Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning
| Autor: | Aniekan Essien, Sandra Sampaio, Pedro Sampaio, Ilias Petrounias |
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| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Computer Networks and Communications Computer science Big data 02 engineering and technology Machine learning computer.software_genre Data modeling Long short-term neural networks Artificial Intelligence 0502 economics and business 0202 electrical engineering electronic engineering information engineering Time series intelligent transportation systems (ITS) 050210 logistics & transportation Artificial neural network business.industry Deep learning 05 social sciences deep learning Traffic flow Sensor fusion traffic data science 020201 artificial intelligence & image processing Artificial intelligence business Operating speed computer data-fusion Information Systems |
| Zdroj: | BigComp Essien, A, Petrounias, I, Sampaio, P & Sampaio, S 2019, Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning . in 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019-Proceedings ., 8679231, 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019-Proceedings, IEEE Computer Society, Kyoto, Japan . https://doi.org/10.1109/bigcomp.2019.8679231 |
| DOI: | 10.1109/bigcomp.2019.8679231 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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