论文摘要
We develop a Kalman filter for predicting trafficflow at urban arterials based on data obtained from connected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period.Moreover, it can predict the traffic flow for various penetration rates of connected vehicles（the ratio of the number of connected vehicles to the total number of vehicles）. Atfirst, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetration rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator.We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous prediction methods depend highly on data from fixed sensors（i.e., loop detectors and video cameras）, which are associated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.
论文目录
文章来源
类型: 期刊论文
作者: Azadeh Emami,Majid Sarvi,Saeed Asadi Bagloee
来源: Journal of Modern Transportation 2019年03期
年度: 2019
分类: 工程科技Ⅱ辑,信息科技
专业: 公路与水路运输,计算机软件及计算机应用
单位: Department of Infrastructure Engineering, University of Melbourne
基金: sponsored by the Australian Integrated Multimodal EcoSystem (AIMES),https:,industry.eng. unimelb.edu.au,aimes
分类号: U495
页码: 222-232
总页数: 11
文件大小: 704K
下载量: 42