Abstract This research work is focused on modeling vehicle following behavior under mixed traffic conditions. Vehicle-following behavior can be potentially utilized in building real-time drivers’ assistance systems or identifying collision escaping time thresholds for varying roadway and traffic conditions. For this purpose, initially, vehicular trajectory data was developed over the road sections in India. Thereafter, based on hysteresis phenomenon among the vehicles, vehicles in the following conditions are identified. Further to model the following the behavior of vehicles, selected car-following models, such as Wiedemann-74, Wiedemann-99, Gipps, Bando and Intelligent Driver Model (IDM) are calibrated for followers (subject vehicle) from each vehicle category. The model’s validity is tested by comparing the observed position and modeled position of the follower under varying conditions. Subsequently, to test the car-following model’s efficacy in replicating the following behavior, simulation runs were performed using VISSIM 9.0. For this purpose, the car following models (other than Wiedemann models) were coded in VISSIM using external driving behavior program. The models were then tested using calibrated vehicle-dependent following-behavior parameters. Based on the simulation runs, the comparison was made with derived traffic parameters for macroscopic validation. For better understanding hysteresis plots were also compared for different vehicle-following models, using simulated trajectory data. Further, simulated hysteresis for different vehicle types was compared with related plots made using actual trajectory data. It was well-established that psychophysical models (Wiedemann), multi-regime (Gipps), and IDM car-following models resemble Indian traffic behavior reasonably well, whereas single-regime car-following model, optimal velocity model fails in replicating the actual following-behavior.

中文翻译：

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在混合交通条件下使用轨迹数据评估选定车辆跟随模型的性能

摘要 本研究工作的重点是模拟混合交通条件下的车辆跟随行为。车辆跟随行为可潜在地用于构建实时驾驶员辅助系统或识别不同道路和交通状况的碰撞逃逸时间阈值。为此，最初在印度的路段上开发了车辆轨迹数据。此后，基于车辆之间的滞后现象，识别处于以下条件的车辆。为了进一步对车辆的行为进行建模，选定的跟驰模型，例如 Wiedemann-74、Wiedemann-99、Gipps、Bando 和智能驾驶员模型 (IDM)，针对每个车辆类别的跟随者（目标车辆）进行校准。通过比较不同条件下跟随者的观察位置和建模位置来测试模型的有效性。随后，为了测试跟驰模型在复制以下行为方面的功效，使用 VISSIM 9.0 进行了仿真运行。为此，使用外部驾驶行为程序在 VISSIM 中对汽车跟随模型（Wiedemann 模型除外）进行编码。然后使用经过校准的与车辆相关的跟随行为参数对模型进行测试。基于模拟运行，与用于宏观验证的派生交通参数进行了比较。为了更好地理解滞后图，还使用模拟轨迹数据比较了不同车辆跟随模型的滞后图。更多，将不同车辆类型的模拟滞后与使用实际轨迹数据制作的相关图进行比较。众所周知，心理物理模型 (Wiedemann)、多机制 (Gipps) 和 IDM 跟车模型与印度的交通行为相当相似，而单机制跟车模型、最佳速度模型无法复制实际跟车行为-行为。