Sparse Link Travel Time Estimation Using Big Data of Floating Car

ZHANG Faming; ZHU Xinyan; GUO Wei; HU Tao

doi:10.13203/j.whugis20150425

Volume 42 Issue 1

Jan. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(1): 56-62. > DOI: 10.13203/j.whugis20150425

ZHANG Faming, ZHU Xinyan, GUO Wei, HU Tao. Sparse Link Travel Time Estimation Using Big Data of Floating Car[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 56-62. DOI: 10.13203/j.whugis20150425

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Sparse Link Travel Time Estimation Using Big Data of Floating Car

ZHANG Faming^1,,
ZHU Xinyan^{1, 2},
GUO Wei^{1, 2, ,},
HU Tao^{1, 2}

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China

Funds:

The National 863 Program of China 2013AA122301

National Science and Technology Support Program 2012BAH35B03

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Received Date: January 20, 2016
Published Date: January 04, 2017

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Abstract

Abstract

Although there exists quantities of GPS data of floating car, partial links lack real data during some certain period of time. Therefore, we can't estimate target link travel time. Considering the problem of sparse data when using floating car data estimating link travel time, we put forward a kind of inferred method based on big data of floating car. We designed a three-layer artificial neural network model, whose input information and output information are the feature relationship and the travel time ratio between target link and adjacent link respectively. We obtained traffic spatiotemporal association relationship using historical big data of floating car and then inferred link travel time. The model was verified by historical big data of Wuhan's floating car from March to July, 2014 and the MAPE of estimated value of link travel time is less than 25% which proved the effectiveness of the proposed method.

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