Abstract
Floating car data (FCD) are one of the most important sources of traffic data. However, their processing requires several steps that may seem trivial but have far-reaching consequences. One such step is map-matching, i.e. assignment of the FCD measurement to the correct road segment. While it can be done very simply by assigning the point of measurement to the closest road, this approach may produce a highly undesirable level of error. The second challenge connected with processing of FCD measurements is missing measurements. They are usually caused by the shortcomings of GPS technology (e.g. the satellites can be obscured by buildings or bridges) and may deny us many measurements during longer downtimes. The last problem we will solve is the assignment of measurements to very short segments. FCD measurements are taken in periodic steps for several seconds long. However, some road segments are very short and can be passed by a car in the shorter interval. Such segments are therefore very difficult to monitor. We plan to solve all these problems through a combination of geometric map-matching with the Dijkstra’s shortest path algorithm.
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Acknowledgements
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science - LQ1602”. This work has been partially funded by ANTAREX, a project supported by the EU H2020 FET-HPC programme under grant 671623.
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Ptošek, V., Rapant, L., Martinovič, J. (2020). Floating Car Data Map-Matching Utilizing the Dijkstra’s Algorithm. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-13-9364-8_9
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