{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:53:47Z","timestamp":1778356427183,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41871320"],"award-info":[{"award-number":["41871320"]}]},{"name":"National Natural Science Foundation of China","award":["2021JJ30276"],"award-info":[{"award-number":["2021JJ30276"]}]},{"name":"National Natural Science Foundation of China","award":["22A0341"],"award-info":[{"award-number":["22A0341"]}]},{"name":"National Natural Science Foundation of China","award":["62262018"],"award-info":[{"award-number":["62262018"]}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["41871320"],"award-info":[{"award-number":["41871320"]}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["2021JJ30276"],"award-info":[{"award-number":["2021JJ30276"]}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["22A0341"],"award-info":[{"award-number":["22A0341"]}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["62262018"],"award-info":[{"award-number":["62262018"]}]},{"name":"the Key Project of Hunan Provincial Education Department","award":["41871320"],"award-info":[{"award-number":["41871320"]}]},{"name":"the Key Project of Hunan Provincial Education Department","award":["2021JJ30276"],"award-info":[{"award-number":["2021JJ30276"]}]},{"name":"the Key Project of Hunan Provincial Education Department","award":["22A0341"],"award-info":[{"award-number":["22A0341"]}]},{"name":"the Key Project of Hunan Provincial Education Department","award":["62262018"],"award-info":[{"award-number":["62262018"]}]},{"name":"National Natural Science Foundation of China","award":["41871320"],"award-info":[{"award-number":["41871320"]}]},{"name":"National Natural Science Foundation of China","award":["2021JJ30276"],"award-info":[{"award-number":["2021JJ30276"]}]},{"name":"National Natural Science Foundation of China","award":["22A0341"],"award-info":[{"award-number":["22A0341"]}]},{"name":"National Natural Science Foundation of China","award":["62262018"],"award-info":[{"award-number":["62262018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functional areas are of great significance for detecting urban traffic flow directions and traffic congestion causes, as well as helping commuters plan routes in advance. Existing methods based on ride-hailing trajectories are relatively effective solution schemes, but they often lack in-depth analyses on time and space. In the paper, to explore the rules and trends of traffic flow among functional areas, a new spatiotemporal characteristics analysis and forecast method of traffic flow among functional areas based on urban ride-hailing trajectories is proposed. Firstly, a city is divided into areas based on the actual urban road topology, and all functional areas are generated by using areas of interest (AOI); then, according to the proximity and periodicity of inter-area traffic flow data, the periodic sequence and the adjacent sequence are established, and the topological structure is learned through graph convolutional neural (GCN) networks to extract the spatial correlation of traffic flow among functional areas. Furthermore, we propose an attention-based gated graph convolutional network (AG-GCN) forecast method, which is used to extract the temporal features of traffic flow among functional areas and make predictions. In the experiment, the proposed method is verified by using real urban traffic flow data. The results show that the method can not only mine the traffic flow characteristics among functional areas under different time periods, directions, and distances, but also forecast the spatiotemporal change trend of traffic flow among functional areas in a multi-step manner, and the accuracy of the forecasting results is higher than that of common benchmark methods, reaching 96.82%.<\/jats:p>","DOI":"10.3390\/ijgi12040144","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T01:41:34Z","timestamp":1679967694000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Analysis and Forecast of Traffic Flow between Urban Functional Areas Based on Ride-Hailing Trajectories"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9720-7910","authenticated-orcid":false,"given":"Zhuhua","family":"Liao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China"},{"name":"Metaverse Innovation Research Institute, Hunan University of Science and Technology, Xiangtan 411100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haokai","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China"},{"name":"Metaverse Innovation Research Institute, Hunan University of Science and Technology, Xiangtan 411100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China"},{"name":"Metaverse Innovation Research Institute, Hunan University of Science and Technology, Xiangtan 411100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China"},{"name":"Metaverse Innovation Research Institute, Hunan University of Science and Technology, Xiangtan 411100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","article-title":"Short-term traffic forecasting: Where we are and where we\u2019re going","volume":"43","author":"Vlahogianni","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liao, Z., Xiao, H., Liu, S., Liu, Y., and Yi, A. (2021). Impact Assessing of Traffic Lights via GPS Vehicle Trajectories. ISPRS Int. J. Geo. Inf., 10.","DOI":"10.3390\/ijgi10110769"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cai, L., Sha, C., He, J., and Yao, S. (2023). Spatial\u2013Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division. ISPRS Int. J. Geo. Inf., 12.","DOI":"10.3390\/ijgi12010013"},{"key":"ref_4","first-page":"1","article-title":"Survey of network traffic forecast based on deep learning","volume":"7","author":"Kang","year":"2015","journal-title":"Comput. Eng. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Almatar, K.M. (2022). Transit-Oriented Development in Saudi Arabia: Riyadh as a Case Study. Sustainability, 14.","DOI":"10.3390\/su142316129"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wei, X., Liu, Y., and Liao, Z. (2022). A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology Concepts. Appl. Sci., 12.","DOI":"10.3390\/app122211363"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, Y., Qing, R., Zhao, Y., and Liao, Z. (2022). Road Intersection Recognition via Combining Classification Model and Clustering Algorithm Based on GPS Data. ISPRS Int. J. Geo. Inf., 11.","DOI":"10.3390\/ijgi11090487"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12544-015-0170-8","article-title":"Short-term traffic flow prediction using seasonal ARIMA model with limited input data","volume":"7","author":"Kumar","year":"2015","journal-title":"European Transp. Res. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","article-title":"Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results","volume":"129","author":"Williams","year":"2003","journal-title":"J. Transp. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0968-090X(97)82903-8","article-title":"Combining Kohonen maps with ARIMA time series models to forecast traffic flow","volume":"4","author":"Dougherty","year":"1996","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3141\/1857-09","article-title":"Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches","volume":"1857","author":"Kamarianakis","year":"2003","journal-title":"Transp. Res. Rec."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xia, Y., and Chen, J. (2017, January 24\u201325). Traffic flow forecasting method based on gradient boosting decision tree. Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017), Taiyuan, China.","DOI":"10.2991\/fmsmt-17.2017.87"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1007\/s00128-015-1521-9","article-title":"The influence of sample drying procedures on mercury concentrations analyzed in soils","volume":"94","author":"Rohovec","year":"2015","journal-title":"Bull. Environ. Contam. Toxicol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/LSP.2009.2016451","article-title":"Data Imputation Using Least Squares Support Vector Machines in Urban Arterial Streets","volume":"16","author":"Yang","year":"2009","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.trc.2012.08.004","article-title":"Short-term traffic speed forecasting hybrid model based on Chaos\u2013Wavelet Analysis-Support Vector Machine theory","volume":"27","author":"Wang","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nadarajan, J., and Sivanraj, R. (2022). Attention-Based Multiscale Spatiotemporal Network for Traffic Forecast with Fusion of External Factors. ISPRS Int. J. Geo. Inf., 11.","DOI":"10.3390\/ijgi11120619"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Feng, F., Zou, Z., Liu, C., Zhou, Q., and Liu, C. (2023). Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model. Sustainability, 15.","DOI":"10.3390\/su15043296"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vinayakumar, R., Soman, K.P., and Poornachandran, P. (2017, January 13\u201316). Applying deep learning approaches for network traffic prediction. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Manipal, India.","DOI":"10.1109\/ICACCI.2017.8126198"},{"key":"ref_20","unstructured":"Sutskever, I., Vinyals, O., and Le, Q.V. (2014). Sequence to Sequence Learning with Neural Networks, NIPS, MIT Press."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., and Wang, Y. (2017). Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, 17.","DOI":"10.3390\/s17040818"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zheng, H., Ding, X., Wang, Y., and Zhao, C. (2021, January 16\u201318). Attention Based Spatial-Temporal Graph Convolutional Networks forRSU Communication Load Forecasting. Proceedings of the International Conference on Collaborative Computing: Networking, Applications and Worksharing, 17th EAI International Conference, CollaborateCom 2021, Virtual Event.","DOI":"10.1007\/978-3-030-92635-9_7"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Diao, Z., Wang, X., Zhang, D., Liu, Y., Xie, K., and He, S. (2019, January 29\u201331). Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. Proceedings of the National Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, HI, USA.","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","article-title":"A hybrid deep learning based traffic flow prediction method and its understanding","volume":"90","author":"Wu","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_26","unstructured":"Ahmed, M.S., and Cook, A.R. (1979). Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Technique, Transportation Research Board."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_28","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/4\/144\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:04:27Z","timestamp":1760123067000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/4\/144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,28]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["ijgi12040144"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12040144","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,28]]}}}