Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction

Abstract

Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/ .