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Related Experiment Video

Updated: Apr 29, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Understanding overtaking risk evolution patterns and their influencing factors based on trajectory data.

Jun Bai¹, Jaeyoung Jay Lee², Liang Zheng¹

¹School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.

Accident; Analysis and Prevention

|April 27, 2026

Summary

This summary is machine-generated.

Understanding overtaking risks is key for highway safety. This study identifies three driving patterns—hesitant, aggressive, and robust—and reveals distinct risk peaks for each, informing adaptive driving strategies.

Keywords:

Heterogeneity Overtaking behavior Random parameter logit model Risk evolution Time-series clustering

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Area of Science:

Traffic Safety
Behavioral Analysis
Automated Driving Systems

Background:

Overtaking maneuvers are critical highway events with inherent risks.
Dynamic risk assessment is crucial for developing effective adaptive driving strategies.
Previous studies often lack a stage-based analysis of overtaking risks.

Purpose of the Study:

To develop a stage-based framework for analyzing overtaking risks.
To identify and characterize distinct risk evolution patterns in overtaking behaviors.
To investigate factors influencing these risk patterns and their formation.

Main Methods:

Constructed a comprehensive risk indicator across Lane-change, Overtaking, and Back-to-lane stages.
Utilized Dynamic Time Warping for time-series clustering of overtaking trajectories.
Estimated random parameters multinomial logit models with heterogeneity in means using traffic-flow indicators.

Main Results:

Identified three risk evolution patterns: hesitant (42.45%), aggressive (10.07%), and robust (47.48%).
Hesitant drivers showed dual risk peaks; aggressive drivers peaked during overtaking; robust drivers had lowest overall risk.
Truck presence increased hesitant trajectories; higher upstream speed standard deviation correlated with aggressive behaviors.

Conclusions:

The study provides a data-driven paradigm for understanding dynamic overtaking risks.
Findings offer practical insights for adaptive risk management in automated driving systems.
Accounting for unobserved heterogeneity in traffic flow is essential for accurate modeling.