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Title	Data-Driven Decision Support By Utilizing Machine Learning To Predict Passenger Flow For Route And Station Optimization
ID_Doc	17407
Authors	Patel M.; Patel S.B.; Swain D.
Year	2025
Published	Journal of The Institution of Engineers (India): Series B
DOI	http://dx.doi.org/10.1007/s40031-025-01215-2
Abstract	A smart public transportation system with reliable services addresses urban challenges like traffic congestion, infrastructure maintenance, travel costs, and pollution. As part of smart city initiatives, urban public transport systems have adopted advanced data collection methods, enabling data analytics and predictive model development. This study evaluates optimal machine learning models for predicting passenger flow across routes and stations within the Thane Municipal Transport (TMT) network using real historical data from March 2021 to May 2022. Key spatial and temporal features such as weekday patterns, route distance and direction, public holidays, COVID-19 impacts, and weather conditions were identified through exploratory data analysis and incorporated into the models. Several algorithms, including Decision Trees, XGBoost, Extra Tree Regressor, and Random Forest, were implemented to predict daily passenger commute on an hourly basis. Random Forest emerged as the most accurate model, achieving an MAE of 114.55, RMSE of 236.71, and an R2 of 0.9516, leveraging its ensemble approach to capture complex, non-linear patterns and ensure robustness to variability, including disruptions caused by the pandemic. These findings provide actionable insights for optimizing route design, bus allocation, maintenance scheduling, and overall transport system efficiency, enabling stakeholders to achieve sustainable and adaptive public transport management. © The Institution of Engineers (India) 2025.
Author Keywords	Intelligent transport system; Passenger flow prediction; Public transit optimization; Route-level prediction; Smart mobility; Station-level prediction

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