Improving Map Matching of Floating Car Data with Artificial Intelligence Techniques

Abstract

Map matching is a crucial data processing task for transferring measurements from the dynamic sensor location to the relevant road segment. It is especially important when estimating road network speed by using probe vehicles (floating car data) as speed measurement sensors. Most common approaches rely on finding the closet road segment, but road network geometry (e.g., dense areas, two-way streets, and superposition of road segments due to different heights) and inaccuracy in the GNSS location (up to decades of meters in urban areas) can wrongly allocate up to 30% of the measurements. More advanced methods rely on taking the topology of the network into account, significantly improving the accuracy at a higher computational cost, especially when the accuracy of the GNSS location is low. In order to both improve the accuracy of the “closet road segment” methods and reduce the processing time of the topology-based methods, the data can be pre-processed using AI techniques to reduce noise created by the inaccuracy of the GNSS location and improve the overall accuracy of the map-matching task. This paper applies AI to correct GNSS locations and improve the map-matching results, achieving a matching accuracy of 76%. The proposed methodology is demonstrated to the floating car data generated by a fleet of 1200 taxi vehicles in Thessaloniki used to estimate road network speed in real time for information services and for supporting traffic management in the city.

Keywords

• map matching
• GNSS trajectory
• floating car data
• artificial intelligence
• deep neural networks