Dash Cam videos on YouTube™ offer insights into factors related to moose-vehicle collisions

Rea, Roy V.; Johnson, Chris J.; Aitken, Daniel A.; Child, Kenneth N.; Hesse, Gayle

doi:10.1016/j.aap.2018.02.020

Cited by 19 publications

(7 citation statements)

References 27 publications

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“…e collisions would not be avoided even if the drivers had taken some evasive actions. Rea et al [24] reviewed dash cam videos on YouTube ™ that recorded moose-vehicle interactions and built a logit regression model, and found that "vehicle deceleration" was a significant variable to explain whether a moose would collide with a vehicle. Guo et al [25] evaluated the safety impacts of powered two wheelers on a shared roadway by using automated computer video analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Wang

Cheng

et al. 2021

Journal of Advanced Transportation

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Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Wang

Cheng

et al. 2021

Journal of Advanced Transportation

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“…This group does not include animals under species protection, such as the European bison or the brown bear, because of the small numbers of these individuals in Poland. The most common publication topic is road crashes with moose [17,[28][29][30][31][32][33][34][35][36]. Other large animals studied include roe deer [37], deer [32,38,39], reindeer [11], and wild boar [38].…”

Section: Introduction 1the Problem Of Road Animal-vehicle Crashesmentioning

confidence: 99%

Spatiotemporal Analysis of Road Crashes with Animals in Poland

2022

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This article describes the issue of the influences of the time factor and wildlife populations on road animal–vehicle crashes. The article presents problems associated with animal–vehicle crashes in Poland. They are the subject of many court disputes. For the purposes of the study, data on animal–vehicle road crashes were obtained from the SEWiK database, as well as data on the numbers of animals in hunting districts from 2016–2020. The relationship between the number of road crashes and time was analysed, and the relationships between the numbers of road crashes and the animal populations, as well as the locations, types, and categories of the hunting districts, were analysed as well. The factors related to changes in the wildlife populations, road crashes in previous years, and the lengths of the road networks were also analysed. The research shows no relationship between the abundance of a particular species and the number of road crashes. Instead, there is a correlation between the number of crashes in previous years and the road network length.

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“…A dashcam is widely used in developed countries like Russia, Taiwan, China, etc. (Rea et al, 2018) in vehicles for safety purposes. They serve as an important source of evidence during the disputes and to claim insurance for the big commercial transportation companies like Uber, Lyft, etc.…”

Section: Introductionmentioning

confidence: 99%

Roadside Forest Modeling Using Dashcam Videos and Convolutional Neural Nets

Joshi

Witharana

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Tree failure is a primary cause of storm-related power outages throughout the United States. Roadside vegetation management is therefore critical to electric utility companies to prevent power outages during extreme weather conditions. It is difficult to execute roadside vegetation management practices, at the landscape level, without proper monitoring of roadside forests’ physical structure and health condition. Remote sensing images and LiDAR are widely used to characterize the forest edge; however, the limitation on the temporal and spatial resolution for most of that dataset is a big challenge. Also, there is a need for a ground-level dataset that provides the vertical profile of the forest trees so that we can more accurately characterize the forest structure and health and recommend the optimal management strategies according to the local forest conditions. For the first time, we introduced Dashcam videos as an alternative to the existing aerial remote sensing data sources to characterize the roadside forest condition using the deep learning (DL) convolutional neural net (CNN) algorithms. In this study, we used dashcam videos taken during the leaf-on and leaf-off conditions and various weather conditions along the roadside. We trained a DLCNN model based on the U-Net and YOLO v5 architectures to classify the multilayer vegetation and detect utility poles and tree trunks alongside the road. Our experiment results suggest that a dashcam can be a viable alternative and complementary way to characterize the roadside vegetation and can be used in the management of roadside forests as a cost-effective data acquisition mechanism for utility companies.

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Dash Cam videos on YouTube™ offer insights into factors related to moose-vehicle collisions

Cited by 19 publications

References 27 publications

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Spatiotemporal Analysis of Road Crashes with Animals in Poland

Roadside Forest Modeling Using Dashcam Videos and Convolutional Neural Nets

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