A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model

The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making proces...

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Main Authors: Sheng Dong, Jibiao Zhou
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2020/1250827
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author Sheng Dong
Jibiao Zhou
author_facet Sheng Dong
Jibiao Zhou
author_sort Sheng Dong
collection DOAJ
description The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.
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spelling doaj-art-837296991b2b4902bfc9e3c101fddfee2025-08-20T02:09:01ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/12508271250827A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification ModelSheng Dong0Jibiao Zhou1School of Civil and Transportation Engineering, Ningbo University of Technology, Fenghua Rd. #201, Jiangbei District, Ningbo, Zhejiang 315211, ChinaDepartment of Transportation Engineering, Tongji University, Caoan Rd. #4800, Shanghai 201804, ChinaThe stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.http://dx.doi.org/10.1155/2020/1250827
spellingShingle Sheng Dong
Jibiao Zhou
A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
Journal of Advanced Transportation
title A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
title_full A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
title_fullStr A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
title_full_unstemmed A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
title_short A Comparative Study on Drivers’ Stop/Go Behavior at Signalized Intersections Based on Decision Tree Classification Model
title_sort comparative study on drivers stop go behavior at signalized intersections based on decision tree classification model
url http://dx.doi.org/10.1155/2020/1250827
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AT jibiaozhou acomparativestudyondriversstopgobehavioratsignalizedintersectionsbasedondecisiontreeclassificationmodel
AT shengdong comparativestudyondriversstopgobehavioratsignalizedintersectionsbasedondecisiontreeclassificationmodel
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