A Method for Extracting Section Information of Highway Based on Massive GPS Data

Information of highway is the basic element of intelligent transportation system. This study proposes a method for extracting section information of highway based on massive GPS data. The method has the advantages of low time complexity and high accuracy. The method is inspired by the algorithm of Density Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN can discover cluster of arbitrary shape. The method calculated neighborhood density of GPS points by algorithm of zone division; therefore this method reduces the computation scale and the time complexity greatly. Taking into account the uneven distribution of GPS data between sections, the method of hierarchical division is adopted in clustering, and iterative clustering is computed from dense to sparse. Finally we find that our method can find the road information efficiently on massive GPS data.