Social Mobility Sharing

Funding: MIT Institute of Data, System and Society (IDSS) Research Fund 2016

Traffic congestion, dominated by single-occupancy vehicles, reflects not only the inefficiency of the transportation system, but also a sociological state of human isolation. Advances in information and communications technology are enabling the growth of real-time ride sharing—whereby passengers are paired up on car trips with similar origins and destinations and proximate time windows—to improve system efficiency by moving more people in fewer cars. Lesser known, however, are the opportunities presented by shared mobility as a tool to foster and strengthen human interactions. In contrast to typical social interactions in public or private space (meeting rooms, streets, public squares, living rooms, etc.) the nature of shared car rides is impromptu, holds passengers "captive" for a considerable duration, and is remarkably more intimate than other sorts of transportation journeys, representing a unique juxtaposition of spontaneity and intensity. In these ways, it is distinct from mass transit modes such as buses and trains, where most passengers refrain from engaging with each other. Funded by the MIT Institute of Data, System and Society, this research examines the bi-directional connections between shared mobility as a transportation technology, and as an emerging mode of human interaction: how the unique temporal-spatial setting of ride sharing may (a) contribute to or preclude its growth in travel mode share, and (b) affect the quality and quantity of meaningful human interactions in the urban environment. 

Currently, passenger-matching algorithms are based upon the criterion of efficiency, namely maximizing occupancy and minimizing travel distance, while neglecting the sociological aspects of mobility sharing. In this IDSS research, we study mobility sharing from the perspective of human interactions, define and measure individuals’ preferences for fellow passengers, and develop matching algorithms that respect both the transportation network optimization and the individuals’ preferences (or lack of) for human interactions, in order to realize the societal benefits of interactions complementing the environmental and economic benefits of reduced congestion.

 

Discriminatory Attitudes between Ridesharing Passengers, Middleton, Scott, and Jinhua Zhao , Transportation, (Submitted)

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...

Mobility Sharing as a Preference Matching Problem, Hongmou Zhang, and Jinhua Zhao , 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, Yu Shen, Xiaohu Zhang, and Jinhua Zhao , 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...

Home-work Carpooling for Social Mixing, Librino, Federico, Renda Elena, Santi Paolo, Martelli Francesca, Resta Giovanni, Fabio Duarte, Ratti Carlo, and Jinhua Zhao , Working paper, (2018)

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,...

The Tradeoff Between Efficiency and Fellow Passenger Preference: a Preference-Based Ridesharing Model, Hongmou Zhang, and Jinhua Zhao , 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, Margo Dawes, and Jinhua Zhao , 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...

Rider-To-Rider Discriminatory Attitudes and Ridesharing Behavior, Joanna Moody, Middleton Scott, and Jinhua Zhao , Transportation Research Part F, (Submitted)

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...

Discriminatory Attitudes Between Ridesharing Passengers, Middleton, Scott, and Jinhua Zhao , 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...

Dynamic Pricing in Shared Mobility on Demand Service and its Social Impacts, Han Qiu, Ruimin Li, and Jinhua Zhao , 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, Corinna Li, and Jinhua Zhao , 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

Margo Dawes's picture
MCP Student
Corinna Li's picture
MCP-MST Student
Scott Middleton's picture
MST/MCP Student
Javier Morales Sarriera's picture
MST
M. Elena Renda's picture
Visiting Scholar
Adam Rosenfield's picture
MST/MCP Student
Yu Shen's picture
Assistant Professor at Tongji Univ.
Hongmou Zhang's picture
PhD Candidate
Hui Kong's picture
Research Fellow
Jinhua Zhao's picture
Edward H. and Joyce Linde Associate Professor