Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles

Abstract

Congestion in the world’s traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. The incorporation of contemporary technologies into transportation systems creates opportunities to significantly improve traffic prediction alongside modern academic challenges. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, a deep neural network architecture based on long short-term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers has been structured to build the deep neural network in order to predict the performance of the traffic flow in four distinct junctions, which has a great impact on the Internet of vehicles’ applications. The structure is composed of sixteen layers, five of which are GRU layers and one is a bi-directional LSTM layer. The dataset employed in this work involved four congested junctions. The dataset extended from November 1, 2016, to June 30, 2017. Cleaning and preprocessing operations were performed on the dataset before feeding it to the designed deep neural network in this paper. Results show that the suggested method produced comparable performance with respect to state-of-the-art approaches.

Keywords

• Flow prediction
• BiLSTM
• deep neural network
• GRU
• LSTM
• urban transportation