A Traffic Flow Prediction Model Based on Vehicle Travel Big Data and Simulation

With the accelerating global urbanization process, urban transportation systems are facing multiple challenges, including surging traffic flow, environmental protection, and road safety. To address the shortcomings of traditional traffic flow prediction methods, this paper proposes a traffic flow prediction model based on vehicle travel data. The model integrates real road network data and big vehicle travel data to construct a discretized dynamic road network and uses an adjacency list data structure for road network storage. It employs real-time speed data, combined with traffic flow distribution algorithms, for multi-time period path optimization and utilizes a dynamic travel time function to enable path re-planning across time periods. Based on field data from a complex road network area in Chengdu, experimental results demonstrate that the model exhibits high accuracy in traffic flow simulation and prediction, along with strong real-time response capability.