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Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 7348705, 13 pages
Research Article

Urban Link Travel Time Estimation Based on Low Frequency Probe Vehicle Data

1College of Transportation, Jilin University, Changchun 130025, China
2State Key Laboratory of Automobile Simulation and Control, College of Transportation, Jilin University, Changchun 130025, China
3Jilin Province Key Laboratory of Road Traffic, College of Transportation, Jilin University, Changchun 130025, China
4Shandong High-Speed Group Co., Ltd., Jinan 250000, China

Received 31 August 2015; Accepted 2 November 2015

Academic Editor: Filippo Cacace

Copyright © 2016 Xiyang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To improve the accuracy and robustness of urban link travel time estimation with limited resources, this research developed a methodology to estimate the urban link travel time using low frequency GPS probe vehicle data. First, focusing on the case without reporting points for the GPS probe vehicle on the target link in the current estimation time window, a virtual report point creation model based on the -Nearest Neighbour Rule was proposed. Then an improved back propagation neural network model was used to estimate the link travel time. The proposed method was applied to a case study based on an arterial road in Changchun, China: comparisons with the traditional artificial neural network method and the spatiotemporal moving average method revealed that the proposed method offered a higher estimation accuracy and better robustness.