A Hybrid Model for Forecasting Traffic Flow: Using Layerwise Structure and Markov Transition Matrix

• Main Authors: Shaokun Zhang, Zejian Kang, Zhemin Zhang, Congren Lin, Cheng Wang, Jonathan Li
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Traffic flow forecast; layerwise structure; Markov transition matrix; point of interest
• Online Access: https://ieeexplore.ieee.org/document/8649574/

Forecasting the traffic flow is greatly significant for traffic safety, energy conservation, and environmental protection. However, in the face of many external uncertainties, making accurate predictions about traffic volumes is a challenging issue. Many previous types of researche only explore the utility of a single factor in their prediction and rarely conduct the multi-factor research. As for the traffic flow prediction, many past types of researche focus primarily on the temporal distribution of the traffic flow on a single point on the road, ignoring the spatial correlation. In terms of global forecasting, it was logically far-fetched to mechanically view traffic as images. In this paper, considering the effects of many exogenous variables and the interaction between monitor sites, we propose a hybrid model to simultaneously predict the traffic flow in multiple positions by combining the layerwise structure and the Markov transition matrix (MTM). More specifically, we employ the layerwise structure to capture the periodicity, trend, and nonlinearity characteristics of traffic flow and, then, generate the MTM that captures the dynamics embodied in the data and produces the corresponding distributions. Considering the spatial correlation of traffic data, the real road network distance was thus introduced in our model. We apply the methodology on the real-world traffic data from Xiamen, and the experimental results show that the satisfactory predictions can be achieved using our model, which demonstrates the value of the transition matrix in traffic forecasts. In addition, we also introduce the point of interest and analyze its impact on the prediction results.