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Electric Vehicles (EVs) are increasing gradually in recent days and replacing the IC engine in the transportation system. But charging of those EVs is a challenging task for the less availability of the Electric Vehicle Charging Station (EVCS), poor infrastructure and maintenance. Hence it is essential to predict hour wise EVs traffic flow to estimate electrical power demand to charge EVs. The hour wise EVs flow pattern is dependent on weather data and day types. Therefore, for predicting hour wise traffic flow pattern, future weather data forecasting is needed. For forecasting future weather data and hour wise traffic flow pattern, Random Forest Regressor model is used in this paper. The hour wise predicted EVs flow pattern is clustered based on data types.

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EV Flow Forecasting in Highway Using Random Forest Machine Learning Model

  • 会议论文

  • 发表日期：

    2025年

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  • Vargab Ghosh,
  • Aniruddha Bhattacharya

摘要

Electric Vehicles (EVs) are increasing gradually in recent days and replacing the IC engine in the transportation system. But charging of those EVs is a challenging task for the less availability of the Electric Vehicle Charging Station (EVCS), poor infrastructure and maintenance. Hence it is essential to predict hour wise EVs traffic flow to estimate electrical power demand to charge EVs. The hour wise EVs flow pattern is dependent on weather data and day types. Therefore, for predicting hour wise traffic flow pattern, future weather data forecasting is needed. For forecasting future weather data and hour wise traffic flow pattern, Random Forest Regressor model is used in this paper. The hour wise predicted EVs flow pattern is clustered based on data types.