Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes

Yu, Rongjie; Abdel-Aty, Mohamed

doi:10.1016/j.aap.2013.04.025

Cited by 110 publications

(44 citation statements)

References 30 publications

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“…As shown in Table 2, the results indicate that both SV and MV vehicle crashes are more likely to occur when the speed gap gets larger. This finding is partially similar to those in some previous studies [17]. In addition, the increase of truck ratio will decrease the likelihood of multivehicle crashes, which is in accordance with the conclusions drawn by Anastasopoulos and Mannering [37].…”

Section: Traffic Characteristicsupporting

confidence: 82%

“…Real-time crash risk prediction which seeks to identify crash precursor, on the other hand, shows a great appeal for proactive traffic management. It has become a hot topic and been frequently examined by researchers in recent years [17,26,27].…”

Section: Real-time Crash Predictionmentioning

confidence: 99%

“…To account for the dependence between crash types, researchers have proposed multivariate models to study SV and MV accidents jointly. For instance, Yu and Abdel-Aty [17] employed Bayesian bivariate Poisson-lognormal model and hierarchical Poisson models to examine the different characteristic of SV and MV crashes.…”

Section: Studies On Sv and MVmentioning

confidence: 99%

“…However, those models largely ignored the shared unobserved effects between SV and MV crashes, which is problematic and leads to inconsistent estimates [13]. To account for these shared heterogeneities between SV and MV crashes, researchers have proposed advanced models such as bivariate Poisson-gamma/lognormal models to study SV and MV accidents jointly [14][15][16][17]. These previous studies mainly employed count data models and had undoubtedly provided many useful finding which contributed to the overall understanding of the characteristics of SV and MV accidents.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

Dong

Chen

et al. 2018

Journal of Advanced Transportation

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Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

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Section: Traffic Characteristicsupporting

confidence: 82%

Section: Real-time Crash Predictionmentioning

confidence: 99%

Section: Studies On Sv and MVmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

Dong

Chen

et al. 2018

Journal of Advanced Transportation

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“…The main cause of these crashes at intersections is the convergence of different traffic flows at a point. Accident at intersections can be due to different factors, each of which requires a suitable approach [10].…”

Section: Urban Intersectionsmentioning

confidence: 99%

Modeling and Evaluation of Intelligent Monitoring Systems at Urban Intersections to Improve Real-Time Monitoring of Congestion and Traffic Safety

Fatholahzadeh¹,

Akbarizadeh²,

Romoozi³

2018

IJCATR

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Abstract:In recent decades, with the advent of technology and information technology, transportation systems have also been moving in this direction, and one of the challenges of the transportation system is due to the multiplicity and increase of vehicle management and control. Many techniques and methods have been proposed in this field that in today's world, the use of intelligent systems has a higher efficiency. In fact, safety has the ability to reduce the number of vehicles with minimal delay and create discomfort for the users depends directly in order and arrangement to facilitate the immediate and direct involvement of the vehicle in traffic flow. One of the ways to demonstrate the efficiency of intelligent monitoring systems is create model before the implementation phase. This article aims at modeling and evaluating an intelligent surveillance system at urban intersections, which is carried out using colored petri nets. In the proposed method, first, the urban intersection architecture is described with the benefit of traffic rules then, the inconsistency and coincidence states are determined and finally, reliability and response time are measured by the CpnTools with the help of Petri Nets. The results show that the intelligent monitoring equipment related to the intelligent surveillance system increases the safety of the road network and, finally, has secured and fluent traffic.

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Impact of Combined Alignments and Different Weather Conditions on Vehicle Rollovers

Alrejjal

Ksaibati

2021

KSCE J Civ Eng

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Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes

Cited by 110 publications

References 30 publications

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

Modeling and Evaluation of Intelligent Monitoring Systems at Urban Intersections to Improve Real-Time Monitoring of Congestion and Traffic Safety

Impact of Combined Alignments and Different Weather Conditions on Vehicle Rollovers

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