The energy-saving low-frequency floating car data have attracted significantly increasing attention in traffic management, but the low-frequency GPS data have an adverse effects on map-matching in urban road networks. Therefore, this paper develops a new spatial-temporal path filter-based and route choice model aided map-matching (FCMM) algorithm that enhances the map-matching of low-frequency positioning data on an urban road map. We introduce filters based on spatial and temporal analyses, which consider real-time traffic status to eliminate unreasonable paths between adjacent candidate points, calculate the heuristic information, and establish the candidate graph. Considering the driver path preference, we further use the route choice model estimated from from real drive data to assess each candidate path. The algorithm was evaluated using ground truth data, and the results of the experiment show that the proposed FCMM algorithm outperforms the baseline methods in both effectiveness and efficiency under the condition of low sampling rates.

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