Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin
The goal of the current study is to identify and quantify the influence of various contributing factors on dockless e-scooter demand. Drawing on high-resolution e-scooter trip level data for 2019 from Austin, Texas, we develop census tract (CT) level demand data for four time periods of the day. The...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/9905842 |
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| author | Nami Alsulami Sudipta Dey Tirtha Shamsunnahar Yasmin Naveen Eluru |
| author_facet | Nami Alsulami Sudipta Dey Tirtha Shamsunnahar Yasmin Naveen Eluru |
| author_sort | Nami Alsulami |
| collection | DOAJ |
| description | The goal of the current study is to identify and quantify the influence of various contributing factors on dockless e-scooter demand. Drawing on high-resolution e-scooter trip level data for 2019 from Austin, Texas, we develop census tract (CT) level demand data for four time periods of the day. The time-period specific data is partitioned for weekdays and weekends. Using the prepared datasets, we develop a joint panel linear regression (JPLR) model framework that accommodates for the influence of unobserved factors at multiple levels-CT, month, day, and time period levels. The analysis results indicate that the proposed JPLR models outperform the independent linear regression models for both weekdays and weekends. The results also manifest a significant association between e-scooter demand and several independent variables including sociodemographic attributes, transportation infrastructure variables, land use and built environment variables, meteorological attributes, and situational attributes. Further, several panel-specific correlation effects are found to be significant across four dimensions highlighting the importance of accommodating the influence of common unobserved factors on e-scooter demand across different time-of-day dimensions. The model validation exercise results revealed that the proposed models performed well compared to the independent models. Finally, the estimated models are employed to conduct a policy exercise illustrating the value of the estimated models for understanding CT level e-scooter demand on weekdays and weekends. The results indicate that land use mix, proportion of commuters, and season are some of the most influential factors for e-scooter demand. |
| format | Article |
| id | doaj-art-affd5e558ad243b69624da334c7fd315 |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-affd5e558ad243b69624da334c7fd3152025-08-20T03:04:29ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/9905842Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of AustinNami Alsulami0Sudipta Dey Tirtha1Shamsunnahar Yasmin2Naveen Eluru3Department of Civil, Environmental and Construction EngineeringDepartment of Civil, Environmental and Construction EngineeringQueensland University of Technology (QUT)Department of Civil, Environmental and Construction EngineeringThe goal of the current study is to identify and quantify the influence of various contributing factors on dockless e-scooter demand. Drawing on high-resolution e-scooter trip level data for 2019 from Austin, Texas, we develop census tract (CT) level demand data for four time periods of the day. The time-period specific data is partitioned for weekdays and weekends. Using the prepared datasets, we develop a joint panel linear regression (JPLR) model framework that accommodates for the influence of unobserved factors at multiple levels-CT, month, day, and time period levels. The analysis results indicate that the proposed JPLR models outperform the independent linear regression models for both weekdays and weekends. The results also manifest a significant association between e-scooter demand and several independent variables including sociodemographic attributes, transportation infrastructure variables, land use and built environment variables, meteorological attributes, and situational attributes. Further, several panel-specific correlation effects are found to be significant across four dimensions highlighting the importance of accommodating the influence of common unobserved factors on e-scooter demand across different time-of-day dimensions. The model validation exercise results revealed that the proposed models performed well compared to the independent models. Finally, the estimated models are employed to conduct a policy exercise illustrating the value of the estimated models for understanding CT level e-scooter demand on weekdays and weekends. The results indicate that land use mix, proportion of commuters, and season are some of the most influential factors for e-scooter demand.http://dx.doi.org/10.1155/2023/9905842 |
| spellingShingle | Nami Alsulami Sudipta Dey Tirtha Shamsunnahar Yasmin Naveen Eluru Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin Journal of Advanced Transportation |
| title | Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin |
| title_full | Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin |
| title_fullStr | Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin |
| title_full_unstemmed | Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin |
| title_short | Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin |
| title_sort | modeling dockless shared e scooter demand by time of day a case study of austin |
| url | http://dx.doi.org/10.1155/2023/9905842 |
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