Abstract
Locating the position of the device on the road network is a crucial component of a location-based system. The performance of location-based systems is highly affected by the mapping of user location on the digital map. Many spatial computing methods were developed by the research community to map the GPS fix on to the digital map. These mapping methods take GPS data and spatial data as input. The data used by these spatial computing algorithms is in huge amounts and is recorded using sensors and GPS receivers. While handling GPS data, these algorithms face many issues and based upon that each method has its advantages and disadvantages. This paper provides working of a few prominent methods. As per the research trends, four methods are considered and empirical evaluation and analysis are presented. To analyze the performance of these methods a standard data-set is used. 3 different routes having nodes in the range of 100 to 12598 are considered for this experiment. In this experiment mapping, results are analyzed using GPS trajectories of commutative distance 154.2 km. Performance and accuracy of considered map matching algorithms were analyzed on a total of 16804 GPS points. Results are analyzed using RMSE, accuracy ratio, and execution time. HMM-based map matching algorithm is considered as the most preferred algorithm having 94% accuracy with average execution time.
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Singh, S., Singh, J. (2020). Analysis of GPS Trajectories Mapping on Shape Files Using Spatial Computing Approaches. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds) Big Data Analytics. BDA 2020. Lecture Notes in Computer Science(), vol 12581. Springer, Cham. https://doi.org/10.1007/978-3-030-66665-1_7
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