Is There a Limit to Adoption of Dynamic Ridesharing Systems? Evidence from Analysis of Uber Demand Data from New York City

Author(s):  
Raymond Gerte ◽  
Karthik C. Konduri ◽  
Naveen Eluru

Recent technological advances have paved the way for new mobility alternatives within established transportation networks, including on-demand ride hailing/sharing (e.g., Uber, Lyft) and citywide bike sharing. Common across these innovative modes is a lack of direct ownership by the user; in each of these mobility offerings, a resource not owned by the end users’ is shared for fulfilling travel needs. This concept has flourished and is being hailed as a potential option for autonomous vehicle operation moving forward. However, substantial investigation into how new shared modes affect travel behaviors and integrate into existing transportation networks is lacking. This paper explores whether the growth in the adoption and usage of these modes is unbounded, or if there is a limit to their uptake. Recent trends and shifts in Uber demand usage from New York City were investigated to explore the hypothesis. Using publicly available data about Uber trips, temporal trends in the weekly demand for Uber were explored in the borough of Manhattan. A panel-based random effects model accounting for both heteroscedasticity and autocorrelation effects was estimated wherein weekly demand was expressed as a function of a variety of demographic, land use, and environmental factors. It was observed that demand appeared to initially increase after the introduction of Uber, but seemed to have stagnated and waned over time in heavily residential portions of the island, contradicting the observed macroscopic unbounded growth. The implications extend beyond already existing fully shared systems and also affect the planning of future mobility offerings.

2014 ◽  
Vol 40 (3) ◽  
pp. 530-533 ◽  
Author(s):  
Corey H. Basch ◽  
Danna Ethan ◽  
Patricia Zybert ◽  
Sarah Afzaal ◽  
Michael Spillane ◽  
...  

Cities ◽  
2021 ◽  
pp. 103475
Author(s):  
Yan Chen ◽  
Yongping Zhang ◽  
D'Maris Coffman ◽  
Zhifu Mi

2021 ◽  
Author(s):  
Sergio Dellepiane ◽  
Akhil Vaid ◽  
Suraj K Jaladanki ◽  
Ishan Paranjpe ◽  
Steven Coca ◽  
...  

AbstractAcute Kidney Injury (AKI) is among the most common complications of Coronavirus Disease 2019 (COVID-19). Throughout 2020 pandemic, the clinical approach to COVID-19 has progressively improved, but it is unknown how these changes have affected AKI incidence and severity. In this retrospective analysis, we report the trend over time of COVID-19 associated AKI and need of renal replacement therapy in a large health system in New York City, the first COVID-19 epicenter in United States.


2017 ◽  
Vol 2650 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Lucas Mestres Mendes ◽  
Manel Rivera Bennàssar ◽  
Joseph Y. J. Chow

Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.


2002 ◽  
Vol 23 (4) ◽  
pp. 221-223 ◽  
Author(s):  
Mary Beth Terry ◽  
Moïse Desvarieux ◽  
Margaret Short

AbstractNew York City hospitalization rates were analyzed to investigate whether tuberculosis (TB) hospitalizations declined after implementation of directly observed therapy QOOT) for TB. TB hospitalization rates mirrored incidence rates in pattern but not in magnitude. Rates have declined significantly following widespread implementation of DOT in 1993.


Author(s):  
Raymond Gerte ◽  
Karthik C. Konduri ◽  
Nalini Ravishanker ◽  
Amit Mondal ◽  
Naveen Eluru

The concept of shared travel, making trips with other users via a common vehicle, is far from novel. However, a changing technological climate has laid the tracks for new dynamically shared modes in the form of transportation network companies (TNCs), to substantially impact travel behavior. The current body of research on how these modal offerings impact the demand for existing shared modes (e.g., bikeshare, transit) is growing. However, a comprehensive investigation of the temporal evolution of the demand for TNCs and their relationship to other shared modes, is lacking. This research tackles this important limitation by analyzing ridership data for TNCs, taxi, subway, and Citi Bike in New York City using daily ridership data from January 2015 through June 2017. The primary objective was to understand the relationship between TNCs and other shared modal offerings while accounting for the influence of temporal trends and other exogenous factors. A dynamic linear modeling framework was formulated to accommodate time-dependent trends, periodicity, and time-varying exogenous factors on the demand for TNCs. As a preliminary work, the findings of this study reinforce the observed substitution relationship between taxis and TNCs. The results may also indicate a substitutional relationship between TNCs and Citi Bike, and a complementary relationship with subway, however these results still need to be explored further. With potentially impactful findings for planning and policymakers, the predictive model developed in the study can be used to carry out forecasting in support of short- and long-term operations and planning applications.


2017 ◽  
Vol 119 ◽  
pp. 42-50 ◽  
Author(s):  
Lani M’cleod ◽  
Richard Vecsler ◽  
Yuan Shi ◽  
Ekaterina Levitskaya ◽  
Sunny Kulkarni ◽  
...  

1991 ◽  
Vol 26 (10) ◽  
pp. 1089-1105 ◽  
Author(s):  
K. H. Van Hoeven ◽  
R. L. Stoneburner ◽  
W. C. Rooney

2021 ◽  
Author(s):  
Sergio Dellepiane ◽  
Akhil Vaid ◽  
Suraj K. Jaladanki ◽  
Steven Coca ◽  
Zahi A. Fayad ◽  
...  

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