PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting

• Published in: IEEE access Vol. 8; pp. 6505 - 6514
• Main Authors: Cai, Weihong, Yang, Junjie, Yu, Yidan, Song, Youyi, Zhou, Teng, Qin, Jing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computational modeling; extreme learning machine; Feedforward neural networks; Forecasting; Machine learning; Management systems; Model accuracy; Neurons; Particle swarm optimization; Predictive models; Short-term traffic flow forecasting; time-series model; Traffic congestion; Traffic flow; Traffic management; Traffic models; Traffic planning; Training; Urban planning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2963784

Accurate and reliable traffic flow forecasting is of importance for urban planning and mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic management systems. However, constructing a reasonable and robust forecasting model is a challenging task due to the uncertainties and nonlinear characteristics of traffic flow. Aiming at the nonlinear relationship affecting traffic flow forecasting effect, a PSO-ELM model based on particle swarm optimization is proposed for short-term traffic flow forecasting, which takes the advantages of particle swarm optimization to search global optimal solution and extreme learning machine to fast deal with the nonlinear relationship. The proposed model improves the accuracy of traffic flow forecasting. The traffic flow data from highways A1, A2, A4, A8 connecting to Amsterdam's ring road are employed for the case study. The RMSEs of PSO-ELM model are respectively 252.61, 173.75, 200.24, 146.05, while the MAPEs of PSO-ELM model are respectively 11.86%, 10.10%, 10.74%, 11.60%. The experimental results show that the performance of the proposal is significantly better than the performance of state-of-the-art models.