Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Yu, Rongjie; Wang, Xuesong; Yang, Kui; Abdel-Aty, Mohamed

doi:10.1016/j.aap.2015.11.029

Cited by 54 publications

(31 citation statements)

References 22 publications

(19 reference statements)

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“…Therefore, the significant variables in four time-slice models were investigated. Average speed was found to have significant negative effect on the odds of crash occurrence, which is consistent with previous studies , Ahmed et al 2012a, b, Ahmed and Abdel-Aty 2012, Xu et al 2012, Shi and Abdel-Aty 2015, Yu et al 2016, Yuan et al 2018. The left turn volume was surprisingly found to be negatively correlated with the odds of crash occurrence, which might be explained as the higher left turn volume may increase the driver awareness when approaching the entering approach, which may improve the safety performance.…”

Section: Discussionsupporting

confidence: 89%

Approach-level real-time crash risk analysis for signalized intersections

Yuan

Abdel-Aty

2018

Accident Analysis & Prevention

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100

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Intersections are among the most dangerous roadway facilities due to the complex traffic conflicting movements and frequent stop-and-go traffic. However, previous intersection safety analyses were conducted based on static and highly aggregated data (e.g., annual average daily traffic (AADT), annual crash frequency). These aggregated data may result in unreliable findings simply because they are averages and cannot represent the real conditions at the time of crash occurrence. This study attempts to investigate the relationship between crash occurrence at signalized intersections and real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Central Florida. The intersection and intersection-related crashes were collected and then divided into two types, i.e., within intersection crashes and intersection entrance crashes. Bayesian conditional logistic models were developed for these two kinds of crashes, respectively. For the within intersection models, the model results showed that the through volume from "A" approach (the traveling approach of at-fault vehicle), the left turn volume from "B" approach (near-side crossing approach), and the overall average flow ratio (OAFR) from "D" approach (far-side crossing approach), were found to have significant positive effects on the odds of crash occurrence. Moreover, the increased adaptability for the left turn signal timing of "B" approach and more priority for "A" approach could significantly decrease the odds of crash occurrence. For the intersection entrance models, average speed was found to have significant negative effect on the odds of crash occurrence. The longer average green time and longer average waiting time for the left turn phase, higher green ratio for the through phase, and higher adaptability for the through phase can significantly improve the safety performance of intersection entrance area. In addition, the average queue length on the through lanes was found to have positive effect on the odds of crash occurrence. These results are important in real-time safety applications at signalized intersections in the context of proactive traffic management.

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Section: Discussionsupporting

confidence: 89%

Approach-level real-time crash risk analysis for signalized intersections

Yuan

Abdel-Aty

2018

Accident Analysis & Prevention

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100

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“…The results of the slice 2 model indicate that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator are significantly associated with the crash risk on urban arterials. In general, these finding are consistent with previous studies, in which the average speed was found to have significant negative impact on crash occurrence Ahmed et al, 2012a, b;Ahmed and Abdel-Aty, 2012;Shi and Abdel-Aty, 2015;Xu et al, 2012;Yu et al, 2016), while adverse weather (Ahmed et al, 2012a;Xu et al, 2013a) were found to be positively correlated with crash likelihood. In terms of the effect of traffic volume, only the upstream left-turn volume was found to have significant effect on crash likelihood, which indicates that more vehicles from the intersecting road segment left turning into the subject segment may increase the crash risk on the segment.…”

Section: Conclusion and Discussionsupporting

confidence: 92%

“…Finally, the slice 2 model was selected to conduct further interpretation and model comparison. Based on the estimation results in the slice 2 model, four variables were found to be significantly associated with the crash occurrence on urban arterials: (1) the negative coefficient (-0.025) of average speed indicates that higher average speed tends to decrease the crash risk, which is consistent with other studies Ahmed et al, 2012a, b;Ahmed and Abdel-Aty, 2012;Shi and Abdel-Aty, 2015;Xu et al, 2012;Yu et al, 2016). This could be explained as the traffic condition with higher average speed, which represents more smooth traffic flow, could have better safety performance.…”

Section: Modeling Resultssupporting

confidence: 86%

“…Real-time crash risk analysis has been widely adopted to reveal crash occurrence precursors by investigating the differences in traffic conditions between crash and non-crash events. As crash risk analysis is a typical binary classification problem, the most commonly used methods are the matched case-control logistic models (Abdel-Aty and Pande, 2005;Abdel-Aty et al, 2004;Ahmed and Abdel-Aty, 2012;Xu et al, 2012;Zheng et al, 2010), Bayesian logistical models (Ahmed et al, 2012a;Shi and Abdel-Aty, 2015;Wang et al, 2017a;Wang et al, 2015a;, Bayesian random effect logistic models (Shi and Abdel-Aty, 2015;Yu et al, 2016), Bayesian random parameter logistic models (Shi and Abdel-Aty, 2015;Xu et al, 2014;Yu et al, 2017). Besides, several approaches of data mining such as neural networks (Abdel-Aty and Pande, 2005;Abdel-Aty et al, 2008), support vector machines (Yu and Abdel-Aty, 2013;, and Bayesian networks (Hossain and Muromachi, 2012;Sun and Sun, 2015) were also applied to evaluate the real-time crash risk.…”

Section: Real-time Crash Risk Analysismentioning

confidence: 99%

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Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

Yuan

Abdel-Aty

Wang

et al. 2018

Transportation Research Part C: Emerging Technologies

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Real-time safety analysis has become a hot research topic as it can more accurately reveal the relationships between real-time traffic characteristics and crash occurrence, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. This study attempts to examine the relationship between crash occurrence and realtime traffic and weather characteristics based on four urban arterials in Central Florida. Considering the substantial difference between the interrupted urban arterials and the access controlled freeways, the adaptive signal phasing data was introduced in addition to the traditional traffic data. Bayesian conditional logistic models were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 minutes (four 5-minute intervals) before the time of crash occurrence. Model comparison results indicated that the model based on 5-10 minute interval dataset performs the best. It revealed that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator were found to have significant effects on crash occurrence. Furthermore, both Bayesian random parameters logistic and Bayesian random parameters conditional logistic models were developed to compare with the Bayesian conditional logistic model, and the Bayesian random parameters conditional logistic model was found to have the best model performance in terms of the AUC and DIC values. These results are important in real-time safety applications in the context of Integrated Active Traffic Management.

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“…e crash risk analysis models were developed for total crashes and timerelated crashes to reveal the significant factors that affect crash risk [17]. In addition, typical scenarios leading to an accident were found from the modelling results.…”

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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Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Cited by 54 publications

References 22 publications

Approach-level real-time crash risk analysis for signalized intersections

Approach-level real-time crash risk analysis for signalized intersections

Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

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

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