Probabilistic map-matching for low-frequency gps trajectories

Abstract

The ability to infer routes taken by vehicles from sparse and noisy GPS data is of crucial importance in many traffic applications. The task, known as map-matching, can be accurately approached by a popular technique known as ST-Matching. The algorithm is computationally efficient and has been shown to outperform more traditional map-matching approaches, especially on low-frequency GPS data. The major drawback of the algorithm is a lack of confidence scores associated with its outputs, which are particularly useful when GPS data quality is low. In this paper, we propose a probabilistic adaptation of ST-Matching that equips it with the ability to express map-matching certainty using probabilities. The adaptation, called probabilistic ST-Matching (PST-Matching) is inspired by similarities between ST-Matching and probabilistic approaches to map-matching based on a Hidden Markov Model. We validate the proposed algorithm on GPS trajectories of varied quality and show that it is similar to ST-Matching in terms of accuracy and computational efficiency, yet with the added benefit of having a measure of confidence associated with its outputs.