Route Planning Scheme Research Combined With Urban Road Network Data Model

To cope with the high speed of urban life,efficiency has become the highest pursuit of transportation.There is a huge gap between the practical needs of urban transport infrastructure and the construction speed of transport. Thus,the urban road congestion problem is get increasingly serious.Road congestion will not only result in inefficient travel, but will also result in vehicle fuel and other resources’waste, and it also leads to the increasing of travel costs.Thus, analysis of the urban road traffic related data to find the optimal travel route has become the focus of current research.In this paper,the research objective is to find the shortest time-consuming path of travel.We use the 1600 taxis’historical GPS track data as the data base. Then use the grid method to make the city road network is abstracted into grid model so as to facilitate the future work.Through the scientific and rational data processing and analysisof the historical trajectory,mining thetraveling time distribution pattern and degree of congestion of roads and buildthe road network data model based on this.Then respectively combined road network data model with the traditional Ant Colony Algorithm and the traditional Dijkstra Algorithm to find the shortesttime-consuming path comprehensive taxi drivers’path selection experienced. Finally,as the result, our algorithm respectively reduces travel-time by 62.61% on average and 41.75% on average compared with classic Ant Colony Algorithm and Dijkstra Algorithm, and respectively reduces travel-spend by 37.14% on average and 21.58% on average compared with classic Ant Colony Algorithm and Dijkstra Algorithm.