Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China

Rear-end accidents are the most common accident type at signalized intersections because of the different driving tendencies in the dilemma zone (DZ), where drivers are faced with indecisiveness of making “stop or go” decisions at yellow onset. In various researches, the number of vehicles in the DZ...

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Main Authors: Weijie Wang, Yingshuai Li, Jian Lu, Yaping Li, Qian Wan
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2019/4836908
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spelling doaj-8dc91f50713646b2a0ba00f4bab5f5072020-11-25T00:56:39ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/48369084836908Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in ChinaWeijie Wang0Yingshuai Li1Jian Lu2Yaping Li3Qian Wan4School of Transportation, Nanjing Tech University, Nanjing 210009, ChinaSchool of Transportation, Nanjing Tech University, Nanjing 210009, ChinaJiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, ChinaJiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, ChinaHualan Design & Consulting Group, Hua Dong Lu #39, Nanning 530011, ChinaRear-end accidents are the most common accident type at signalized intersections because of the different driving tendencies in the dilemma zone (DZ), where drivers are faced with indecisiveness of making “stop or go” decisions at yellow onset. In various researches, the number of vehicles in the DZ has been used as a safety indicator—the more the vehicles in the DZ, the higher the probability of rear-end accidents. However, the DZ-associated rear-end accident potential varies depending on drivers’ driving tendencies and the situations (position and speed) at the yellow onset. This study’s primary objective is to explore how the driving tendency impacts the DZ distribution and the probability of rear-end accidents. To achieve this, three types of driving tendencies were classified using K-means clustering analysis based on driving variables. Further, the boundary of the DZ is determined by logistic regression model of drivers’ stop/go decision. Then, we proposed the conditional probability model of rear-end accidents and developed a Monte Carlo simulation framework to calculate the model. The results indicate that the rear-end accident probability is dependent on the driving tendency even at the same position with the same speed in the DZ. The aggressive type has the highest risk probability followed by conservative and then the normal types. The quantitative results of the study can provide the basis for rear-end accident assessments.http://dx.doi.org/10.1155/2019/4836908
collection DOAJ
language English
format Article
sources DOAJ
author Weijie Wang
Yingshuai Li
Jian Lu
Yaping Li
Qian Wan
spellingShingle Weijie Wang
Yingshuai Li
Jian Lu
Yaping Li
Qian Wan
Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China
Journal of Advanced Transportation
author_facet Weijie Wang
Yingshuai Li
Jian Lu
Yaping Li
Qian Wan
author_sort Weijie Wang
title Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China
title_short Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China
title_full Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China
title_fullStr Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China
title_full_unstemmed Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China
title_sort estimating rear-end accident probabilities with different driving tendencies at signalized intersections in china
publisher Hindawi-Wiley
series Journal of Advanced Transportation
issn 0197-6729
2042-3195
publishDate 2019-01-01
description Rear-end accidents are the most common accident type at signalized intersections because of the different driving tendencies in the dilemma zone (DZ), where drivers are faced with indecisiveness of making “stop or go” decisions at yellow onset. In various researches, the number of vehicles in the DZ has been used as a safety indicator—the more the vehicles in the DZ, the higher the probability of rear-end accidents. However, the DZ-associated rear-end accident potential varies depending on drivers’ driving tendencies and the situations (position and speed) at the yellow onset. This study’s primary objective is to explore how the driving tendency impacts the DZ distribution and the probability of rear-end accidents. To achieve this, three types of driving tendencies were classified using K-means clustering analysis based on driving variables. Further, the boundary of the DZ is determined by logistic regression model of drivers’ stop/go decision. Then, we proposed the conditional probability model of rear-end accidents and developed a Monte Carlo simulation framework to calculate the model. The results indicate that the rear-end accident probability is dependent on the driving tendency even at the same position with the same speed in the DZ. The aggressive type has the highest risk probability followed by conservative and then the normal types. The quantitative results of the study can provide the basis for rear-end accident assessments.
url http://dx.doi.org/10.1155/2019/4836908
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AT yingshuaili estimatingrearendaccidentprobabilitieswithdifferentdrivingtendenciesatsignalizedintersectionsinchina
AT jianlu estimatingrearendaccidentprobabilitieswithdifferentdrivingtendenciesatsignalizedintersectionsinchina
AT yapingli estimatingrearendaccidentprobabilitieswithdifferentdrivingtendenciesatsignalizedintersectionsinchina
AT qianwan estimatingrearendaccidentprobabilitieswithdifferentdrivingtendenciesatsignalizedintersectionsinchina
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