Accurate representation of observed driving behavior is critical for effectively evaluating safety and performance interventions in simulation modeling. In this study, we implement and evaluate a safety-based Optimal Velocity Model (OVM) to provide a high-fidelity replication of safety-critical behavior in microscopic simulation and showcase its implications for safety-focused assessments of traffic control strategies. A comprehensive simulation model is created for the site of study in PTV VISSIM utilizing detailed vehicle trajectory information extracted from real-time video inference, which are also used to calibrate the parameters of the safety-based OVM to replicate the observed driving behavior in the site of study. The calibrated model is then incorporated as an external driver model that overtakes VISSIM's default Wiedemann 74 model during simulated car-following episodes. The results of the preliminary analysis show the significant improvements achieved by using our model in replicating the existing safety conflicts observed at the site of the study. We then utilize this improved representation of the status quo to assess the potential impact of different scenarios of signal control and speed limit enforcement in reducing those existing conflicts by up to 23%. The results of this study showcase the considerable improvements that can be achieved by utilizing data-driven car-following behavior modeling, and the workflow presented provides an end-to-end, scalable, automated, and generalizable approach for replicating the existing driving behavior observed at a site of interest in microscopic simulation by utilizing vehicle trajectories efficiently extracted via roadside video inference.
翻译:在模拟模型中,我们实施并评价一个基于安全的优化快速度模型(OVM),以便在微型模拟中提供高度信任性的安全关键行为复制,并展示其对以安全为重点的交通管制战略评估的影响。初步分析的结果显示,利用我们的模型复制研究现场观察到的现有安全冲突,取得了重大改进。然后,我们利用这一改进的假设现状来评估基于安全的OVM参数的潜在影响,在研究地点复制观察到的驱动行为。在模拟汽车跟踪时,校准模型被作为外部驱动模型,超过VISSIM默认的Wiedmann 74模型。初步分析的结果显示,利用我们的模型复制在研究地点观察到的现有安全冲突,取得了显著的改进。然后,我们利用这一改进的假设来评估基于安全的OVM参数在研究地点复制观察到的已观察到的驱动行为。 校准模型随后被纳入外部驱动模型,在减少现有车辆冲突中的信号控制和执行速度限制的潜在影响,通过使用可观测到的车辆行为到23 %。