Short-term Traffic Flow Prediction Based on ASO-ELM Hybrid Optimization Model

被引：0
|
作者：
Cai H. [1 ]
Li L.-F. [1 ]
Li H. [1 ]
Li X. [1 ]
Zhou T. [2 ]
机构：
[1] Department of Computer Science and Technology, Shantou University, Guangdong, Shantou
[2] School of Cyberspace Security, Hainan University, Haikou
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2023年 / 23卷 / 05期
关键词：
atom search optimization; extreme learning machine; hybrid forecasting models; intelligent transportation; short-term traffic flow forecasting;
D O I：
10.16097/j.cnki.1009-6744.2023.05.008
中图分类号：
学科分类号：
摘要：
Due to the dynamic, uncertain and nonlinear characteristics of the short-term traffic flow, it is difficult to predict traffic flow accurately. In this paper, we build an ASO-ELM short-term traffic flow prediction hybrid optimization model based on Extreme Learning Machine (ELM) by embedding Atom Search Optimization (ASO). The hybrid optimization model is used to explore the prediction performance of the hybrid optimization model in the field of short-term traffic flow prediction by comparing the existing short-term traffic flow prediction models. The A10 ring road in Amsterdam, the Netherlands, is selected as the prototype of the road network, and the ASO-ELM hybrid model is used to compare with common traffic flow forecasting models for simulation forecasting experiments. The experimental results show that the mean absolute percentage error (MAPE) of the ASO-ELM hybrid model decreases by 4.3%, 3.5%, 6.9% and 5.4%, respectively, and the root mean squared error (RMSE) decreases by 4.8%、4.0%、2.0% and 5.2%, respectively. Secondly, MAPE decreased by 9.6%, 8.6%, 9.8% and 5.0%, respectively, and RMSE decreased by 4.5%, 5.9%, 2.6% and 1.7%, respectively, compared to the Artificial Neural Network (ANN). The research results reveal the potential of hybrid optimization models in the field of short-term traffic flow forecasting and provide an important basis for model exploration in the field of short-term traffic flow forecasting. © 2023 Science Press. All rights reserved.
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页码：75 / 82and183
相关论文
共 11 条
  • [1] LIU X G, ZHANG T F, HAN Y., Research on short-term traffic flow prediction based on ARIMA model, Logistics Sci-Tech, 42, 12, pp. 91-94, (2019)
  • [2] CAI L, ZHANG Z, YANG J, Et al., A noise-immune Kalman filter for short-term traffic flow forecasting, Physica A: Statistical Mechanics and its Applications, 536, (2019)
  • [3] GUO H, XIAO X P, JEFFREY F., Urban road short-term traffic flow forecasting based on the delay and nonlinear grey model, Journal of Transportation Systems Engineering and Information Technology, 13, 6, pp. 60-66, (2013)
  • [4] FU G, HAN G Q, LU F, Et al., Short-duration traffic flow prediction model based on support vector machine regression, Journal of South China University of Technology(Natural Science Edition), 41, 9, pp. 71-76, (2013)
  • [5] CHENG Z, CHEN X F., A short-term traffic flow prediction method based on a random forest model, Microcomputer & Its Applications, 35, 10, pp. 46-49, (2016)
  • [6] JIA Z Z, JIN X C, YUAN Z., Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections, Transportation Research Part C: Emerging Technologies, 47, 2, pp. 139-154, (2014)
  • [7] WANG X X, XU L H., Short-term traffic flow prediction based on deep learning, Journal of Transportation Systems Engineering and Information Technology, 18, 1, pp. 81-88, (2018)
  • [8] HUANG G B, ZHU Q Y, SIEW C K., Extreme learning machine: A new learning scheme of feedforward neural networks, 2004 IEEE International Joint Conference on Neural Networks, 2, pp. 985-990, (2004)
  • [9] ZHAO W, WANG L, ZHANG Z., A novel atom search optimization for dispersion coefficient estimation in groundwater, Future Generation Computer Systems, 91, pp. 601-610, (2019)
  • [10] LENNARD- JONES J E., Cohesion, Proceedings of the Physical Society (1926-1948), 43, 5, (1931)
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