PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor

Abstract

Traffic congestion prediction is a vital part of Intelligent Transportation Systems in smart cities. Effective methods for traffic congestion prediction can help people make travel plans reasonably with Advanced Traveler Information Systems. Most of the existing methods for traffic congestion prediction was designed for a specific location. The parameters need to be modified when applying these methods to different locations. Other studies on the traffic network require sophisticated data pre-processing such as map matching. In this paper, we build a model named Relative Position Congestion Tensor and propose a Predictor for Position Congestion Tensor for traffic congestion prediction. First, we design a novel approach to construct congestion matrix on region traffic networks using the concept of relative locations for road nodes and convert matrices into three-dimensional spatio-temporal tensors. Then, we propose a method based on convolutional long-short term memory network to predict congestion at all locations of the road network in the near future. The experiments show that in all locations where congestion often occurs, the proposed method significantly outperforms baseline models including Linear Regression, Autoregressive Integrated Moving Average, Support Vector Regression, Random Forest, Gradient Boosting Regression, Long-Short Term Memory and generally outperforms the Convolution-based deep Neural Network modeling Periodic traffic data. Furthermore, we study the internal structure of the Predictor for Position Congestion Tensor model to analyze the interpretability of the model for congestion prediction. The results show that the proposed model can accurately capture the temporal and spatial characteristics of traffic.