Forecasting urban travel demand with geo-AI: a combination of GIS and machine learning techniques utilizing uber data in New York City
Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New Yor...
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| Published in | Environmental earth sciences Vol. 83; no. 20; p. 594 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1866-6280 1866-6299 |
| DOI | 10.1007/s12665-024-11900-y |
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| Abstract | Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New York City, employing automated machine learning algorithms to assess and forecast ride demand inside the city. In conventional transportation planning, forecasting travelers’ destination selections is a crucial stage. Traditional methodologies, such as physical models like gravity models, are constrained in their capacity to encompass the comprehensive array of elements affecting travel behavior, frequently addressing just two or three variables. This study employs machine learning methodologies for predicting passenger destination choices, illustrating that these algorithms can include a wider array of variables, hence providing enhanced forecast accuracy relative to conventional approaches. The utilization of ArcGIS Pro and Python modules for automation enhanced the efficiency of spatial analysis. A range of machine learning methodologies, such as decision trees, Light-GBM, XG-Boost, Cat-Boost, and hybrid models, were utilized to predict demand. The selected source demand forecasting model is an ensemble model, with a R
2
of 0.94 and a Mean Absolute Error of 91.63. The optimal model for predicting destination demand was identified as a composite model comprising eight distinct algorithms. The model’s R
2
is 0.95, while the Mean Absolute Error is 67.12. Moreover, the examination of environmental factors indicated that proximity to recreational activities, median age, and population density had the most substantial influence on predicting travel demand. |
|---|---|
| AbstractList | Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New York City, employing automated machine learning algorithms to assess and forecast ride demand inside the city. In conventional transportation planning, forecasting travelers’ destination selections is a crucial stage. Traditional methodologies, such as physical models like gravity models, are constrained in their capacity to encompass the comprehensive array of elements affecting travel behavior, frequently addressing just two or three variables. This study employs machine learning methodologies for predicting passenger destination choices, illustrating that these algorithms can include a wider array of variables, hence providing enhanced forecast accuracy relative to conventional approaches. The utilization of ArcGIS Pro and Python modules for automation enhanced the efficiency of spatial analysis. A range of machine learning methodologies, such as decision trees, Light-GBM, XG-Boost, Cat-Boost, and hybrid models, were utilized to predict demand. The selected source demand forecasting model is an ensemble model, with a R
2
of 0.94 and a Mean Absolute Error of 91.63. The optimal model for predicting destination demand was identified as a composite model comprising eight distinct algorithms. The model’s R
2
is 0.95, while the Mean Absolute Error is 67.12. Moreover, the examination of environmental factors indicated that proximity to recreational activities, median age, and population density had the most substantial influence on predicting travel demand. Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New York City, employing automated machine learning algorithms to assess and forecast ride demand inside the city. In conventional transportation planning, forecasting travelers’ destination selections is a crucial stage. Traditional methodologies, such as physical models like gravity models, are constrained in their capacity to encompass the comprehensive array of elements affecting travel behavior, frequently addressing just two or three variables. This study employs machine learning methodologies for predicting passenger destination choices, illustrating that these algorithms can include a wider array of variables, hence providing enhanced forecast accuracy relative to conventional approaches. The utilization of ArcGIS Pro and Python modules for automation enhanced the efficiency of spatial analysis. A range of machine learning methodologies, such as decision trees, Light-GBM, XG-Boost, Cat-Boost, and hybrid models, were utilized to predict demand. The selected source demand forecasting model is an ensemble model, with a R² of 0.94 and a Mean Absolute Error of 91.63. The optimal model for predicting destination demand was identified as a composite model comprising eight distinct algorithms. The model’s R² is 0.95, while the Mean Absolute Error is 67.12. Moreover, the examination of environmental factors indicated that proximity to recreational activities, median age, and population density had the most substantial influence on predicting travel demand. Presently, online ride-hailing firms and municipal transportation systems substantially influence urban traffic management. Considering the increasing use of these services, it is essential to assess and predict travel demand to optimize service efficiency. This study analyzes Uber data from New York City, employing automated machine learning algorithms to assess and forecast ride demand inside the city. In conventional transportation planning, forecasting travelers’ destination selections is a crucial stage. Traditional methodologies, such as physical models like gravity models, are constrained in their capacity to encompass the comprehensive array of elements affecting travel behavior, frequently addressing just two or three variables. This study employs machine learning methodologies for predicting passenger destination choices, illustrating that these algorithms can include a wider array of variables, hence providing enhanced forecast accuracy relative to conventional approaches. The utilization of ArcGIS Pro and Python modules for automation enhanced the efficiency of spatial analysis. A range of machine learning methodologies, such as decision trees, Light-GBM, XG-Boost, Cat-Boost, and hybrid models, were utilized to predict demand. The selected source demand forecasting model is an ensemble model, with a R2 of 0.94 and a Mean Absolute Error of 91.63. The optimal model for predicting destination demand was identified as a composite model comprising eight distinct algorithms. The model’s R2 is 0.95, while the Mean Absolute Error is 67.12. Moreover, the examination of environmental factors indicated that proximity to recreational activities, median age, and population density had the most substantial influence on predicting travel demand. |
| ArticleNumber | 594 |
| Author | Vafaeinejad, Alireza Haery, Sana Mahpour, Alireza |
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| Cites_doi | 10.1145/1015330.1015432 10.1016/j.scs.2023.104889 10.1016/j.tranpol.2023.07.022 10.1016/j.cities.2022.103706 10.1080/10106049.2021.1871669 10.1016/j.cities.2023.104439 10.1016/j.tbs.2018.06.002 10.3390/buildings12091406 10.1016/j.jtrangeo.2020.102661 10.1016/j.trip.2023.100903 10.1016/j.jtrangeo.2021.103172 10.1080/10298436.2020.1858483 10.1016/j.tre.2022.102835 10.1016/j.ajsl.2023.02.003 10.1016/j.trd.2023.103839 10.1016/j.cities.2023.104520 10.1016/j.treng.2020.100013 10.1016/j.icte.2022.02.001 10.1016/j.engappai.2023.106411 10.1016/j.apgeog.2022.102699 10.1007/s42421-021-00041-4 10.1109/I2CT45611.2019.9033922 10.1016/j.conbuildmat.2022.130184 10.1007/978-1-4842-5967-2 10.1016/j.jtrangeo.2019.01.004 10.1016/j.jtrangeo.2022.103310 10.1016/j.jtrangeo.2023.103596 10.1007/978-0-387-84858-7 |
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| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| References_xml | – reference: Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: unbiased boosting with categorical features. 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| Title | Forecasting urban travel demand with geo-AI: a combination of GIS and machine learning techniques utilizing uber data in New York City |
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