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Journal Article
LiDAR-Based Urban Traffic Flow and Safety Assessment Using AI-Driven Surrogate Indicators
- Authors
- Dohun Kim, Hongjin Kim, Wonjong Kim
- Issue Date
- 2025-12
- Citation
- Remote Sensing, v.17, no.24, pp.1-33
- ISSN
- 2072-4292
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- Abstract
- Highlights: What are the main findings? Developed an AI-driven LiDAR analysis framework for continuous urban traffic flow and safety assessment using vehicle-mounted sensors and real-world road data collected in South Korea. Proposed two novel surrogate safety indicators, Hazardous Modified Time to Collision (HMTTC) and Searching for Safety Space (SSS), and implemented a Moving Detection System (MDS) approach to quantify both temporal and spatial collision risks. What is the implication of the main findings? The AI–LiDAR and MDS-based framework enables infrastructure-independent evaluation of urban traffic safety using surrogate indicators correlated with congestion and geometric road features. The proposed indicators and mobile sensing approach provide a scalable foundation for proactive traffic safety management and data-driven urban transportation planning. Urban mobility systems increasingly depend on remote sensing and artificial intelligence to enhance traffic monitoring and safety management. This study presents a LiDAR-based framework for urban road condition analysis and risk evaluation using vehicle-mounted sensors as dynamic remote sensing platforms. The framework integrates deep learning based object detection with mathematically defined surrogate safety indicators to quantify collision risk and evaluate evasive maneuverability in real traffic environments. Two indicators, Hazardous Modified Time to Collision (HMTTC) and Searching for Safety Space (SSS), are introduced to assess lane-level safety and spatial availability of avoidance zones. LiDAR point cloud data are processed using a Voxel RCNN architecture and converted into parameters such as density, speed, and spacing. Field experiments conducted on highways and urban corridors in South Korea reveal strong correlations between HMTTC occurrences, congestion, and geometric road features. The results demonstrate that AI-driven analysis of LiDAR data enables continuous, infrastructure-independent urban traffic safety monitoring, thereby supporting data-driven, resilient transportation systems.
- KSP Keywords
- Collision risk, Condition analysis, Data collected, Data-Driven, Detection Systems(IDS), Field experiment, Independent evaluation, Infrastructure-independent, LIDAR point cloud, LiDAR data, Mobile Sensing
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
