Study on Urban Road Network Traffic District Division based on Clustering Analysis

Aiming at the problems of road congestion and complex road network information during peak commuting period, a spatial statistical method for traffic district division based on K-means algorithm is proposed. This method uses Jinan taxi GPS data, uses Python software to realize the K-means clustering division of taxi departure and departure points, and uses Moran's I model to analyze the spatial statistical characteristics of the partition results. Then, the taxi speed data is analyzed by ArcGIS software to further divide the community according to the clustering results. The results will be displayed on the map. The results show that this method can more reasonably and accurately classify traffic districts by collecting and analyzing a large number of historical data.