A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data

Tongyu Zhu, Zhixin Song, Dongdong Wu and Jianjun Yu
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Tongyu Zhu: State Key Lab of Software Development Environment, Beihang University, Beijing, China
Zhixin Song: State Key Lab of Software Development Environment, Beihang University, Beijing, China
Dongdong Wu: Beijing Transportation Information Center, Beijing, China
Jianjun Yu: Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

International Journal of Web Services Research (IJWSR), 2016, vol. 13, issue 1, 69-87

Abstract: With the growing popularity of cell phones, using massive cellular signaling data as probe to track the vehicles movement trajectory and obtain the real-time traffic condition has become one of the most attractive candidate techniques. However, traditional approaches may offer a poor performance in removing noisy data and minimizing deviation of traffic speed in adjacent time intervals. In this paper, a novel approach is proposed to solve these two issues. The authors move noisy data by comparing the cellular signaling data with the trained data set, and adopt a modified Kalman filter algorithm to minimize the deviations. The experiment results show that the accuracy of the approach performs better in comparison to other two traffic speed estimation approaches.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:13:y:2016:i:1:p:69-87

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