent, for example, only 19.7 km of the entire road network (37,469 km). Conclusions: We present an objective method for hotspots identification which can be used for AVC data. This method is unique because it determines the significance level of hotspots in an objective way. The prioritization of hotspots allows a transportation manager to effectively allocate resources to a feasible number of identified hotspots. We describe the software, data preparation and present the KDE+ application to AVC data.