Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm

Zhu, Chunwu; Brown, Charles T.; Dadashova, Bahar; Ye, Xinyue; Sohrabi, Soheil; Potts, Ingrid B

doi:10.1016/j.aap.2023.106964

Cited by 14 publications

(2 citation statements)

References 56 publications

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“…In the study by Wang et al [47], the factors influencing the occurrence of accidents on e-bikes in China were pointed out, and in the study by Outay et al [48] it was also pointed out that motorcycle accidents are more serious during peak hours, which may be related to severe braking or acceleration events. In a study by Zhu et al [49] it was found that people with the same socio-economic background were more likely to be involved in accidents between bicycles and other vehicles.…”

Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model

Wang,

Ji,

et al. 2023

Sustainability

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Understanding the causes of traffic road accidents is crucial; however, as data collection is conducted by traffic police, accident-related environmental information is not available. To fill this gap, we collect information on the built environment within R = 500 m of the accident site; model the factors influencing accident severity in Shenyang, China, from 2018 to 2020 using the Random Forest algorithm; and use the SHapley Additive exPlanation method to interpret the underlying driving forces. We initially integrate five indicators of the built environment with 18 characteristics, including human and vehicle at-fault characters, infrastructure, time, climate, and land use attributes. Our results show that road type, urban/rural, season, and speed limit in the first 10 factors have a significant positive effect on accident severity; density of commercial-POI in the first 10 factors has a significant negative effect. Factors such as urban/rural and road type, commercial and vehicle type, road type, and season have significant effects on accident severity through an interactive mechanism. These findings provide important information for improving road safety.

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Section: Analysis and Discussion Of Resultsmentioning

confidence: 99%

Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model

Wang,

Ji,

et al. 2023

Sustainability

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“…. , f n }; (4) summarize the predictions for each random sample x, providing the function f of the RF [27].…”

Section: Random Forestmentioning

confidence: 99%

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Wang,

Wei,

Wang

2024

Applied Sciences

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The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures.

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A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes

Sun,

Wang,

et al. 2023

Accident Analysis & Prevention

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Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm

Cited by 14 publications

References 56 publications

Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model

Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes

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