Forecasting Ride-Hailing Demand in Urban Areas: A Deep Ensemble and Time Series Clustering Approach
| dc.contributor.advisor | Quadrifoglio, Luca | |
| dc.contributor.committeeMember | Zhang, Yunlong | |
| dc.contributor.committeeMember | Aprahamian, Hrayer | |
| dc.creator | Acharya, Kushal | |
| dc.date.accessioned | 2024-07-30T22:59:56Z | |
| dc.date.created | 2023-12 | |
| dc.date.issued | 2023-12-04 | |
| dc.date.submitted | December 2023 | |
| dc.date.updated | 2024-07-30T22:59:57Z | |
| dc.description.abstract | This paper investigates the increasingly important task of forecasting demand for ride-hailing services, which have significantly disrupted traditional transportation models. Notably, the study concentrates on New York City's Yellow Cab service, which despite the surge in popularity of app-based services, continues to serve a substantial number of commuters. The study highlights the necessity of accurate demand prediction for efficient resource allocation, reduced wait times and improved user satisfaction. Traditional forecasting methods like Historical Average, Exponential Weighted Moving Averages, ARIMA etc., are examined, alongside the more recent machine learning and data mining techniques, CNN-LSTM and XGBoost. A novel approach, utilizing an ensemble of machine learning models – XGBoost and Convolutional Neural Network – LSTM along with creative feature engineering is proposed for real-time demand forecasting across numerous locations. Furthermore, the study also tries to understand the application of time-series clustering methods and their effectiveness in grouping similar time-series together and extracting clustering features to improve the performance of the model. Additionally, the study observes the ineffectiveness of generalized model to forecast demand in low-demand reasons and presents possible research direction for solving the issue. This study contributes to the growing literature on demand forecasting in the ride-hailing industry and provides insights into the use of time-series clustering for the same. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/203051 | |
| dc.language.iso | en | |
| dc.subject | Ride-Hailing | |
| dc.subject | Ensemble | |
| dc.subject | CNN-LSTM | |
| dc.subject | Time-Series Forecasting | |
| dc.title | Forecasting Ride-Hailing Demand in Urban Areas: A Deep Ensemble and Time Series Clustering Approach | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.lift | 2025-12-01 | |
| local.embargo.terms | 2025-12-01 | |
| local.etdauthor.orcid | 0009-0001-8756-4217 | |
| thesis.degree.department | Civil and Environmental Engineering | |
| thesis.degree.discipline | Civil Engineering | |
| thesis.degree.grantor | Texas A&M University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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