Intersection Complexity Quantification Considering Driver Behavior Characteristics and Subjective Cognition

Intersections with high complexity often present an increased risk of accidents, thereby reducing traffic safety. Current models for measuring intersection complexity primarily focus on objective factors that influence intersection operation. However, they fail to consider the impact of intersection complexity on driver behavior or the feedback mechanism drivers exhibit in response to complex traffic environments at intersections. This study aims to investigate the intrinsic connection between driver behavior and intersection complexity. A real‐vehicle experiment was conducted using three two‐phase signal‐controlled level intersections, each varying in objective complexity. Data on seven indices related to driver behavior characteristics and subjective cognition were collected from 28 participants during the experiment. Two methods were employed to analyze the data: (1) a descriptive analysis of driving behavior characteristics under varying levels of intersection complexity and (2) an entropy‐object topologically comprehensive evaluation method for measuring two‐phase intersection complexity based on driver behavior characteristics and subjective cognition. The results indicated that (1) drivers’ subjective perceptions of the complexity of two‐phase signal‐controlled intersections significantly differed from the calculated objective complexity, (2) differences in the effects of varying signal transition methods on driver behavior at complex intersections were not statistically significant, and (3) a two‐phase intersection complexity measurement model based on driver behavior characteristics and subjective perceptions was developed and validated. These findings contribute to understanding the intrinsic relationship between driver behavior and intersection complexity in urban settings. Future research could integrate intelligent algorithms to enhance the safety of autonomous vehicles navigating intersections.

Authors

Institutions

Publication Details

Journal
Journal of Advanced Transportation
Published
2025-01-01
DOI
https://doi.org/10.1155/atr/6161135
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intersection Complexity Quantification Considering Driver Behavior Characteristics and Subjective Cognition

Lei Yang, Wenchen Yang, Chang’an Xiong, Yulin Zhou et al.
Journal of Advanced Transportation
Human-Automation Interaction and Safety
article

Intersection Complexity Quantification Considering Driver Behavior Characteristics and Subjective Cognition

Lei Yang, Wenchen Yang, Chang’an Xiong, Yulin Zhou, Fengxiang Guo, Wei Li
article en

Abstract

Intersections with high complexity often present an increased risk of accidents, thereby reducing traffic safety. Current models for measuring intersection complexity primarily focus on objective factors that influence intersection operation. However, they fail to consider the impact of intersection complexity on driver behavior or the feedback mechanism drivers exhibit in response to complex traffic environments at intersections. This study aims to investigate the intrinsic connection between driver behavior and intersection complexity. A real‐vehicle experiment was conducted using three two‐phase signal‐controlled level intersections, each varying in objective complexity. Data on seven indices related to driver behavior characteristics and subjective cognition were collected from 28 participants during the experiment. Two methods were employed to analyze the data: (1) a descriptive analysis of driving behavior characteristics under varying levels of intersection complexity and (2) an entropy‐object topologically comprehensive evaluation method for measuring two‐phase intersection complexity based on driver behavior characteristics and subjective cognition. The results indicated that (1) drivers’ subjective perceptions of the complexity of two‐phase signal‐controlled intersections significantly differed from the calculated objective complexity, (2) differences in the effects of varying signal transition methods on driver behavior at complex intersections were not statistically significant, and (3) a two‐phase intersection complexity measurement model based on driver behavior characteristics and subjective perceptions was developed and validated. These findings contribute to understanding the intrinsic relationship between driver behavior and intersection complexity in urban settings. Future research could integrate intelligent algorithms to enhance the safety of autonomous vehicles navigating intersections.

Journal of Advanced TransportationVol. 2025(1)
Kunming University of Science and Technology (CN), Yunnan University (CN), Yunnan Provincial Department of Transportation (CN)
National Natural Science Foundation of China, Kunming University of Science and Technology, Yunnan Provincial Department of Education, Yunnan Provincial Science and Technology Department, Yunnan Provincial Department of Education Science Research Fund Project, Analysis and Testing Foundation of Kunming University of Science and Technology
Openalex Percentile: Top 92%
Human-Automation Interaction and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.