A Novel Time Efficient Machine Learning-based Traffic Flow Prediction Method for Large Scale Road Network
How to effectively improve the traffic efficiency of the road network plays a crucial role in ensuring the regular operation of modern society. This is also a key concern in the field of intelligent transportation systems. As the basis for formulating traffic control strategies, efficient and accurate traffic flow forecasting is essential. Accordingly, various prediction methods have been proposed for addressing the traffic flow prediction issue. However, we notice that most researchers only take the accuracy performance as the primary evaluation criteria and do not consider the problem of time cost. Consequently, the timeliness of the prediction results cannot be guaranteed. In this case, no matter how high the accuracy of the prediction is, it cannot provide practical information for the formulation of traffic measures. Therefore, in this paper, by exploiting the dimension reduction ability of Auto-Encoder (AE), we proposed a time-efficient prediction method for a large-scale road network that significantly reduces the prediction processing time while ensuring prediction accuracy. We conducted simulation experiments, and the corresponding test results demonstrate a substantial improvement in the time efficiency of our method compared to the traditional methods.
