Hybrid Deformable Convolutional with Recurrent Neural Network for Optimal Traffic Congestion Prediction: A Fuzzy Logic Congestion Index System

In the field of Intelligent Transportation Systems (ITs), traffic congestion is considered as an important problem. Traffic blockage usually affects the quality of time, travel time, economy of the country, and transportability of people. The information of traffic congestion is collected and analyzed in ITs, and the methods to prevent the traffic congestion are predicted. However, the tackling of huge data is still challenging. The rapid increase in vehicle usage and road construction has resulted in traffic congestion. Various studies are undergone in ITs to recognize the traffic management system by adopting few resources. Real time-based traffic services are implemented to prevent the traffic congestion in existing areas. These services provide high expense accuracy. This paper plans to develop a new technique to predict the traffic congestion using improved deep learning approaches. At first, the benchmark dataset is gathered and the pre-processing of data is performed with removing the bad data, organizing the raw data, and filling the null values. The optimized weighted features are selected from the pre-processed data by adopting a new meta-heuristic Hybrid Jaya Harris Hawk Optimization (HJHHO) algorithm. The prediction of congestion parameters such as speed reduction rate, very low speed rate, and volume to capacity ratio of vehicles are performed by the proposed Improved Deformable Convolutional Recurrent Network (IDCRN) prediction model. These predicted measures are subjected to fuzzy interference system for congestion index computation. From the experimental analysis, it has proved that the proposed method has reduced the error rate while comparing with other deep learning and machine learning approaches.