基于数据驱动的城市地下快速路跟驰行为模型构建

Modeling of Car-Following Behavior on Urban Underground Expressways Based on Data-Driven Methods

摘要:

为了揭示地下快速路交通流的运行特性，利用驾驶模拟器获得的高精度车辆轨迹数据，针对地下快速路提出一种基于数据驱动方法的车辆跟驰模型，并对该模型进行了标定与验证。首先，根据上海市北横通道东段场景模型进行驾驶模拟实验以获取车辆跟驰数据；其次，选择了采用支持向量回归（SVR）方法建立车辆跟驰模型，对模型改进并引入了驾驶行为约束条件；最后，利用实验数据对改进过的SVR车辆跟驰模型进行了标定与验证。结果表明，考虑驾驶行为约束的支持向量回归车辆跟驰模型能很好地描述地下快速路中的车辆跟驰行为；该模型具有一定的可移植性，在其他地下快速路上也有较高的精度。建立的车辆跟驰模型可以量化地分析地下快速路中跟驰车辆间的相互作用，为进行交通仿真和风险研究提供基础。

Abstract:

In order to reveal the operational characteristics of traffic flow on underground expressways， a car-following model based on the data-driven method was proposed by using the high-precision vehicle trajectory data obtained by driving simulator， and was calibrated and verified. First， the driving simulation experiment was conducted according to the scenario model of the east section of the North Cross Passage in Shanghai to obtain the car-following data. Next，the support vector regression （SVR） method was selected to establish the car-following model， into which the driving behavior constraints were introduced.Finally， the improved SVR-based car-following model was calibrated and verified by using the experimental data. The results show that the support-vector-regression-based car-following model considering driving behavior constraints can well describe the car-following behavior on underground expressways. The model possesses a great transplantability， which has a high accuracy on other underground expressways. The car-following model proposed in this paper can quantitatively analyze the interaction of vehicles on underground expressways， and provide the basis for traffic simulation and risk research.