Travel Time Prediction Model for Urban Road Network Based on Multi-source Data

被引：8
|
作者：
Jiang, Zhou [1 ]
Zhang, Cunbao [1 ]
Xia, Yinxia [1 ]
机构：
[1] Wuhan Univ Technol, Intelligent Transport Syst Res Ctr, Wuhan 430063, Peoples R China
来源：
9TH INTERNATIONAL CONFERENCE ON TRAFFIC AND TRANSPORTATION STUDIES (ICTTS 2014) | 2014年 / 138卷
关键词：
multi-source data; travel time prediction; urban road network; Kalman filtering; Vissim simulation;
D O I：
10.1016/j.sbspro.2014.07.230
中图分类号：
U [交通运输];
学科分类号：
08 ; 0823 ;
摘要：
In view of the deficiencies of single data source for travel time prediction, multi-source data are used to improve the precision of travel time. Floating car and fixed detector are commonly used in traffic data collection, and they have certain complementarities in data types and accuracy. Therefore, the real-time traffic data of these two detectors are used as input parameters of prediction model, and Kalman filtering theory is used to establish travel time prediction model of urban road network. Finally, the model is simulated by Vissim 4.3 and the simulation results show that the average absolute relative error of travel time based on multi-source data is 5.18%, and it is increased by 13.4% comparing with fixed detector data and increased by 7.2% comparing with floating car data. (C) 2014 Elsevier Ltd.
引用
收藏
页码：811 / 818
页数：8
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