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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorSun, B-
dc.creatorSun, T-
dc.creatorJiao, P-
dc.publisherJohn Wiley & Sonsen_US
dc.rights© 2021 Bo Sun et al. This 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.en_US
dc.rightsThe following publication Bo Sun, Tuo Sun, Pengpeng Jiao, "Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost", Journal of Advanced Transportation, vol. 2021, Article ID 5559562, 24 pages, 2021 is available at
dc.titleSpatio-temporal segmented traffic flow prediction with ANPRS data based on improved XGBoosten_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractTraffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of advanced transportation, 2021, v. 2021, 5559562-
dcterms.isPartOfJournal of advanced transportation-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
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