Abstract
The floating car data contain abundant traffic information for many transportation applications. Traditional map matching algorithms are useful to handle the high frequency navigation global positioning system (GPS) data, but for low frequency floating car data they always play a poor role. This paper gives a stochastic map matching method for low frequency floating car data. First, the authors figure out the differences between the floating car data and the traditional GPS data. Also the authors analyze the statistical properties of the floating car data. Then the authors give out a new map matching method. This method uses the stochastic theory to select support GPS points for each unmatched GPS point. And this method uses an algorithm based on the Hidden Markov Model to judge the probability of each candidate link. At last, with some actual floating car data the authors prove that this model is useful and its computing speed meets the requirement for online matching.
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