Department of Computer Systems and Technologies, Technical University Sofia, Plovdiv Branch, 4017 Plovdiv, Bulgaria.
Sensors (Basel). 2022 Dec 14;22(24):9801. doi: 10.3390/s22249801.
Driver behavior models are an important part of road traffic simulation modeling. They encompass characteristics such as mood, fatigue, and response to distracting conditions. The relationships between external factors and the way drivers perform tasks can also be represented in models. This article proposes a methodology for establishing parameters of driver behavior models. The methodology is based on road traffic data and determines the car-following model and routing algorithm and their parameters that best describe driving habits. Sequential and parallel implementation of the methodology through the urban mobility simulator SUMO and Python are proposed. Four car-following models and three routing algorithms and their parameters are investigated. The results of the performed simulations prove the applicability of the methodology. Based on more than 7000 simulations performed, it is concluded that in future experiments of the traffic in Plovdiv it is appropriate to use a routing algorithm with the default routing step and the car-following model with the default configuration parameters.
驾驶员行为模型是道路交通模拟建模的重要组成部分。它们包含情绪、疲劳和对分散注意力的情况的反应等特征。模型还可以表示外部因素与驾驶员执行任务方式之间的关系。本文提出了一种建立驾驶员行为模型参数的方法。该方法基于道路交通数据，确定了最能描述驾驶习惯的跟驰模型和路径规划算法及其参数。通过城市交通模拟器 SUMO 和 Python 提出了该方法的顺序和并行实现。研究了四种跟驰模型、三种路径规划算法及其参数。所进行的模拟结果证明了该方法的适用性。基于执行的 7000 多次模拟，得出的结论是，在未来的普罗夫迪夫交通实验中，使用默认路径规划步长的路径规划算法和具有默认配置参数的跟驰模型是合适的。
