A Macroscopic-Fundamental-Function-Aided Neural Networks for Traffic Flows Prediction From Mobile Signaling Data

The widespread deployment of the Internet of Things (IoT) allows for the utilization of mobile signaling data (MSD) in cellular networks to perceive the underlying traffic states on road networks. Nevertheless, the application of MSD for traffic flow prediction can be impeded by the limited positioning accuracy of MSD and stringent privacy policies. To surmount these obstacles, we propose a traffic flow prediction method that integrates an estimation process using a macroscopic fundamental function (M) with a positional factor (P), combined with the sequence-to-sequence (S2S) neural networks model (MPS2S). First, we estimate traffic flow using a macroscopic fundamental function (M). In this approach, we introduce a positional factor (P) that fuses road inflows and outflows to capture the relationship between cellular network and road network. Next, we employ an S2S neural network model, which considers the spatial and temporal aggregation information to infer future traffic flows. Specifically, an encoder estimates traffic parameters from MSD, a decoder infers multistep traffic flows, and a coefficients estimation method introduces the macroscopic fundamental function into the neural network. To validate the prediction performance of our proposed model, we collect video data from roadside cameras to measure ground-truth values. In addition, we analyze the impact of the positional factor on our estimation method and the impact of spatial and temporal aggregation on our prediction model in real-world freeway scenarios. The results indicate that MSD can link the potential traffic information, and our method contributes to better traffic flow prediction.