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<p>This study investigates the relationship between vehicle speed and following distance using real-world driving data collected via global positioning system (GPS) and light detection and ranging (LiDAR) sensors in urban traffic. Results show that following distance (FD) increases with vehicle speed, reflecting driver comfort and perceived safety. Speed enforcement cameras and road curvature were found to significantly affect driver behavior, often leading to congestion. A vehicle speed prediction model was developed using a Fine Tree regression algorithm, incorporating features such as following distance, road curvature, speed limit, and driver’s comfort speed (DCS). The model achieved strong performance (R² = 0.80, RMSE = 11.62&#xa0;km/h) and was validated on unseen data. These findings offer practical insights for intelligent traffic systems and lay the groundwork for future modeling of following distance under varying traffic conditions.</p>

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Predictive Modeling of Vehicle Speed in Urban Traffic Using LiDAR-Measured Following Distance

  • 期刊论文

  • 发表日期：

    2025年11月5日

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  • Wookeun Choi,
  • Seongsu Kim,
  • Jingyu Yang,
  • Junghwan Kim

摘要

This study investigates the relationship between vehicle speed and following distance using real-world driving data collected via global positioning system (GPS) and light detection and ranging (LiDAR) sensors in urban traffic. Results show that following distance (FD) increases with vehicle speed, reflecting driver comfort and perceived safety. Speed enforcement cameras and road curvature were found to significantly affect driver behavior, often leading to congestion. A vehicle speed prediction model was developed using a Fine Tree regression algorithm, incorporating features such as following distance, road curvature, speed limit, and driver’s comfort speed (DCS). The model achieved strong performance (R² = 0.80, RMSE = 11.62 km/h) and was validated on unseen data. These findings offer practical insights for intelligent traffic systems and lay the groundwork for future modeling of following distance under varying traffic conditions.