{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:41:06Z","timestamp":1781242866553,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,10]],"date-time":"2025-08-10T00:00:00Z","timestamp":1754784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing University of Civil Engineering and Architecture\u2019s young teachers research ability enhancement program","award":["x21023"],"award-info":[{"award-number":["x21023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these diverse trajectory characteristics, we propose a spatiotemporal multi-model ensemble framework, which is an ensemble model called GLEN (GCN and LSTM Ensemble Network). Firstly, the trajectory feature adaptive driven model selection mechanism classifies trajectories into dynamic travel and fixed-route scenarios. Secondly, we use a Graph Convolutional Network (GCN) to capture dynamic travel patterns and Long Short-Term Memory (LSTM) network to model fixed-route patterns. Subsequently the outputs of these models are dynamically weighted, integrated, and fused over a spatiotemporal grid to produce accurate forecasts of urban total traffic flow at multiple future time steps. Finally, experimental validation using Beijing\u2019s Chaoyang district datasets demonstrates that our framework effectively captures spatiotemporal and interactive characteristics between multimodal travel trajectories and outperforms mainstream baselines, thereby offering robust support for urban traffic management and planning.<\/jats:p>","DOI":"10.3390\/ijgi14080308","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:04:53Z","timestamp":1754903093000},"page":"308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Spatiotemporal Multi-Model Ensemble Framework for Urban Multimodal Traffic Flow Prediction"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhenkai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lujin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5615","DOI":"10.1109\/TITS.2021.3055258","article-title":"Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method","volume":"23","author":"Ma","year":"2021","journal-title":"IEEE Trans. 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