Urban Traffic Travel Time Short-Term Prediction Model Based onSpatio-Temporal Feature Extraction

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dc.contributor.authorKang, Leilei
dc.contributor.authorHu, Guojing
dc.contributor.authorHuang, Hao
dc.contributor.authorLu, Weike
dc.contributor.authorLiu, Lan
dc.date.accessioned2025-11-24T19:55:50Z
dc.date.available2025-11-24T19:55:50Z
dc.date.issued2020
dc.descriptionOpen Access Article
dc.description.abstractIn order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. *e dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. *rough the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. *e results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.
dc.description.sponsorshipNational Natural Science Foundation of China. Grant Number: 61873216
dc.format.mimetypeapplication/pdf
dc.identifier.citationKang, Leilei, Hu, Guojing, Huang, Hao, Lu, Weike, Liu, Lan, Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction, Journal of Advanced Transportation, 2020, 3247847, 16 pages, 2020. https://doi.org/10.1155/2020/3247847
dc.identifier.doi0.1155/2020/3247847
dc.identifier.issn2042-3195
dc.identifier.urihttps://ir.ua.edu/handle/123456789/17430
dc.language.isoen_US
dc.rightsCopyright © 2020 Leilei Kang et al.
dc.rights.licenseCC BY 4.0
dc.titleUrban Traffic Travel Time Short-Term Prediction Model Based onSpatio-Temporal Feature Extraction
dc.typeArticle
dc.typetext
dcterms.licenseThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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