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Traffic-induced environmental health assessment at intersections using roadside LiDAR
Yue Wang1, Ciyun Lin2,3, Ganghao Sun1
1Department of Traffic Information and Control Engineering, Jilin University, Changchun, 130022, China.
Environmental Monitoring and Assessment
|August 5, 2026
Summary
This study introduces a novel framework using LiDAR sensors to evaluate intersection environmental health, assessing traffic emissions, noise, and vibrations. Findings highlight significant differences between urban and suburban intersections, emphasizing human sensitivity to pollution.
Area of Science:
- Environmental Science
- Urban Planning
- Transportation Engineering
Background:
- Urban intersections generate significant traffic emissions, noise, and ground vibrations, impacting public health and infrastructure.
- Effective environmental monitoring at intersections is crucial for sustainable urban development and risk mitigation.
- Existing assessment methods often lack the multi-dimensional and dynamic evaluation capabilities needed for complex urban environments.
Purpose of the Study:
- To develop and validate a multi-dimensional evaluation framework for intersection environmental health using roadside LiDAR technology.
- To quantify traffic-related environmental impacts, including emissions, noise, and ground vibrations, at an area-wide scale.
- To establish a robust method for classifying environmental health at intersections based on integrated assessment.
Main Methods:
- Utilized a roadside light detection and ranging (LiDAR) sensor to extract high-resolution vehicle trajectories.
- Estimated traffic emissions, noise, and ground vibrations from trajectory data.
- Developed a combined weighting strategy integrating quadratic programming, grey entropy, and game theory.
- Applied an improved fuzzy comprehensive assessment (FCA) method with Jenks Natural Breaks and standard thresholds for classification.
Main Results:
- Demonstrated significant spatiotemporal heterogeneity in traffic-induced environmental health at intersections.
- Observed a substantial decrease in comprehensive scores from daytime to nighttime at an urban intersection (69.45 to 57.12).
- Found stable environmental health scores at a suburban intersection (72.80 daytime, 74.54 nighttime).
- Indicated consistently lower population-dimension scores compared to environmental-dimension scores, suggesting higher human sensitivity to pollution.
Conclusions:
- The proposed framework effectively captures dynamic pollution characteristics and supports intersection-level environmental health assessment.
- The findings underscore the importance of considering diurnal variations and location-specific factors in urban environmental management.
- The study highlights the critical need to address human sensitivity in traffic-related pollution mitigation strategies.
