Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting
Passenger flow forecasting is crucial for optimizing urban transit operations, especially in developing countries such as India, where congestion, infrastructure constraints, and diverse commuter behaviors pose significant challenges. Despite its importance, limited research explored forecasting mod...
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MDPI AG
2025-04-01
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| author | Madhuri Patel Samir B. Patel Debabrata Swain Rishikesh Mallagundla |
| author_facet | Madhuri Patel Samir B. Patel Debabrata Swain Rishikesh Mallagundla |
| author_sort | Madhuri Patel |
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| description | Passenger flow forecasting is crucial for optimizing urban transit operations, especially in developing countries such as India, where congestion, infrastructure constraints, and diverse commuter behaviors pose significant challenges. Despite its importance, limited research explored forecasting models for Indian urban transit systems, particularly incorporating the effects of holidays and disruptions caused by the COVID-19 pandemic. To address this gap, we propose TBATS Boosting, a novel hybrid forecasting model that integrates the statistical strengths of trigonometric, Box–Cox, ARMA, trend, and seasonal (TBATS) with the predictive power of LightGBM. The model is trained on a five-year real-world dataset from e-ticketing machines (ETM) in Thane Municipal Transport (TMT), incorporating holiday and pandemic-related variations. While Route 12 serves as a primary evaluation route, different station pairs are analyzed to validate their scalability across varying passenger demand levels. To comprehensively evaluate the proposed framework, a rigorous performance assessment was conducted using MAE, RMSE, MAPE, and WMAPE across station pairs characterized by heterogeneous passenger flow patterns. Empirical results demonstrate that the TBATS Boosting approach consistently outperforms benchmark models, including standalone SARIMA, TBATS, XGBoost, and LightGBM. By effectively capturing complex temporal dependencies, multiple seasonalities, and nonlinear relationships, the proposed framework significantly enhances forecasting accuracy. These advancements provide transit authorities with a robust tool for optimizing resource allocation, improving service reliability, and enabling data-driven decision making across varied and dynamic urban transit environments. |
| format | Article |
| id | doaj-art-88d97518f6e447d1b7df5d7c14ebdc27 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-88d97518f6e447d1b7df5d7c14ebdc272025-08-20T03:16:21ZengMDPI AGModelling2673-39512025-04-01623210.3390/modelling6020032Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS BoostingMadhuri Patel0Samir B. Patel1Debabrata Swain2Rishikesh Mallagundla3Department of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaDepartment of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaDepartment of Computer Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaDepartment of Computer Engineering and Technology, Chaitanya Bharathi Institute of Technology, Hyderabad 500075, IndiaPassenger flow forecasting is crucial for optimizing urban transit operations, especially in developing countries such as India, where congestion, infrastructure constraints, and diverse commuter behaviors pose significant challenges. Despite its importance, limited research explored forecasting models for Indian urban transit systems, particularly incorporating the effects of holidays and disruptions caused by the COVID-19 pandemic. To address this gap, we propose TBATS Boosting, a novel hybrid forecasting model that integrates the statistical strengths of trigonometric, Box–Cox, ARMA, trend, and seasonal (TBATS) with the predictive power of LightGBM. The model is trained on a five-year real-world dataset from e-ticketing machines (ETM) in Thane Municipal Transport (TMT), incorporating holiday and pandemic-related variations. While Route 12 serves as a primary evaluation route, different station pairs are analyzed to validate their scalability across varying passenger demand levels. To comprehensively evaluate the proposed framework, a rigorous performance assessment was conducted using MAE, RMSE, MAPE, and WMAPE across station pairs characterized by heterogeneous passenger flow patterns. Empirical results demonstrate that the TBATS Boosting approach consistently outperforms benchmark models, including standalone SARIMA, TBATS, XGBoost, and LightGBM. By effectively capturing complex temporal dependencies, multiple seasonalities, and nonlinear relationships, the proposed framework significantly enhances forecasting accuracy. These advancements provide transit authorities with a robust tool for optimizing resource allocation, improving service reliability, and enabling data-driven decision making across varied and dynamic urban transit environments.https://www.mdpi.com/2673-3951/6/2/32urban transit systempassenger flow forecastingmachine learninghybrid forecasting modeltime series forecasting |
| spellingShingle | Madhuri Patel Samir B. Patel Debabrata Swain Rishikesh Mallagundla Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting Modelling urban transit system passenger flow forecasting machine learning hybrid forecasting model time series forecasting |
| title | Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting |
| title_full | Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting |
| title_fullStr | Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting |
| title_full_unstemmed | Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting |
| title_short | Enhancing Accuracy in Hourly Passenger Flow Forecasting for Urban Transit Using TBATS Boosting |
| title_sort | enhancing accuracy in hourly passenger flow forecasting for urban transit using tbats boosting |
| topic | urban transit system passenger flow forecasting machine learning hybrid forecasting model time series forecasting |
| url | https://www.mdpi.com/2673-3951/6/2/32 |
| work_keys_str_mv | AT madhuripatel enhancingaccuracyinhourlypassengerflowforecastingforurbantransitusingtbatsboosting AT samirbpatel enhancingaccuracyinhourlypassengerflowforecastingforurbantransitusingtbatsboosting AT debabrataswain enhancingaccuracyinhourlypassengerflowforecastingforurbantransitusingtbatsboosting AT rishikeshmallagundla enhancingaccuracyinhourlypassengerflowforecastingforurbantransitusingtbatsboosting |