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Short‐Term Traffic Speed Prediction for an Urban Corridor
Author(s) -
Yao Baozhen,
Chen Chao,
Cao Qingda,
Jin Lu,
Zhang Mingheng,
Zhu Hanbing,
Yu Bin
Publication year - 2017
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12221
Subject(s) - computer science , taxis , term (time) , artificial neural network , support vector machine , intelligent transportation system , global positioning system , floating car data , data mining , simulation , real time computing , artificial intelligence , traffic congestion , engineering , transport engineering , telecommunications , physics , quantum mechanics
Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.