基于车辆自动识别技术的动态OD矩阵估计新方法

A Novel Dynamic OD Estimation Approach Based on Automatic Vehicle Identification Data

根据车辆自动识别(automatic vehicle identification,AVI)技术检测的新信息,提出了利用AVI检测信息进行动态OD(origin-destination)矩阵估计的新方法.该方法以粒子滤波算法思想为基础,引入AVI检测的部分路径信息、动态行程时间信息以及检测器可测性判据,首先通过贝叶斯估计算法对路径缺失的车辆信息进行缺失路径范围确定和选择概率修正;然后利用蒙特卡洛随机仿真模拟任意车辆对缺失路径的选择,进而获得基于个体车辆部分路径的初始修复OD矩阵;最后运用AVI检测的流量信息校正初始修复OD矩阵,得到最终的OD矩阵估计值.以上海市南北高架快速路网为研究对象,分析了在不同AVI覆盖率和先验精度条件下的动态OD估计精度,结果表明在60%及以上的AVI覆盖率都能获取相当高的OD估计精度;即使在50%覆盖率和60%的先验精度条件下运用本方法进行OD估计,其平均相对误差仅为28.87%.同时本方法对OD先验信息的精度要求较低,能更好地满足我国目前OD基础数据精度不高的现实需求.

Based on the new information which was detected through automatic vehicle identification （AVI） technology, an approach for dynamic OD estimation by using the AVI information is put forward. Partial trajectory, dynamic travel time and detector measurability were introduced into this approach with a reference to the particle filter, First the selection scope and the probability were reduced and collected by Bayesian estimation. Then the absented trajectory of any vehicles was determined by Monte Carlo stochastic simulation and the initial corrected OD matrix was obtained by correcting the individual vehicles trajectory. At last, the initial OD matrix was corrected by the path-link flow function based on the AVI volume information. Finally, an analysis was made of the accuracy of dynamic OD estimation on different coverage of AVI and different accuracy of prior information based on the Shanghai North-South expressway. The analysis result shows that the accuracy of OD estimation is high when the coverage is 60% and the relative error is 28.87% in 50% coverage and 60% accuracy of prior information. This approach can be used with low accuracy prior information which can better overcome the defect that the current OD information precision is low in China.