Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

Yang, Xiaoqi; Zou, Yajie; Wu, Lingtao; Zhong, Xinzhi; Wang, Yinhai; Ijaz, Muhammad; Peng, Yichuan

doi:10.1155/2019/3521793

Cited by 8 publications

(9 citation statements)

References 41 publications

(42 reference statements)

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“…Although the finding is counter-intuitive, it is not uncommon. Some studies attributed the reason for this finding to the impact of louder noises of trucks compared with lighter vehicles in keeping the wildlife away from the roadway segments with higher truck percentage ( 26 , 28 ). Furthermore, the elevated seating position and the wider view of the drivers inside a truck may also provide advantages in spotting wildlife far down the road.…”

Section: Resultsmentioning

confidence: 99%

Investigating the Safety Effectiveness of Wildlife–Vehicle Crash Countermeasures using a Bayesian Approach with a Comparison between Carcass Removal Data and Traditional Crash Data

Ahmed

2022

Transportation Research Record: Journal of the Transportation R

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Wildlife–vehicle crashes (WVCs) pose a significant threat to not only wildlife populations but also highway safety. The most expensive WVC countermeasures include crossing structures with fencing, whereas the least expensive countermeasure is the wildlife warning signs. This study is aimed at estimating the crash modification factors (CMFs) for these two countermeasures using cross-sectional analysis. Two types of WVC data are used in this study: carcass removal data and traditional crash data. A random-intercept Bayesian approach was utilized to incorporate the contributing factors representing traffic volume, roadway geometry, weather conditions, and unobserved heterogeneity resulting from between-site variance. The No-U-Turn Hamiltonian Monte Carlo sampling technique was employed because of its high efficiency in handling complex models. The results suggest that the treatment of implementing wildlife warning signs on hotspots of high WVCs has been ineffective, perhaps because warning signs are passive devices, and the high uncertainty of wildlife interaction still remains. The crossing structures are found to be effective, with an estimated CMF of 0.66 and 0.55 using the carcass data and crash data, respectively. Recommendations could be made to implement more active information dissemination via wildlife-actuated warning signs, where crossing structures may not be feasible. The findings from this study indicate that the carcass removal data are more comprehensive than the crash data, despite the underreporting issue existing in both datasets. Therefore, a unique identifier should be added in both datasets to enable merging the data and obtain more complete results from the analyses.

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Section: Resultsmentioning

confidence: 99%

Investigating the Safety Effectiveness of Wildlife–Vehicle Crash Countermeasures using a Bayesian Approach with a Comparison between Carcass Removal Data and Traditional Crash Data

Ahmed

2022

Transportation Research Record: Journal of the Transportation R

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“…Murphy and Xia (2016) There are a few studies done both nationally and internationally which seek to identify the hotspots of AVCs. Yang et al (2019) compared the difference in hotspot identification between carcass removal data and reported AVCs. Both negative binomial data and generalized negative binomial data are applied in this study using the animal collision data collected on ten highways in Washington State.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating trends and characteristics of animal-vehicle crashes in Wyoming

Roy

Ksaibati

2021

J. sustain. dev. transp. logist.

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Animal-vehicle crashes (AVCs) are a severe concern in the United States as well as in Wyoming. This study investigated the current trends of AVCs and crash rate per vehicle miles traveled (VMT) for the State of Wyoming using police-reported crash data for ten years collected from the Wyoming Department of Transportation (WYDOT). The study also examined different driver, vehicular, roadway, and environmental conditions related factors that had a strong association with AVCs. The logistic regression model was developed to check how the factors prevalent in AVCs influence the severity of AVCs. The results showed that the percentage of AVCs to that of total crashes ranged from around 15% to 22%. Among different animals involved in crashes, the majority were deer. AVCs were found to have two peaks: dawn and dusk. AVC rate was found to be the highest in November. Also, dark and unlit conditions had a strong association with AVCs. Most of the AVCs tended to occur when there were no adverse weather conditions. When the speed limit was examined, it was found that the AVC crash rate tended to increase when the posted speed limit was higher than 60 mph. AVC rate was also higher when the road surface condition was dry. Higher posted speed limit, younger drivers, and dry road surface were also found to increase the severity of AVCs. The results identified in this study will be helpful to identify effective countermeasures to reduce AVCs in Wyoming.

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“…Measurement of risk was created named as "deer-vehicle collision index". Yang et al (2019) applied statistical analysis technique of Negative Binomial (NB) model and Generalized Negative Binomial (GNB) to differentiate the location of high-risk collision and effect of some specific parameters for two different types of reported data: carcass removal and AVC record. Empirical Binomial (EB) values are produced and the results show that further variables, such as types of road, are needed to increase the accuracy of the analysis.…”

Section: Avc Spatial Modelling Studymentioning

confidence: 99%

Review of Current Animal-Vehicle Collision (AVC) Studies

Said

Mohd²,

Faye³

et al. 2021

JSAEM

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Animal-Vehicle Collision (AVC) is one of the widest and foremost research segments in the road safety research area. This paper aims to give a critical review of highlighted points and characteristics of different types of research that is within the AVC research segment. There are distinctively four categories of AVC research namely: trend and characteristic of AVC study, AVC spatial modeling study, human-animal behavioral study, and AVC mitigation study. Specific traits of each category are explored and related reported data are presented. In conclusion, each category of AVC research required further analysis especially on gathering comprehensive field data and records.

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Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

Cited by 8 publications

References 41 publications

Investigating the Safety Effectiveness of Wildlife–Vehicle Crash Countermeasures using a Bayesian Approach with a Comparison between Carcass Removal Data and Traditional Crash Data

Investigating the Safety Effectiveness of Wildlife–Vehicle Crash Countermeasures using a Bayesian Approach with a Comparison between Carcass Removal Data and Traditional Crash Data

Investigating trends and characteristics of animal-vehicle crashes in Wyoming

Review of Current Animal-Vehicle Collision (AVC) Studies

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