Travel Time Prediction: Comparison of Machine Learning Algorithms in a Case Study

被引：8

作者：

Goudarzi, Forough ^{[1
]}

机构：

[1] Brunel Univ London, Coll Engn Design & Phys Sci, London, England

来源：

IEEE 20TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS / IEEE 16TH INTERNATIONAL CONFERENCE ON SMART CITY / IEEE 4TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS) | 2018年

关键词：

D O I：

10.1109/HPCC/SmartCity/DSS.2018.00232

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Travel time prediction has important applications within the field of intelligent transportation, such as vehicle routing, congestion and traffic management. A challenging task in travel time prediction is obtaining data that is not readily available, as a clear majority of links in roads network are not equipped with traffic sensors. In this paper, data of travel time is collected for a link using Google Maps Application Programming Interface (API). Then, travel times are predicted for short horizons of up to one hour on the link by applying machine learning algorithms. The Mean Absolute Error (MAE) of predictions are compared. The study indicates that a shallow Artificial Neural Network (ANN) can provide more accurate prediction than the other algorithms.

引用

页码：1404 / 1407

页数：4

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