Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model
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- 作者：Yilong Ren ylren@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Yunpeng Wang ypwang@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author (Professor) ; Xinkai Wu xinkaiwu@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author (Professor) ; Guizhen Yu ; yugz@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author (Associate Professor) ; Chuan Ding cding@buaa.edu.cn" class="auth_mail" title="E-mail the corresponding author (Assistant Professor)
- 关键词：Red light running ; Influential factors ; Rare events ; Logistic regression ; High-resolution traffic data
- 刊名：Accident Analysis & Prevention
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：95
- 期：part_PA
- 页码：266-273
- 全文大小：576 K
文摘
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9-month’s Red Light Running (RLR) data were extracted from high-resolution traffic and signal event data to study drivers’ RLR behaviors.
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The significant influential factors which affect drivers’ RLR behaviors were identified.
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A modified rare events logistic regression model was developed for RLR prediction.
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The new method significantly improves the prediction accuracy and brings great potential for future field applications.