Social Mobility Sharing
On-demand ride-sharing has been enabled by new technology deployed at a large scale. Passengers are matched with strangers with similar origins, destinations, and time windows, and share their rides in captive, intimate spaces for a moderate period of time.
In theory, this new transportation option could contribute to a reduction of the number of vehicles on the road while increasing accessibility. In addition, this emerging mode could enable a new paradigm for social interaction through a combination of spontaneous and intense interactions. The unique shared-trip setting could be used as a venue for productive dialogue between passengers.
At JTL, our research in this cluster focuses on three levels of the social interactions present in shared mobility. At the individual level, our research focuses on how people perceive this unique transportation setting, and investigates how these attitudes affect individual use of this mode. At the interaction level, we consider the social dynamics at play in shared rides, considering how individuals contribute to the experiences of their fellow passengers. At the system level, our research focuses on how social aspects can be incorporated into the broader design of mobility sharing systems, through the inclusion preference-based matching, pricing, information dissemination, and social mixing. By combining research on these three levels, we aim to realize the societal benefits of interactions as a complement to the potential environmental and economic benefits of reduced congestion.
 |
Discriminatory Attitudes between Ridesharing Passengers, , Transportation, (2019) |
Prior studies have provided evidence of discrimination between drivers and passengers in the context of ridehailing. This paper extends prior research by investigating passenger-to-passenger discriminatory attitudes in the context of ridesharing. We conducted a survey of 1,110 TNC users in the US using Mechanical Turk, 76.5% of which have used UberPool or LyftLine, and estimated two structural equation models. The first model examines the influence of one’s demographic, social and economic... |
 |
Rider-To-Rider Discriminatory Attitudes and Ridesharing Behavior, , Transportation Research Part F, (2019) |
Using online survey data from N = 2,041 Uber and Lyft users in the United States collected in 2016 and 2018, this paper establishes the validity, reliability, and invariance of a measure of rider-to-rider race and social class discrimination. This measure is then incorporated into three structural models that investigate associations between rider-to-rider discriminatory attitudes and four aspects of ridesharing behavior. We find that rider-to-rider discriminatory attitudes do not... |
 |
Home-work Carpooling for Social Mixing, , Transportation, Washington, D.C., (2019) |
Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time,... |
 |
Mobility Sharing as a Preference Matching Problem, , IEEE Transactions on Intelligent Transportation Systems, (2018) |
Traffic congestion, dominated by single-occupancy vehicles, reflects not only transportation system inefficiency and negative externalities, but also a sociological state of human isolation. Advances in information and communication technology are enabling the growth of real-time ridesharing to improve system efficiency. While ridesharing algorithms optimize passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time or vehicle-distance... |
 |
Understanding the Usage of Stationless Bike Sharing in Singapore, , International Journal of Sustainable Transportation, (2018) |
A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted... |
 |
Discriminatory Attitudes Between Ridesharing Passengers, , Working paper, (2018) |
Prior studies have provided evidence of discrimination between drivers and passengers in the context of ridehailing. This paper extends prior research by investigating passenger-to-passenger discriminatory attitudes in the context of ridesharing. This paper 1) examines the variations in class- and race-related discriminatory attitudes between fellow passengers based on their demographic, social and economic characteristics; and 2) tests the impact of one's generic social dominance... |
 |
The Tradeoff Between Efficiency and Fellow Passenger Preference: a Preference-Based Ridesharing Model, , Transportation Research Board 96th Annual Conference, Washington, D.C., (2017) |
Advances in information technology are enabling the growth of real-time ridesharing—whereby passengers are paired up on car trips to improve system efficiency by using fewer cars. Lesser known, however, are the opportunities of shared mobility as a tool to foster and strengthen human interactions. The nature of shared car rides is impromptu, captive for a considerable duration, and remarkably more intimate, representing a unique juxtaposition of spontaneity and intensity. While ridesharing... |
 |
User Identification of and Attitude Toward Dynamic Ridesourcing Services, , Transportation Research Board 96th Annual Conference, Washington, D.C., (2017) |
Media coverage of ridesourcing services such as Uber and Lyft has described a rivalry between new technology and the established taxi industry. Individual users and non-users of ridesourcing may have more nuanced perspectives, but policymakers have had little guidance on how to best represent these interests. This study uses a standardized questionnaire distributed across the United States by an online survey company to understand individual attitudes toward Uber, Lyft, and ridesourcing... |
 |
Dynamic Pricing in Shared Mobility on Demand Service and its Social Impacts, , Working paper, (2017) |
We consider a daily-level profit maximization of a shared mobility on-demand (MoD) service with request-level control, and possible government interventions to improve system efficiency. We use discrete choice models to describe traveler behavior, apply the assortment and price optimization framework to model the request-level dynamics, and leverage insights from dynamic programming to develop daily-level optimization problem. We solve this problem by designing parametric rollout... |
 |
Humanizing Travel: How E-hail Apps Transform Stakeholder Relationships in Taxi Services, , Transportation Research Board 94th Annual Meeting, Washington, D.C., (2015) |
Efficiency benefits of the increasingly popular taxi-hailing (e-hailing) apps have been widely discussed - reduction of search time, convenience for passengers, and higher income for drivers. However the project proposes that there is a qualitative difference that e-hailing apps are engendering in the United States (U.S.) taxi industry above and beyond their quantitative impacts. E-hailing establishes direct connections between drivers and passengers, making ride requests person-to-person... |
TEAM MEMBERS
 MCP Student |
 MCP-MST Student |
 MST/MCP Student |
 MST |
 Visiting Scholar |
 MST/MCP Student |
 Assistant Professor at Tongji Univ. |
 Postdoctoral Associate |
 Research Fellow |
 Edward H. and Joyce Linde Associate Professor |