Reinforcement learning-based vehicle travel path reconstruction from sparse automatic licence plate recognition data

Abstract

Automatic licence plate recognition (ALPR) data is a vital source for acquiring large-scale vehicle trajectory data in urban transportation research. However, the sparse distribution of ALPR sensors often results in incomplete vehicle trajectories with unobserved travel paths between adjacent ALPR sensors. To address this challenge, this study proposes a novel travel path reconstruction model based on reinforcement learning. First, the maximum entropy inverse reinforcement learning (MaxEnt IRL) method is employed to learn vehicle path selection strategy, i.e. the reward function, from the real data. Then, a comprehensive reward function is formulated by integrating destination rewards. Finally, the Q-learning algorithm is utilized to reconstruct vehicle travel paths between adjacent ALPR sensors based on the comprehensive reward function. Evaluation on a randomly selected set of 100 pairs of adjacent ALPR sensors demonstrates that the proposed model significantly outperforms two baseline methods, achieving reductions of 24.3% and 22.5% in deviation from the real paths.

Keywords

• Vehicle travel path reconstruction
• reinforcement learning
• inverse reinforcement learning
• Q-learning
• automatic licence plate recognition data