Name
Abstract | ||
|---|---|---|
The Citi Bike deployment changes the landscape of urban mobility in New York City and provides an example of a scalable solution that many other large cities are already adopting around the world. Urban stakeholders who are considering a similar deployment would largely benefit from a quantitative assessment of the impact of bike sharing on urban transportation, as well as associated economic, social and environmental implications. While the Citi Bike usage data is publicly available, the main challenge of such an assessment is to provide an adequate baseline scenario of what would have happened in the city without the Citi Bike system. Existing efforts, including the reports of Citi Bike itself, largely imply arbitrary and often unrealistic assumptions about the alternative transportation mode people would have used otherwise (e.g. by comparing bike trips against driving). The present paper offers a balanced baseline scenario based on a transportation choice model to describe projected customer behavior in the absence of the Citi Bike system. The model also acknowledges the fact that Citi Bike might be used for recreational purposes and, therefore, not all the trips would have been actually performed, if Citi Bike would not be available. The model is trained using open Citi Bike and other urban transportation data and it is applied to assess direct benefits of Citi Bike trips for the end users, as well as for urban stakeholders across different boroughs of New York City and the nearby Jersey City. Besides estimating the travel time and cost savings, the model also reports the associated gas savings, emissions cut and additional exercise for the customers, covering all three areas of anticipated impacts - economic, social and environmental. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Physics and Society | Software deployment,End user,Consumer behaviour,Transport engineering,Recreation,Artificial intelligence,Quantitative assessment,TRIPS architecture,Usage data,Mathematics,Machine learning,Scalability |
DocType | Volume | Citations |
Journal | abs/1808.06606 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanislav Sobolevsky | 1 | 464 | 32.15 |
| Ekaterina Levitskaya | 2 | 1 | 0.77 |
| Henry Chan | 3 | 43 | 7.51 |
| Marc Postle | 4 | 1 | 0.77 |
| Constantine E. Kontokosta | 5 | 25 | 6.81 |