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Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles

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  • Shang, Wen-Long
  • Zhang, Mengxiao
  • Wu, Guoyuan
  • Yang, Lan
  • Fang, Shan
  • Ochieng, Washington

Abstract

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation.

Suggested Citation

  • Shang, Wen-Long & Zhang, Mengxiao & Wu, Guoyuan & Yang, Lan & Fang, Shan & Ochieng, Washington, 2023. "Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles," Applied Energy, Elsevier, vol. 351(C).
    • Handle: RePEc:eee:appene:v:351:y:2023:i:c:s0306261923012801
    DOI: 10.1016/j.apenergy.2023.121916
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    References listed on IDEAS

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    Cited by:

    1. Renjie Li & Yanyan Qin, 2024. "Car-Following Strategy Involving Stabilizing Traffic Flow with Connected Automated Vehicles to Reduce Particulate Matter (PM) Emissions in Rainy Weather," Sustainability, MDPI, vol. 16(5), pages 1-23, February.
    2. Yanzhan Chen & Fan Yu, 2023. "A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application," Mathematics, MDPI, vol. 11(23), pages 1-30, November.
    3. Chen, Xinqiang & Lv, Siying & Shang, Wen-long & Wu, Huafeng & Xian, Jiangfeng & Song, Chengcheng, 2024. "Ship energy consumption analysis and carbon emission exploitation via spatial-temporal maritime data," Applied Energy, Elsevier, vol. 360(C).

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