Multi-Source Traffic State Estimation: Exploring Advanced Filtering Algorithms for Rural Arterial Networks

Traffic state estimation (TSE) is essential for enhancing transportation systems by providing critical, real-time data on road conditions to support decision-making and optimize network performance. Traditional TSE methods have predominantly focused on highways, relying on single-source data inputs like loop detectors or GPS data, which may limit adaptability in diverse traffic scenarios. However, the integration of multi-source data spanning loop detectors, GPS, and Bluetooth has opened new pathways for improved accuracy and responsiveness in TSE models, particularly within rural arterial networks and at complex intersections. This review analyzes the progression of TSE methodologies, focusing on model-based techniques such as the Kalman Filter (KF), Sliding Kalman Filter (SKF), and cell transmission models. By examining the combined use of varied data inputs, this review underscores the benefits of multi-source fusion in accurately capturing dynamic traffic conditions in rural settings. Key challenges, including non-linear traffic flows, inherent data noise, and the limitations of current validation methods, are discussed. Future research directions are identified, highlighting the need for adaptable algorithms that can effectively manage the complex, variable datasets characteristic of rural traffic environments.