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This paper introduces a hybrid traffic forecasting model combining Deep Learning (DL) to capture nonlinear spatiotemporal dynamics from IoT sensor data and Agent-Based Modeling (ABM) to simulate individual traffic participant behaviors. The hybrid approach aims to improve forecasting accuracy and interpretability by integrating data-driven prediction with rule-based simulation. Experiments on real-world traffic data demonstrate that the proposed model outperforms traditional forecasting techniques in short-term accuracy and scenario-based flexibility.
By blending deep learning with agent-based modeling, this hybrid approach delivers more accurate and flexible urban traffic forecasts than traditional methods.
Urban traffic systems are becoming increasingly complex due to rapid urbanization and the dynamic nature of mobility patterns in smart cities. Accurate and adaptive forecasting of urban traffic is essential for effective traffic management and sustainable urban planning. This study proposes a hybrid modeling approach that integrates Deep Learning (DL) with Agent-Based Modeling (ABM) to enhance the accuracy and interpretability of traffic forecasting. The deep learning component leverages spatiotemporal data from IoT sensors and historical traffic records to capture nonlinear traffic dynamics, while the agent-based model simulates the behaviors and interactions of individual traffic participants under various scenarios. By combining data-driven prediction with rule-based simulation, the hybrid model can forecast traffic flows and adapt to changes in infrastructure, policy, or user behavior. Experimental evaluations using real-world traffic datasets from a major metropolitan area demonstrate that the proposed model outperforms traditional forecasting techniques in both short-term accuracy and scenario-based flexibility. This research contributes to the development of intelligent transportation systems and offers practical insights for city planners and traffic authorities.
