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.
Highlights
Estimating and predicting travel times is challenging because of the intrinsic uncertainty of travel on congested urban road networks and uncertainty stemming from the collection of data with probe vehicles equipped with GPS
We fitted the spatiotemporal gradient-boosted regression tree (STGBRT) using different numbers of trees (1–5000) and various learning rates (0.01–1) to training data reflecting probe vehicle spatiotemporal characteristics extracted from the urban road network
To evaluate the performance of an Spatiotemporal Gradient–boosted regression tree (STGBRT) model that combines various parameters, we introduced the mean absolute percentage error (MAPE) as an indicator
Summary
Chung [26] applied a gradient regression tree to study crash occurrences These latter two studies utilized a boosting algorithm to address classification and prediction problems, rather than travel time prediction. Yanru Zhang [27] utilized a gradient boosting method to improve travel time prediction considering real travel time but ignored information from historical travel time data and the spatiotemporal correlation between target and adjacent links. This approach cannot efficiently predict link travel time under sparse data conditions. A discussion of the results and some conclusions are outlined at the end
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
