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Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model

Author

Listed:
  • Xin Liu

    (Northeast Forestry University)

  • Shunlong Li

    (Northeast Forestry University)

Abstract

The present work aims to expand the application of machine learning models in predicting and identifying traffic flow data and provide a reference for the scheduling and management of shared traffic against the Coronavirus Disease 2019 (COVID-19) pandemic. First, a time segmentation-based prediction model is proposed considering the classification superiority of Support Vector Machine (SVM) and combining the Optimal Segmentation Algorithm (OSA), denoted as OSA-SVM. Second, an algorithm for generating a shared traffic flow sequence is proposed based on the historical data of shared traffic flow. Finally, a shared traffic flow moment identification model is constructed based on the label propagation algorithm and the Random Forest (RF) model. Comparative analysis suggests that the OSA-SVM regression prediction model can accurately fit the fluctuations caused by the shared traffic flow data; however, its overall effect is not good, with deviation from the actual traffic sequence. Introducing historical data for weighting processing improves the goodness-of-fit of the regression prediction model significantly, maintaining at the level of 0.66–0.71 after one week. The stochastic gradient descent algorithm can provide a better weighted processing effect. The RF model shows the best recognition effect for the shared traffic data stream compared with other models, presenting an excellent performance in dealing with the imbalance and instability problems. The proposed model and algorithm have outstanding prediction and recognition accuracy in shared traffic scheduling, playing an active role in traffic control during COVID-19 prevention and control.

Suggested Citation

  • Xin Liu & Shunlong Li, 2022. "Impact of COVID-19 pandemic on low-carbon shared traffic scheduling under machine learning model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 987-995, December.
  • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01176-x
    DOI: 10.1007/s13198-021-01176-x
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    References listed on IDEAS

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    1. Zengzhen Shao & Zujun Ma & Shulei Liu & Tongshuang Lv, 2017. "Optimization of a Traffic Control Scheme for a Post-Disaster Urban Road Network," Sustainability, MDPI, vol. 10(1), pages 1-22, December.
    2. Palmer, Kate & Tate, James E. & Wadud, Zia & Nellthorp, John, 2018. "Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan," Applied Energy, Elsevier, vol. 209(C), pages 108-119.
    3. Alvin Lal & Bithin Datta, 2018. "Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2405-2419, May.
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