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.
Reference57 articles.
1. T-Drive: Enhancing Driving Directions with Taxi Drivers’ Intelligence;Yuan;IEEE Trans. Knowl. Data Eng.,2013
2. Stenneth, L., Wolfson, O., Yu, P.S., and Xu, B. Transportation mode detection using mobile phones and GIS information. Proceedings of the 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems.
3. Fleet Management for Vehicle Sharing Operations;Nair;Transp. Sci.,2011
4. Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach;Rahmani;Transp. Res. Part C Emerg. Technol.,2017
5. A dynamic two-dimensional (D2D) weight-based map-matching algorithm;Sharath;Transp. Res. Part C Emerg. Technol.,2019
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