{'fraction_non_alphanumeric': 0.020714285714285713, 'fraction_numerical': 0.0, 'mean_word_length': 5.486111111111111, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Using recursive Bayesian estimation for matching GPS measurements to imperfect road network data

Map-matching refers to the process of projecting positioning measurements to a location on a digital road network map. It is an important element of intelligent transportation systems (ITS) focusing on driver assistance applications, on emergency and incident management, arterial and freeway management, and other applications. This paper addresses the problem of map-matching in the applications characterized by imperfect map quality and restricted computational resources - e.g. in the context of community-based ITS applications. Whereas a number of map-matching methods are available, often these methods rely on topological analysis, thereby making them sensitive to the map inaccuracies. In the paper, a new map-matching method based on the probabilistic approach is introduced. In the method, the probabilities of alternative road links are estimated with recursive Bayesian estimation, and the road link is identified using maximum a posteriori probability principle. The topological analysis is not used; instead, the distance between projections on road links is used to assign road link switching probability. The accuracy of the method is empirically evaluated, and the link identification accuracy is found to be similar to that of alternative approaches relying on topological analysis.