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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bouju, A., Stockus, A., Bertrand, F., Boursier, P.: Location-based spatial data management in navigation systems. In: Intelligent Vehicle Symposium 2002, vol. 1, pp. 172–177. IEEE (2002)
Greenfeld, J.S.: Matching GPS observations to locations on a digital map. In: Transportation Research Board 81st Annual Meeting (2002)
Hunter, T., Abbeel, P., Bayen, A.M.: The path inference filter: model-based low-latency map matching of probe vehicle data. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds.) Algorithmic Foundations of Robotics X. STAR, vol. 86, pp. 591–607. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36279-8_36
Iwaki, F., Kakihara, M., Sasaki, M.: Recognition of vehicle’s location for navigation. In: Conference Record of Papers Presented at the First Vehicle Navigation and Information Systems Conference (VNIS 1989), pp. 131–138. IEEE (1989)
Miler, M., Todić, F., Ševrović, M.: Extracting accurate location information from a highly inaccurate traffic accident dataset: a methodology based on a string matching technique. Transp. Res. Part C Emerg. Technol. 68, 185–193 (2016)
Mohamed, R., Aly, H., Youssef, M.: Accurate real-time map matching for challenging environments. IEEE Trans. Intell. Transp. Syst. 18(4), 847–857 (2017)
Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 336–343. ACM (2009)
Ptošek, V., Rapant, L., Martinovič, J.: Floating car data map-matching utilizing the Dijkstra’s algorithm. In: Sharma, N., Chakrabarti, A., Balas, V.E. (eds.) Data Management, Analytics and Innovation. AISC, vol. 1016, pp. 115–130. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9364-8_9
Sharath, M., Velaga, N.R., Quddus, M.A.: A dynamic two-dimensional (D2D) weight-based map-matching algorithm. Transp. Res. Part C Emerg. Technol. 98, 409–432 (2019)
Singh, S., Singh, J., Sehra, S.S.: Genetic-inspired map matching algorithm for real-time GPS trajectories. Arab. J. Sci. Eng. 45(4), 2587–2603 (2020)
Yang, C., Gidofalvi, G.: Fast map matching, an algorithm integrating hidden Markov model with precomputation. Int. J. Geogr. Inf. Sci. 32(3), 547–570 (2018)
Zhao, F., Shin, J., Reich, J.: Information-driven dynamic sensor collaboration. IEEE Signal Process. Mag. 19(2), 61–72 (2002)
Zhao, L., Ochieng, W.Y., Quddus, M.A., Noland, R.B.: An extended Kalman filter algorithm for integrating GPS and low cost dead reckoning system data for vehicle performance and emissions monitoring. J. Navig. 56(2), 257–275 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-66665-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66664-4
Online ISBN: 978-3-030-66665-1
eBook Packages: Computer ScienceComputer Science (R0)