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As the availability of compressed natural gas (CNG) cars grows, forecasting becomes increasingly important to enhance energy security and sustainability. It is an essential tool for creating investment strategies and regulatory systems that encourage the widespread use of CNG vehicles. Furthermore, the formation of a greener and more sustainable transport sector helps to accelerate the worldwide transition to a more environmentally friendly future. India’s Ministry of Road Transport and Highways operated the Parivahan website, which revealed sales data for four-wheeler CNG cars in the Indian’s transportation industry between 2016 and 2023. The seasonal autoregressive integrated moving average (SARIMA) and Holt–Winters methods are compared in this study utilizing mean absolute percentage error (MAPE) and root mean square error (RMSE) metrics. The SARIMA (0, 1, 0, 1, 0, 0, and 12) framework, with its lowest RMSE and MAPE, is the most precise forecast model for CNG sales throughout the next five years, until 2028. It helps decision-makers and stakeholders to optimize their supply chains, maintenance costs, and improve operational performance.

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Forecasting CNG Vehicle Sales in the Indian Transportation Sector: A Comparison of Holt–Winters and SARIMA Models

  • 会议论文

  • 发表日期：

    2026年

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  • Mohammad Yawar,
  • Anwer Shahir,
  • G. Varaprasad

摘要

As the availability of compressed natural gas (CNG) cars grows, forecasting becomes increasingly important to enhance energy security and sustainability. It is an essential tool for creating investment strategies and regulatory systems that encourage the widespread use of CNG vehicles. Furthermore, the formation of a greener and more sustainable transport sector helps to accelerate the worldwide transition to a more environmentally friendly future. India’s Ministry of Road Transport and Highways operated the Parivahan website, which revealed sales data for four-wheeler CNG cars in the Indian’s transportation industry between 2016 and 2023. The seasonal autoregressive integrated moving average (SARIMA) and Holt–Winters methods are compared in this study utilizing mean absolute percentage error (MAPE) and root mean square error (RMSE) metrics. The SARIMA (0, 1, 0, 1, 0, 0, and 12) framework, with its lowest RMSE and MAPE, is the most precise forecast model for CNG sales throughout the next five years, until 2028. It helps decision-makers and stakeholders to optimize their supply chains, maintenance costs, and improve operational performance.