Journal Article

A Spatiotemporal Multi-Model Ensemble Framework for Urban Multimodal Traffic Flow Prediction

Wang, Zhenkai, Hu, Lujin

2025

Overview

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’s 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.

• Publisher: MDPI AG
• Subject: Accuracy / Artificial neural networks / Collaboration / Deep learning / Intelligent transportation systems / Long short-term memory / Machine learning / Methods / multi-model ensemble / multimodal travel trajectory / Neural networks / Time series / Traffic flow / traffic flow prediction / Traffic management / trajectory interaction / Transportation networks / Travel modes / Travel patterns