Analytical Review of Map Matching Algorithms: Analyzing the Performance and Efficiency Using Road Dataset of the Indian Subcontinent

Publication Name

Archives of Computational Methods in Engineering


Precise position information of moving entities on digital road networks is a vital requirement of location-based applications. Location information received from Global Positioning System has some positional error and this inaccurate information generates errors in further processing of navigation and location-based applications. Map matching algorithms are responsible for the prediction of precise location by considering different parameters of the device. Many map matching algorithms were developed by the research community so as to improve performance and accuracy. These algorithms are categorized into different categories. This paper briefly explains the category-wise working of map matching algorithms and also provides analytically reviews of the performance of these algorithms. Five different algorithms from each category were considered in this experiment. The performance of five basic map matching algorithms was further evaluated on the digital road network of the Indian subcontinent. Six separate routes ranging in length from 0.2 to 55 km were used to analyze the efficiency of considered algorithms. This analytical review provides a performance and accuracy comparison of point to point, topological, Kalman filter-based, Hidden Markov Model-based, and Frechet distance-based map matching algorithms. This review concludes that for online map matching, Hidden Markov model-based map matching algorithm provides good accuracy in comparison to other considered algorithms.

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