Genetic-Inspired Map Matching Algorithm for Real-Time GPS Trajectories

Abstract

Complex road networks, inaccurate GPS receiver output, low sampling rate and many other associated issues pose real challenges for map matching process. Genetic algorithms have recently been trialed for rendering GPS fix on digital maps. This manuscript introduces an improvised genetic algorithm named as post-processing genetic-inspired map matching (GiMM) algorithm. The proposed GiMM intends to mitigate inherent challenges associated with originally proposed genetic algorithm for map matching. The fitness function used by GiMM makes use of Bucket Dijkstra’s and fast dynamic time wrapping (FDTW) algorithms to render GPS information on digital maps. Bucket Dijkstra’s suggests the shortest path available in between two points, and FDTW is responsible for comparing two data series. Unlike traditional genetic algorithm for map matching, GiMM was evaluated on sparse as well as dense GPS data. The performance of the GiMM algorithm was evaluated in real time using OpenStreetMap data and GPS dataset mapped onto a road network of 82 km. GiMM uses population size, generation count, accuracy and execution time as input parameters. A maximum accuracy of 99.4% with root-mean-square error 0.06 was observed, whereas a minimum of 60% accuracy was observed at 0.47 root-mean-square error. Number of iterations and population size were concluded to be the most influential parameters for the performance of genetic algorithms for map matching.