A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

Abstract

Forecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity in tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data, and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the COVID-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 is used. Different estimation models, including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and Long Short-Term Memory are applied, and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.