Storm Cloud Platform Based On The Road Map Matching Algorithm

With the rapid development of urban road traffic system, GIS and GPS technologies has been widely used to solve urban intelligent transportation. Map-matching is the key technologies of GPS vehicle location and navigation system that also affects the main part of the development of intelligent transportation systems (ITS). This paper studies the underlying data processing and map-matching algorithm based on ArcGIS map and migration of map-matching algorithm to verify the feasibility of the Storm platform. The results of the research are of great significance for the application of GPS and digital map information fusion as well as explore Storm platform in the transport sector.First, with a pretreatment to the ArcGIS map of Shenzhen, the pretreatment proposed a new urban road network topology structure construction algorithm based on a new road to underlying data extraction methods underlying the characteristics of the data, which can quickly build Shenzhen City Road network topology. Secondly, by drawing on the idea of Divide and conquer, the lane track GPS taxi in Shenzhen7days subject to preprocess the data, we proposed a road map-matching algorithm based on nine place grid, the algorithm is capable of solving inefficient operations commonly used map-matching algorithm and better matching accuracy and efficiency. With experiments associated, we could verify squares grid map matching algorithm. The results show that the map-matching algorithm based on complex sections can also be matched with higher precisions, which depict a good adaptability to meet map matching of vehicle requirements. Finally, we migrate the map-matching algorithm to Storm Cloud platform. Due to a powerful parallel computing platform of Storm clouds, the map matching algorithm could improve the operating efficiency of2-3times.