Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construction works. The impact of these factors can affect traffic flow parameters by influencing travel time, density, occupancy, and operating speed. Although deep neural networks (DNNs) have recently shown promising signs in traffic prediction using big data, there still exists the issue of maximizing the use of the model capabilities by using big data sources. This paper proposes an improved urban traffic speed prediction approach involving input-level data fusion and deep learning. Motivated by deep learning prediction methods, we propose a Long Short-Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network in Greater Manchester, United Kingdom. The experimental results substantiate the value of the approach when compared to the use of traffic-only data sources for traffic speed prediction.