Robust real-time route inference from sparse vehicle position data

The ability to correctly infer the route traveled by vehicles in real time from infrequent, noisy observations of their position is useful for several traffic management applications. This task, known as map matching, is efficiently performed through probabilistic inference on a Hidden Markov Model that represents the candidate vehicle states and the transitions between them. In this paper, we present new methods for improving the accuracy and timeliness of existing solutions. We propose assigning the transition probability between a pair of candidate vehicle states by considering the alternative paths present in the context. A discrete route choice model is used to estimate the probability that a driver would choose the path under consideration over the best alternative available. In order to facilitate real-time operation, we present a simple yet effective heuristic to reduce the output latency of the route-inference algorithm with negligible loss of accuracy. Tests conducted with ground truth GPS data from a dense urban region in Singapore show that the proposed techniques outperform the conventional baseline approach.