Abstract
Trajectory data of floating cars form an important data source for the studies on transportation, while map matching is always one essential step for them. Most map matching algorithms perform better with trajectory data of high sampling rates than with those of low sampling rates, but the latter can be commonly accessed because of their low cost. In this article, we proposed a map matching algorithm based on then hidden Markov Model. In this algorithm, we concerned both position and direction information for calculating observation and transition probabilities and solved the labelling problem with the Viterbi algorithm by maximizing the state sequence probabilities. We carried out a case study with the GPS trajectory data of floating cars and road network data of Wuhan. The results show that this algorithm can effectively match trajectory data of low sampling rates with the road network with good topology, and the correct rate can reach up to 86% within an acceptable time cost. In particular, it performs well even in some error-prone scenarios, such as two-way multiple parallel lanes, intersections, overpasses and roundabouts. Furthermore, we also discussed factors that might affect the accuracy and efficiency of this algorithm, particularly investigating the effect of topology correctness of the road network.
Highlights
With the continuous development of intelligent transport systems (ITSs) and smart cities, taxis have been commonly equipped with GPS receivers to locate them in real time and are known as floating cars
The R C is 86.927%, and its variance is 0.0166. This is an acceptable accuracy for an Hidden Markov model (HMM)-based map matching algorithm with a low sampling rate
WORK In this study, we proposed a map matching algorithm based on an HMM for floating car data (FCD) of low sampling rates
Summary
With the continuous development of intelligent transport systems (ITSs) and smart cities, taxis have been commonly equipped with GPS receivers to locate them in real time and are known as floating cars. In this sense, floating car data (FCD) are composed of massive historical trajectories of taxis in a city. Errors of GPS devices, saying 5-10 meters, are inherently present, in an urban environment distributed with tall buildings This could largely result in mismatches between the FCD and the corresponding road network, i.e., cars seem to be running off the roads instead of on them. We need to process FCD to match the trajectories with the corresponding road segments, which is so-called ‘‘Map Matching’’ procedure
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