Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82171
Title: PSO-ELM : a hybrid learning model for short-term traffic flow forecasting
Authors: Cai, WH
Yang, JJ
Yu, YD
Song, YY 
Zhou, T 
Qin, J 
Issue Date: 2020
Source: IEEE access, 3 Jan. 2020, v. 8, p. 6505-6514
Abstract: Accurate and reliable traffic flowforecasting 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 flowforecasting, 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.
Keywords: Short-term traffic flow forecasting
Extreme learning machine
Particle swarm optimization
Time-series model
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2963784
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication W. Cai, J. Yang, Y. Yu, Y. Song, T. Zhou and J. Qin, "PSO-ELM: A Hybrid Learning Model for Short-Term Traffic Flow Forecasting," in IEEE Access, vol. 8, pp. 6505-6514, 2020 is available at https://dx.doi.org/10.1109/ACCESS.2019.2963784
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