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<p>Driving behaviors at urban intersections are frequently associated with increased traffic emissions, noise, and ground vibrations due to stop-and-go dynamics and complex vehicular interactions. These factors pose risks to public health and nearby buildings under heavy traffic conditions. Therefore, active environmental monitoring and evaluation at intersections are essential for sustainable urban planning. To assess traffic-related environmental impacts at an area-wide scale, a multi-dimensional evaluation framework was proposed for intersection environmental health using a roadside light detection and ranging&#xa0;(LiDAR) sensor. First, high-resolution vehicle trajectories were extracted to estimate traffic emissions, noise, and traffic-induced ground vibrations. Key indicators were derived from the pollutant evaluation results. Then, a combined weighting strategy was developed by integrating quadratic programming under ranking constraints for health-effect weight, variable-weight grey entropy under threshold constraints for data-driven weight, and game theory to balance the two perspectives. Finally, an improved fuzzy comprehensive assessment (FCA) method was proposed by incorporating an improved Jenks Natural Breaks method and standard threshold values to construct a triangular membership function for environmental health classification. Results reveal significant spatiotemporal heterogeneity in traffic-induced environmental health at intersections. At the urban intersection, the comprehensive score decreased from 69.45 during daytime to 57.12 at night, representing an approximately 18% reduction. In contrast, conditions at the suburban intersection remained stable, with scores of 72.80 in daytime and 74.54 at night. Population-dimension scores were consistently lower than environmental-dimension scores, indicating higher human sensitivity to traffic-related pollution. The proposed framework effectively captures dynamic pollution characteristics and supports intersection-level environmental health assessment.</p>

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Traffic-induced environmental health assessment at intersections using roadside LiDAR

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Yue Wang,
  • Ciyun Lin,
  • Ganghao Sun,
  • Bowen Gong,
  • Hongchao Liu

摘要

Driving behaviors at urban intersections are frequently associated with increased traffic emissions, noise, and ground vibrations due to stop-and-go dynamics and complex vehicular interactions. These factors pose risks to public health and nearby buildings under heavy traffic conditions. Therefore, active environmental monitoring and evaluation at intersections are essential for sustainable urban planning. To assess traffic-related environmental impacts at an area-wide scale, a multi-dimensional evaluation framework was proposed for intersection environmental health using a roadside light detection and ranging (LiDAR) sensor. First, high-resolution vehicle trajectories were extracted to estimate traffic emissions, noise, and traffic-induced ground vibrations. Key indicators were derived from the pollutant evaluation results. Then, a combined weighting strategy was developed by integrating quadratic programming under ranking constraints for health-effect weight, variable-weight grey entropy under threshold constraints for data-driven weight, and game theory to balance the two perspectives. Finally, an improved fuzzy comprehensive assessment (FCA) method was proposed by incorporating an improved Jenks Natural Breaks method and standard threshold values to construct a triangular membership function for environmental health classification. Results reveal significant spatiotemporal heterogeneity in traffic-induced environmental health at intersections. At the urban intersection, the comprehensive score decreased from 69.45 during daytime to 57.12 at night, representing an approximately 18% reduction. In contrast, conditions at the suburban intersection remained stable, with scores of 72.80 in daytime and 74.54 at night. Population-dimension scores were consistently lower than environmental-dimension scores, indicating higher human sensitivity to traffic-related pollution. The proposed framework effectively captures dynamic pollution characteristics and supports intersection-level environmental health assessment.