Stop or go decisions at the onset of yellow light in a connected environment: A hybrid approach of decision tree and panel mixed logit model

被引:52
作者
Ali, Yasir [1 ,2 ]
Haque, Md Mazharul [1 ]
Zheng, Zuduo [3 ]
Bliemer, Michiel C. J. [2 ]
机构
[1] Queensland Univ Technol, Fac Engn, Sch Civil & Environm Engn, Brisbane, Qld, Australia
[2] Univ Sydney, Business Sch, Inst Transport & Logist Studies, Sydney, NSW, Australia
[3] Univ Queensland, Sch Civil Engn, Brisbane, Qld, Australia
基金
澳大利亚研究理事会;
关键词
Connected environment; Road safety; Yellow light; Driving behavior; Correlated grouped random parameters model; Machine learning; LANE-CHANGING BEHAVIOR; DRIVER BEHAVIOR; RESPONSE-TIME; DILEMMA ZONE; RISK-TAKING; CRASH RISK; AGE; SAFETY; COLLISION; IMPACT;
D O I
10.1016/j.amar.2021.100165
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Driver decisions at the onset of yellow traffic lights are often critical, as inaccurate decisions may result in traffic conflicts and collisions. A future connected environment where vehicles can communicate with traffic lights is expected to minimize the uncertainty associated with a driver's decision-making at signalized intersections by providing advance information related to traffic light changes. The effectiveness of such a connected environment, however, remains unexplored due to the paucity of relevant data. This study examines driver decisions at the onset of yellow traffic lights when they are assisted with advance information about traffic light changes. Seventy-eight participants with diverse backgrounds performed driving experiments on an urban route with a signalized intersection simulated in the CARRS-Q Advanced Driving Simulator. The experiment consisted of two randomized driving conditions: baseline (without advance information aids) and connected environment (with advance information aids). Contrary to the existing literature, this study employs a hybrid approach, leveraging the combined benefits of data mining to identify a priori relationships and a panel mixed logit model (more specifically, correlated grouped random parameters logit with heterogeneity-in-means approach) to account for unobserved heterogeneity as well as the correlation among random parameters. Our analysis shows that drivers in the connected environment are less likely to proceed through intersections at the onset of yellow light compared to the baseline condition. However, at the individual driver-level, the connected environment's impact on driver decisions is mixed. Female drivers have been found to have a higher propensity for yellow light running in the connected environment than that of male drivers. Overall, the connected environment assists drivers in making safer decisions at the onset of yellow light. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:20
相关论文
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