Implementation of generic algorithm in map-matching model

This paper proposed a novelty map-matching method based on multi-criteria genetic algorithm. In addition, the new ideas are introduced, such as incorporation of dynamic time wrapping method and dynamic programming technique. Dynamic time wrapping is used to evaluate the geometric similarity between recorded trajectory and observed route, while dynamic programming is used to accelerate the calculations of fitness functions in genetic algorithm. Model is tested on real data collected from the street network, which yielded good results with regard to accuracy and running time. Furthermore, it is shown that proposed model is capable to process the GPS data, recorded on dense street network with negligible amount of errors, which is the most challenging task for majority of map-matching algorithms. Therefore, the main impact of this research is observed through the development of better and more precise global map-matching algorithm. This is really important for expert and intelligent transport systems, which are based on input data collected by floating car data systems. This type of system relies on high quality post-processing map-matching model, which should provide accurate results in order to ensure that intelligent transport system makes more precise estimations and predictions of traffic conditions.