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

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

Tongyu Zhu, Zhixin Song, Dongdong Wu, Jianjun Yu
Copyright: © 2016 |Volume: 13 |Issue: 1 |Pages: 19
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466689046|DOI: 10.4018/IJWSR.2016010105
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MLA

Zhu, Tongyu, et al. "A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data." IJWSR vol.13, no.1 2016: pp.69-87. http://doi.org/10.4018/IJWSR.2016010105

APA

Zhu, T., Song, Z., Wu, D., & Yu, J. (2016). A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data. International Journal of Web Services Research (IJWSR), 13(1), 69-87. http://doi.org/10.4018/IJWSR.2016010105

Chicago

Zhu, Tongyu, et al. "A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data," International Journal of Web Services Research (IJWSR) 13, no.1: 69-87. http://doi.org/10.4018/IJWSR.2016010105

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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.

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