Short-term traffic forecasting: Where we are and where we're going

被引:829
作者
Vlahogianni, Eleni I. [1 ]
Karlaftis, Matthew G. [1 ]
Golias, John C. [1 ]
机构
[1] Natl Tech Univ Athens, Sch Civil Engn, Dept Transportat Planning & Engn, GR-15773 Athens, Greece
关键词
Short-term traffic; Prediction models; Intelligent Transportation Systems; Responsive algorithms; Time series analysis; Computational intelligence; TRAVEL-TIME PREDICTION; INTELLIGENT TRANSPORTATION SYSTEMS; SPACE NEURAL-NETWORKS; FLOW PREDICTION; AGGREGATION LEVEL; MODEL; SERIES; INFORMATION; VOLUME; IDENTIFICATION;
D O I
10.1016/j.trc.2014.01.005
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3 / 19
页数:17
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