Advancing Map-Matching and Route Prediction: Challenges, Methods, and Unified Solutions

Map-matching involves aligning raw positioning data with actual road networks. It is a complex process due to measurement inaccuracies, ambiguous street layouts, and sensor noise. The paper explores the challenges in map-matching and vehicle route prediction and presents an overview of existing methods and algorithms. The solutions employing hidden Markov models (HMMs), where emission and transition probabilities are crucial in correctly matching positions to roads, are examined and evaluated. Machine Learning (ML) offers robust algorithms capable of managing complex urban environments and varied data sources. While HMMs have demonstrated their efficacy in capturing sequential dependencies, more advanced ML techniques, including deep learning, provide enhanced capabilities for learning spatial and temporal relationships. They improve prediction accuracy and adapt to evolving traffic conditions and diverse vehicle behaviours. Special attention is paid to a holistic solution, assuming a combination of map-matching and route prediction within a unified framework. It fosters more efficient route planning, real-time traffic management, and overall decision-making in intelligent transportation systems.