PDF(Pubmed)
Abstract：
Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, and pedestrians. Unlike other traditional methods of trajectory data collection, LiDAR\'s high-speed data processing, fine angular resolution, high measurement accuracy, and high performance in adverse weather and low-light conditions make it well suited for applications requiring real-time response, such as autonomous vehicles. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and their accurate calibration strategies for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point clouds collected at six urban signalized intersections in Lubbock, Texas, in the USA. Each study intersection was modeled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of the Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model\'s parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the combined use of LiDAR-based vehicle trajectory data, microsimulation, and surrogate safety assessment tools to transportation professionals. This integration allows for more accurate and proactive safety evaluations, which are essential for designing safer transportation systems, effective traffic control strategies, and predicting future congestion problems.
摘要：
研究表明，车辆轨迹数据对于校准微观仿真模型是有效的。光检测和测距（LiDAR）技术提供高分辨率的3D数据，允许对周围环境进行详细的映射，包括道路几何形状，路边基础设施，和移动的物体，如车辆，骑自行车的人,和行人。与其他传统的轨迹数据收集方法不同，激光雷达的高速数据处理，精细的角度分辨率，测量精度高,在恶劣天气和弱光条件下的高性能使其非常适合需要实时响应的应用，比如自动驾驶汽车。这项研究提出了一个全面的框架，用于将LiDAR传感器数据集成到仿真模型中，并为主动安全分析提供准确的校准策略。从Lubbock的六个城市信号交叉口收集的LiDAR点云中提取车辆轨迹数据，德州,在美国。使用PTVVISSIM对每个研究交叉点进行建模，并进行校准以复制观察到的野外场景。在VISSIM中，使用了定向蛮力方法来校准Wiedemann1999模型的两个汽车跟随和两个车道改变参数，平均准确率为92.7%。从校准模型中提取的追尾冲突与十年历史碰撞数据集相结合，将其拟合到负二项（NB）模型中，以估计模型的参数。在所有六个十字路口中，后端冲突计数是观察到的后端碰撞频率的统计学显著预测因子(p值<0.05)。这项研究的结果为基于激光雷达的车辆轨迹数据的组合使用提供了一个框架，微观模拟，以及运输专业人员的替代安全评估工具。这种集成允许更准确和主动的安全评估，这对于设计更安全的运输系统至关重要，有效的交通控制策略，并预测未来的拥堵问题。
