Research on Traffic Flow Time Series Model and Shortest Path Algorithm of Urban Traffic Based on Travel Plans

The Development and popularization of the vehicle navigation technology, automatic driving technology, GPS technology and big data technology, provide conditions for the transparency and sharing of traffic data in the future. Meanwhile, the innovation of the 5G technology and cloud computing also create conditions for the calculation of massive data. To effectively alleviate traffic congestion in modern cities, the authors proposed a traffic flow time series model and shortest path algorithm of urban traffic based on travel plans. Travel plans, namely, are the information about users' planning travel, including the starting point, destination, departure time, etc. Adopting lots of users' travel plans as the main source data, the authors constructed a traffic flow time series model with dynamic future traffic flow. Based on this model, the improved Floyd algorithm was used to calculate the shortest path. After the shortest path is adopted by users, update the traffic flow time series model to obtain more accurate future traffic flow data. Travel plans have the characteristics of real-time and reliability, the traffic flow data based on real-time travel plans is more realistic than the traditional traffic flow prediction methods which are based on historical data. Likewise, the optimal path algorithm based on the traffic flow time series model is also more scientific than the traditional optimal path algorithms theoretically.