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首页> 外文会议>COTA international conference of transportation professionals >Car-Following Model Based on Driver Time-Varying Propensity through Dynamic Computing
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Car-Following Model Based on Driver Time-Varying Propensity through Dynamic Computing

机译：动态计算基于驾驶员时变倾向的跟车模型

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摘要

The car-following model, referring to the way that one car follows another in a single lane under non-free flow conditions, is the core of microscopic traffic simulation. The traditional car-following model cannot capture uncertainties related to driver during driving. These uncertainties can result from the driver's physiology, psychology, and/or mental state, namely, the driver's propensities. In this study, driver's propensity is introduced into the car-following model to account for differences between drivers. The data of driver behaviors, vehicle states, and driving environments are collected and analyzed through real vehicle experiments. The car-following model based on driver's propensity is established using a simulated annealing neural network algorithm. The simulation results show that the car-following model is accurate and feasible and can successfully imitate the car-following behaviors.
机译：跟车模型是微观交通仿真的核心，它是指在非自由流动条件下，一辆车在一条车道上跟随另一辆车的方式。传统的跟车模型无法捕捉驾驶过程中与驾驶员相关的不确定性。这些不确定性可以由驾驶员的生理，心理和/或精神状态（即驾驶员的倾向）引起。在本研究中，将驾驶员的倾向引入到跟车模型中以解决驾驶员之间的差异。通过真实的车辆实验收集并分析驾驶员行为，车辆状态和驾驶环境的数据。使用模拟退火神经网络算法建立了基于驾驶员偏好的跟车模型。仿真结果表明，跟车模型是准确可行的，可以成功地模仿跟车行为。

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