A Comparative Study on Drivers’ StopGo Behavior at Signalized Intersections Based on Decision Tree Classification Model

Joint Authors

Zhou, Ji-biao
Dong, Sheng

Source

Journal of Advanced Transportation

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-05-29

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Civil Engineering

Abstract EN

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.

American Psychological Association (APA)

Dong, Sheng& Zhou, Ji-biao. 2020. A Comparative Study on Drivers’ StopGo Behavior at Signalized Intersections Based on Decision Tree Classification Model. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175294

Modern Language Association (MLA)

Dong, Sheng& Zhou, Ji-biao. A Comparative Study on Drivers’ StopGo Behavior at Signalized Intersections Based on Decision Tree Classification Model. Journal of Advanced Transportation No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1175294

American Medical Association (AMA)

Dong, Sheng& Zhou, Ji-biao. A Comparative Study on Drivers’ StopGo Behavior at Signalized Intersections Based on Decision Tree Classification Model. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1175294

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1175294