相关性(法律)
领域(数学)
数据科学
计算机科学
大数据
智能交通系统
技术预测
运输工程
开放式研究
集合(抽象数据类型)
工作(物理)
先进的交通管理系统
交通拥挤
运筹学
管理科学
流量(计算机网络)
交通工程
作者
Ibai Laña,Nikola Kasabov,Manuel Velez,George Yannis
出处
期刊:IEEE Intelligent Transportation Systems Magazine
[Institute of Electrical and Electronics Engineers]
日期:2018-04-23
卷期号:10 (2): 93-109
被引量:121
标识
DOI:10.1109/mits.2018.2806634
摘要
Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.
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