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Identifying the electricity-saving driving behaviors of electric bus based on trip-level electricity consumption: a machine learning approach

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Abstract

Electric buses (EBs) are gaining popularity worldwide as a more sustainable and eco-friendly alternative to diesel buses (DBs). Electricity-saving driving plays a crucial role in minimizing an EB’s energy consumption, subsequently leading to an extended driving range. This study proposes a machine learning–based framework for identifying electricity-saving EB driving behaviors during various driving stages, including running on road segments, entering bus stops/intersections, and exiting bus stops/intersections. The proposed random forest (RF) model effectively evaluates the energy consumption level using EB drivers’ historical driving data under different scenarios. Specifically, the electricity consumption factor (ECF), as the evaluation index, is divided into three categories to determine the implicit relationship between driving behavior and energy consumption. The results indicate that the classification accuracy of RF models surpasses 90%, which highlights the effectiveness in accurately identifying energy-efficient EB driving behaviors. In addition, the Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are utilized to visualize and interpret the results of RF models. A speed interval of 30–40 km/h is identified as the most energy-efficient range for EB running on a road segment. Findings from this study can be applied to targeted optimization of electricity-saving driving strategies in different driving scenarios to improve the overall efficiency and sustainability of the transportation system.

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Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

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Funding

The research in this paper was jointly supported by the National Key Research and Development Program of China (No. 2021YFE0112700), the Postgraduates Research and Innovation Program in Jiangsu Province (No. 36 KYCX21_0128), the National Natural Science Foundation of China (No. 41877395), and the EU-funded project MODALES (grant agreement No. 815189).

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Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, 210096, China

    Sirui Nan & Tiezhu Li

  2. Urban Planning and Transportation Group, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, the Netherlands

    Sirui Nan & Feixiong Liao

  3. Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK

    Haibo Chen

  4. Golden Dragon Bus Co., Ltd, Nanjing, 210096, China

    Jian Sun

Authors
  1. Sirui Nan
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  2. Feixiong Liao
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  3. Tiezhu Li
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  4. Haibo Chen
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  5. Jian Sun
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Contributions

Sirui Nan: conceptualization, methodology, validation, formal analysis, investigation, writing—original draft, and visualization. Feixiong Liao: conceptualization, methodology, writing—review and editing, and supervision. Tiezhu Li: conceptualization, data curation, writing—review and editing, and supervision. Haibo Chen: data curation. Jian Sun: writing—review and editing.

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Correspondence to Tiezhu Li.

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Nan, S., Liao, F., Li, T. et al. Identifying the electricity-saving driving behaviors of electric bus based on trip-level electricity consumption: a machine learning approach. Environ Sci Pollut Res 30, 82743–82759 (2023). https://doi.org/10.1007/s11356-023-28107-6

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  • DOI: https://doi.org/10.1007/s11356-023-28107-6

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