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Short term traffic flow prediction of expressway service area based on STL-OMS

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  • Zhao, Jiandong
  • Yu, Zhixin
  • Yang, Xin
  • Gao, Ziyou
  • Liu, Wenhui

Abstract

To improve the management ability of expressway service area and formulate strategies for traffic flow changes in time, a short-term traffic flow prediction model is proposed. Firstly, cleaning the extracted data according to the rules and constructing four kinds of features (temporal, spatial, statistical and external factors). Then, a short-term traffic flow prediction model WADNN (wide attention and deep neural networks) is constructed. In the model, LSTM (long and short-term memory neural network), CNN (convolution neural network) and self-attention mechanism are used to extract different features respectively. In addition, the STL (Seasonal-Trend decomposition procedure based on LOESS) algorithm is used to decompose the traffic flow to fit the trend better. For the three decomposed components, the OMS (optimal model selection) operation is carried out, the prediction of each component is added to obtain the final predicted value, and the model effect is measured according to the RMSE (root mean square error), MAE (mean absolute error), MAPE (Mean Absolute Percentage Error) and R2 coefficient. Finally, taking an expressway service area as an example, the proposed model is compared with some common models. The results show that the prediction effect of WADNN is better and STL-OMS can further improve the accuracy.

Suggested Citation

  • Zhao, Jiandong & Yu, Zhixin & Yang, Xin & Gao, Ziyou & Liu, Wenhui, 2022. "Short term traffic flow prediction of expressway service area based on STL-OMS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    • Handle: RePEc:eee:phsmap:v:595:y:2022:i:c:s0378437122000516
    DOI: 10.1016/j.physa.2022.126937
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    References listed on IDEAS

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    1. G. E. P. Box & G. M. Jenkins & J. F. MacGregor, 1974. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(2), pages 158-179, June.
    2. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    3. Bergmeir, Christoph & Hyndman, Rob J. & Benítez, José M., 2016. "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 303-312.
    4. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
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    Cited by:

    1. Sun, Xiaoyong & Chen, Fenghao & Wang, Yuchen & Lin, Xuefen & Ma, Weifeng, 2023. "Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Xu, Hao & Xiao, Weiwei & Zhang, Shengyu & Fan, Yuqiang & Kang, Xiaoyuan & Han, Yong & Zhou, Tuqiang, 2024. "Exploring determinants of freeway service area usage in the context of sustainable and collaborated development for transport and tourism," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    3. Li, Chunjie & Xu, Chengcheng & Chen, Yusen & Li, Zhibin, 2024. "Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    5. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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