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ISPRS Int. J. Geo-Inf. 2016, 5(11), 201; doi:10.3390/ijgi5110201

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

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
3
School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 9 August 2016 / Revised: 27 October 2016 / Accepted: 31 October 2016 / Published: 4 November 2016
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Abstract

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction. View Full-Text
Keywords: urban link travel time prediction; spatiotemporal correlations; spatiotemporal gradient–boosted regression tree model; big data urban link travel time prediction; spatiotemporal correlations; spatiotemporal gradient–boosted regression tree model; big data
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Zhang, F.; Zhu, X.; Hu, T.; Guo, W.; Chen, C.; Liu, L. Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations. ISPRS Int. J. Geo-Inf. 2016, 5, 201.

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