Road Traffic Prediction based on Multi-Feature BP Neural Networks.

In smart transportation system, the traffic flow is one of the important metrics to mature road traffic. Along with increasing urbanization, road traffic becomes a thorny issue to limit the development of a city. As a result of technological advancements, the transportation system is enriched with smarter devices that help it perform more efficiently and effectively. One efficient approach is leveraging existing data to predict the road traffic flow, in order to optimize road usage efficiency. There are many studies to leverage existing data to predict road traffic flow. However, the limitation is the generic traffic model cannot represent real dynamic traffic flow precisely. In this study, we analyze macroscopic traffic flow and traffic density, as well as microscopic speed, acceleration, and distance for each individual vehicle in the smart transportation context. Based on the analysis, we propose a macroscopic traffic flow model. Focus on the limitation of existing research, we also involve lane switching in the model and propose the lane switching decision making strategy. After we optimize the traffic model, we adopt BP neural network to predict the road traffic flow. The evaluation results show that our multi-feature traffic model can increase the prediction accuracy on real road traffic datasets.