Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

Shi, Qixin; Abdel-Aty, Mohamed; Yu, Rongjie

doi:10.1016/j.aap.2015.12.007

Cited by 29 publications

(13 citation statements)

References 29 publications

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“…From the variable importance plot, the RI, CL, and Dg are the top featured factors, which can be considered to be important parameters affecting the number of accidents based on the road surface conditions for the Naebu Expressway. This finding supports previous literature reports, where the main factor affecting traffic injury severity was determined to be the designed road geometry [60,61] and one of the factors that was significantly related to severity was road wetness [62,63]. In addition, it has been found that the traffic accident severity correlates with Dg and that male drivers show increased severity [64].…”

Section: Discussionsupporting

confidence: 90%

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

2019

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There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.

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Section: Discussionsupporting

confidence: 90%

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

2019

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“…e results indicated that crash probability is related to higher speeds, greater volume, and high between-lanes speed variation [15]. Shi et al explored the impact of real-time traffic flow on urban expressway crash probability [16].…”

Section: Introductionmentioning

confidence: 99%

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Zhang

et al. 2020

Journal of Advanced Transportation

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Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway.

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“…Several ITS applications such as Variable Speed Limits (VSL) or cooperative systems are designed to provide speed harmonization anticipating that this will lead to lower crash rates (Farah and Koutsopoulos, 2014;Strömgren and Lind, 2016). However, studies considering speed variations as a contributory factor are relatively low in number and their results are varying (Kockelman and Ma, 2007;Quddus, 2013;Shi et al, 2016). Some of the studies find speed variations to be positively associated with crashes (Quddus, 2013;Tanishita and Wee, 2016;Wang et al, 2018) while others find non-significant relationships between speed variations and crash risk (Kockelman and Ma, 2007).…”

Section: Introductionmentioning

confidence: 99%

Impacts of speed variations on freeway crashes by severity and vehicle type

Choudhary

Imprialou

Velaga

et al. 2018

Accident Analysis & Prevention

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Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a fiveminute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures.

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Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

Cited by 29 publications

References 29 publications

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Impacts of speed variations on freeway crashes by severity and vehicle type

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