车辆动力学特性和路口交通信号影响下的车辆动态路径规划研究

项目名称： 车辆动力学特性和路口交通信号影响下的车辆动态路径规划研究

项目编号： No.51475048

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 机械、仪表工业

项目作者： 胡林

作者单位： 长沙理工大学

项目金额： 87万元

中文摘要： 车辆动态路径规划作为疏通拥挤交通流的有效措施而成为研究热点，如何提升其实时性、准确性是关键。本项目考虑车辆动力学特性和路口交通信号的影响，基于最少预期运行时间，对具有信号控制的随机时变路网中的车辆最优路径规划问题进行研究。工作重点围绕路径规划条件下车辆动力学特性分析与建模，路段行程时间短时预测方法和最优路径规划算法这三个关键科学问题，研究车辆动力学特性、路口交通信号和驾驶员因素等对路段短时行程时间影响的内在规律，建立车辆路径规划条件下的车辆动力学模型，对路段行程时间序列进行相空间重构，并应用混沌理论建立路段行程时间短时预测模型，在此基础上，针对路口交通信号控制规律是否已知，分别提出改进的惩罚函数算法和标签修正算法，且对两种算法的归一化进行研究。通过真实路口信号模型，对项目提出的算法进行实验验证并优化，以提高车辆路径规划的实时性、准确性，使车辆路径规划更具现实意义。

中文关键词： 车辆动力学；最少预期运行时间；路段行程时间预测；车辆路径规划；随机时变

英文摘要： The dynamic path planning as an effective measure to clear the congested traffic flow becomes a new display technology, how to improve its real-time accuracy is the key problem. This project considering the vehicle dynamics and intersection signals, will study the optimal path planning problem in time varying road network with the least expected running time. The research concentrates on three key scientific problems: the vehicle dynamics analysis and modeling, the travel time short-term prediction method and the optimal path planning algorithm. This project begins with the investigation on rules how the vehicle dynamics, intersection traffic signals and driver factors influence on the road travel time, then the vehicle dynamics model is built up under the vehicle route planning conditions, the road trip time se series is reconstructed in phase space, and the chaos theory is used to establish the short-term link travel time prediction model. Based on above researches, considering the intersection traffic signal control law, known or unknown, the penalty function algorithm and improved labels correction algorithm are proposed respectively, and the normalization of this two algorithms is studied. Finally, the real intersection signal model will set up to experimental verification the proposed planning algorithm, a more realistic vehicle path planning algorithm is expected.

英文关键词： Vehicle dynamics;The least expected time;Travel Time Prediction;Vehicle path planning;Stochastic time varying