Forecasting journey time distribution with consideration to abnormal traffic conditions

被引:41
作者
Zhong, R. X. [1 ]
Luo, J. C. [1 ]
Cai, H. X. [1 ]
Sumalee, A. [2 ,3 ]
Yuan, F. F. [5 ]
Chow, Andy H. F. [4 ]
机构
[1] Sun Yat Sen Univ, Sch Engn, Guangdong Key Lab Intelligent Transportat Syst, Guangzhou, Guangdong, Peoples R China
[2] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Hong Kong, Peoples R China
[3] King Mongkuts Inst Technol, Dept Civil Engn, Bangkok, Thailand
[4] City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
[5] China Mobile Ltd Grp Guangdong, Guangzhou, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Travel time prediction; Functional principal component analysis; Probabilistic nested delay operator; Parallel computing; Adaptiveness to traffic incident; VEHICLE REIDENTIFICATION; FUNCTIONAL DATA; PREDICTION; MODEL; UNCERTAINTIES; RELIABILITY; FREEWAYS; NETWORK; DEMAND;
D O I
10.1016/j.trc.2017.08.021
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:292 / 311
页数:20
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