Map matching on low sampling rate trajectories through deep inverse reinforcement learning and multi-intention modeling

Analyzing freight vehicle movements using GPS trajectory data presents challenges due to environmental conditions and hardware limitations impacting data accuracy. Map matching, the process of aligning GPS signals with road networks, facilitates accurate route reconstruction. However, existing methods have limitations, particularly with low and ultra-low sampling rates. They often assume the shortest path between points, overlook historical data insights and neglect diverse driving behaviors, which may not align with real-world scenarios where shortest paths are not always optimal and different drivers exhibit varied behaviors. These limitations affect existing methods' reliability, especially when we face low sampling rate trajectories. In this study, we propose multi-intention deep inverse reinforcement learning for map matching (MIDIRL) to address these challenges. MIDIRL integrates deep neural networks and multi-intention capturing mechanisms with inverse reinforcement learning to model complex driving preferences from historical trajectories, improving map matching accuracy, especially in ultra-low-frequency trajectories. Our experiments on real-world datasets demonstrate MIDIRL's improved accuracy and efficiency of map matching compared to previous methods, even with limited training data.