Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data

Abstract

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected to capture within-group and between-group characteristics of the stages. Besides, this study also examines the prediction performance of conflict stage identification using specific driving behaviour characteristics associated with each stage. Results reveal variations in dominant driving characteristics and predictive importance across stages. Heterogeneity exists within stages, with differences among clusters. Drivers slow down during in-conflict, with decreasing speed reduction as stages progress. Reaction time increases in post-conflict. Insufficient space gaps contribute to rear-end conflicts in the in-conflict stage. Furthermore, the prediction performance of conflict stage identification, based on the specific driving behaviour characteristics associated with each stage, is commendable. This study enhances understanding and prediction of conflict stage identification in rear-end conflicts.

Practitioner summary: This study explores driving behaviour in rear-end conflict stages using trajectory data. It identifies pre-, in-, and post-conflict stages via time-to-collision indicator and assesses within-group and between-group characteristics. Besides, prediction performance for conflict stage identification based on these characteristics is commendable. This research enhances understanding and prediction of rear-end conflicts.

Keywords:

• Rear-end collision
• conflict stage
• driving behaviour
• two-step clustering
• machine learning

Ethical approval

We confirm that all the research meets ethical guidelines and adheres to the legal requirements of the study country.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was sponsored by the National Natural Science Foundation of China (71901223), Natural Science Foundation of Hunan Province (2021JJ40746), Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (Changsha University of Science & Technology) (kfj220701), and the Postgraduate Research and Innovation Project of Central South University (No. 2023ZZTS0343).