Meteorological Data-Driven Traffic Flow Forecasting Using Intelligent Algorithms

This paper proposes the incorporation of meteorological and typical seasonal Historical Traffic Flow (HTF) data into Deep Learning (DL) algorithms using Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), and Machine Learning (ML) algorithm using Random Forest Tree (RFT) to enhance the forecasting accuracy of Electric Vehicle (EV) traffic flow through electrified transportation infrastructures. The forecasting accuracy is validated by comparing performance results of three scenarios where the first case employs only HTF, the second utilizes HTF plus typical seasonal values, and the final case considers HTF plus typical seasonal values plus meteorological data. Test results demonstrate a significant improvement in forecasting accuracy of EV traffic flow for 6-hour-ahead and 24-hour-ahead horizons.