Incorporating uncertainty into short-term travel time predictions

被引:58
|
作者
Li, Ruimin [1 ]
Rose, Geoffrey [2 ]
机构
[1] Dept Transportat, Bur Transport Stat, Sydney, NSW 2000, Australia
[2] Monash Univ, Dept Civil Engn, Inst Transport Studies, Clayton, Vic 3800, Australia
关键词
Travel time; Variability; Short term; Prediction; NEURAL-NETWORKS; MODELS; RELIABILITY;
D O I
10.1016/j.trc.2011.05.014
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
To improve the quality of travel time information provided to motorists, there is a need to move away from point forecasts of travel time. Specifically, techniques are needed which predict the range of travel times which motorists may experience. This paper focuses on travel time prediction on motorways and evaluates three models for predicting the travel time range in real time as well as up to 1 h ahead. The first model, termed lane by lane tracing, relies on speed data from each lane to replicate the trajectories of relatively slow and relatively fast vehicles on the basis of speed differences across the lanes. The second model is based on the relationship between mean travel time (estimated using a neural network model) and driver-to-driver travel time variability. The results provide insight into the relative merits of the proposed techniques and confirm that they provide a basis for reliable travel time range prediction in the short-term prediction context (up to 1 h ahead). (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1006 / 1018
页数:13
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