Enhanced HMM Map Matching Model Based on Multiple Type Trajectories

Abstract

Map matching (MM) aims to align GPS trajectory with the actual roads on a map that vehicles pass through, essential for applications like trajectory search and route planning. The Hidden Markov Model (HMM) is commonly employed for online MM due to its interpretability and suitability for low GPS sampling rates. However, in complex urban areas with notable GPS drift, existing HMM methods face efficiency and accuracy challenges due to the use of a uniform road search radius and imprecise real-time road condition understanding. This paper proposes an improved HMM method using multiple trajectory types based on the following key ideas: Vehicle trajectories can be divided into two types: fixed trajectories (e.g., bus) and free trajectories (taxis, private cars). The relatively accurate information of fixed trajectories can help us more accurately measure the error distribution, as well as accurate road conditions. The novelty of our approach lies in the following aspects: i) Using fixed bus trajectories to estimate region-specific GPS error distribution, optimizing observation probabilities and reducing candidate road search costs. ii) Utilizing real-time fixed trajectories for accurate, real-time road state estimation, enhancing dynamic state transition probabilities in HMM. Empirical analysis, based on real bus and taxi trajectories in Shenzhen over half a year, demonstrate that our method outperforms existing methods in terms of map matching efficiency and accuracy.