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.

  • Evaluating the travel impacts of a shared mobility system for remote workers

    Transportation Research Part D
    (
    2023
    )

    Given the rapid rise of remote work, there is an opportunity for new shared mobility services designed to meet the needs of passengers with multiple possible work locations. This paper develops a new optimization model to enable shared mobility systems to match drivers and passengers when passengers have flexible destinations. Constraints representing employer policies, such as mandatory co-location of colleagues and limited capacity of satellite offices are introduced in order to explore the impact of employer remote work policies on travel demand. A case study using historical demand data demonstrates that incorporating flexible work locations can increase ride-pooling participation by up to 6.7% and reduce vehicle-kilometers travelled by 4.9%. Outcomes are found to be significantly affected by employer policies. The implications of the results for shared mobility business models, employer remote work plans and local transportation policy are discussed.

  • ICT’s Impacts on Ride-hailing Use and Individual Travel

    Transportation Research Part A
    (
    2020
    )

    Previous studies have explored the relationships between an individual’s use of information and communication technology (ICT) and their travel. However, these studies often focus on one specific type of travel and have not considered new forms of mobility, such as ride-hailing, that are enabled by greater ICT penetration. This paper focuses on how ICT use impacts an individual’s self-reported travel behavior—including total number of trips, personal miles traveled (PMT), and vehicle miles traveled (VMT) in a typical travel day—and ride-hailing use in the past month. Specifically, we investigate whether substitution or complementarity dominates the relationships between ICT use and an individual’s net travel; how ICT impacts individual ride-hailing adoption and frequency of use; and how ride-hailing use is associated with an individual’s overall travel behavior. Using data from the 2017 U.S. National Household Travel Survey (NHTS), we estimate a structural equation model that includes a robust set of individual, household, built environment, and travel characteristics, frequency of ICT use, and a hurdle model (two-part regression) of the adoption and frequency of ride-hailing use. Results reveal that greater ICT is not significantly related to the total number of trips that an individual takes, but it does significantly predict higher PMT and VMT. Greater ICT use is positively and substantively correlated with whether or not the individual has used ride-hailing in the past 30 days, but has no significant relationship with the frequency of ride-hailing use with this bounded outcome being controlled for. We further find that an individual’s ride-hailing use has a small negative correlation with their PMT and VMT after controlling for other common factors. Our results indicate the importance of future research examining the mechanisms by which ICT use increases the distance individuals travel and the role that new ICT-enabled modes, such as ride- hailing, play in changing these mechanisms at both the individual and system levels.

  • Adoption of Exclusive and Pooled TNC Services in Singapore and the U.S.

    Transportation Research Board 99th Annual Meeting
    Washington, D.C.
    ,
    (
    2020
    )

    On-demand mobility services provided by transport network companies (TNCs) have experienced significant growth in adoption and diversification of services in major metropolitan cities around the world. This study synthesizes information on who uses TNC services, who (among these TNC users) are more likely to pool their trips, and what modes these services are replacing in the (metropolitan) U.S. and in Singapore. We find that the sociodemographics of TNC users in Singapore are similar to those in the U.S.: younger, highly educated, and higher income individuals are more likely to have used TNC services. Furthermore, younger individuals from households that do not own a car are more likely to have pooled in Singapore, while employment is another important predictor in the U.S. Concerning mode substitution, we find that, while TNC trips in the U.S. primarily induce additional trips or replace trips by public and nonmotorized transport, in Singapore they primarily replace personal/private vehicle trips. In Singapore, we explore mode substitution by exclusive and pooled TNC services separately, finding that pooled trips draw more from public and nonmotorized transport, while exclusive trips replace more personal/private vehicle trips. These results suggest that people in Singapore view exclusive and pooled TNC services as distinct travel options that may be more closely related to other private or public transport, respectively. Differences between the U.S. and Singapore highlight the importance of accounting for local context and suggests that the quality of all travel alternatives in the urban area will affect the mode substitution of TNC trips.

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    Is Ridesourcing More Efficient than Taxis?

    Applied Geography
    (
    2020
    )

    Ridesourcing services such as Uber, Lyft, and DiDi are purported to be more efficient than traditional taxis because they can match passengers with drivers more effectively. Previous studies have compared the efficiency of ridesourcing and taxis in several cities. However, gaps still exist regarding the measurement and comparison between the two modes, and the reasons for the higher efficiency of ridesourcing have not been empirically examined. This paper aims to measure, compare, and explain the efficiency and variation of DiDi and taxis. The case study is conducted in Chengdu, China. We use Vehicle occupancy rate (VOR) as the efficiency measure–the percentage of time that a vehicle is occupied by a fare-paying passenger. We measure the VORs of DiDi and taxis and their spatial and temporal variations using the trip origin-destination data from DiDi and the trajectory data for taxis. The VOR patterns between DiDi and taxis are compared and contrasted, and the underlying factors that affect the difference are examined: more efficient driver-rider matching algorithm, larger scale of ridesouricng services, and the number of taxi trips per capita. Results show that the overall VOR of DiDi is six percentage points higher than taxis on the weekday and 12 percentage points higher on the weekend. However, the VOR of taxis is slightly higher than DiDi during the weekday morning peak in downtown areas. Regression models reveal that the more efficient matching and the greater scale of DiDi drivers enlarge the VOR gap between DiDi and taxis, while the number of taxi trips per capita reduce the gap. The findings have implications for both business operation and transportation policies in terms of service design, service coordination, and location-specific regulations.

  • Induction_bus_and_car

    Adoption of Exclusive and Pooled TNC Services in Singapore and the U.S.

    ASCE Journal of Transportation Engineering, Part A: Systems
    146
    ,
    (
    2020
    )

    On-demand mobility services provided by transport network companies (TNCs) have experienced significant growth in their adoption and diversification of services in major metropolitan cities around the world. This study analyzed primary data from Singapore to explore the sociodemographics of TNC users and determine who among TNC users is more likely to pool their trips and what modes these services are replacing. We compared these results with a comprehensive literature review of similar studies of TNC users in the metropolitan US. We found that the sociodemographics of TNC users in general are similar in Singapore and the US: younger, highly educated, and higher income individuals are more likely to have used TNC services. On the other hand, when differentiating by type of TNC service, we found that younger individuals from households that do not own a car are more likely to have pooled in Singapore, whereas employment is an important predictor in the US. We also found differences in mode substitution; whereas TNC trips in the US primarily induce additional trips or replace trips by public and non-motorized transport, in Singapore they primarily replace personal/private vehicle trips. In Singapore, we explored mode substitution by exclusive and pooled TNC services separately, and found that pooled trips draw more from public and nonmotorized transport, whereas exclusive trips replace more personal/private vehicle trips. These results suggest that people in Singapore view exclusive and pooled TNC services as distinct travel options that may be more closely related to other private or public transport, respectively. Differences between Singapore and the US highlight the importance of accounting for local context and suggest that the quality of all travel alternatives in the urban area will affect the mode substitution of TNC trips.

  • The Price of Privacy Control in Mobility Sharing

    Journal of Urban Technology
    (
    2020
    )

    One of the main features in mobility sharing applications is the exposure of personal data provided to the system. Transportation and location data can reveal personal habits, preferences, and behaviors, and riders could be keen not to share the exact location of their origin and/or destination. But what is the price of privacy in terms of decreased efficiency of the mobility sharing system? In this paper, for the first time, we address the privacy issues under this point of view and show how location privacy-preserving techniques could affect the performance of mobility sharing applications, in terms of both System Efficiency and Quality of Service. To this extent, we first apply different data-masking techniques to anonymize geographical information and then compare the performance of shareability networks-based trip matching algorithms for ride-sharing, applied to the real data and to the privacy-preserving data. The goal of the paper is to evaluate the performance of mobility sharing privacy-preserving systems and to shed light on the trade-off between data privacy and its costs. The results show that the total traveled distance increase due to the introduction of data privacy could be bounded if users are willing to spend (or "pay") for more time in order to share a trip, meaning that data location privacy impacts both efficiency and quality of service.

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    How does Ridesourcing Substitute for Public Transit? A geospatial perspective in Chengdu, China

    Journal of Transport Geography
    (
    2020
    )

    The explosive growth of ridesourcing services has stimulated a debate on whether they represent a net substitute for or a complement to public transit. Among the empirical evidence that supports discussion of the net effect at the city level, analysis at the disaggregated level from a geospatial perspective is lacking. It remains unexplored the spatiotemporal pattern of ridesourcing’s effect on public transit, and the factors that impact the effect. Using DiDi Chuxing data in Chengdu, China, this paper develops a three-level structure to recognize the potential substitution or complementary effects of ridesourcing on public transit. Furthermore, this paper investigates the effects through exploratory spatiotemporal data analysis and examines the factors influencing the degree of substitution via linear, spatial autoregressive, and zero-inflated beta regression models. The results show that 33.1% of DiDi trips have the potential to substitute for public transit. The substitution rate is higher during the day (8:00–18:00), and the trend follows changes in public transit coverage. The substitution effect is more exhibited in the city center and the areas covered by the subway, while the complementary effect is more exhibited in suburban areas as public transit has poor coverage. Further examination of the factors impacting the relationship indicates that housing price is positively associated with the substitution rate, and distance to the nearest subway station has a negative association with it, while the effects of most built environment factors become insignificant in zero-inflated beta regression. Based on these findings, policy implications are drawn regarding the partnership between transit agencies and ridesourcing companies, the spatially differentiated policies in the central and suburban areas, and the potential problems in providing ridesourcing service to the economically disadvantaged population. 

  • Induction_bus_and_car

    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 significantly predict whether a TNC user has used a ridesharing service (such as uberPOOL or Lyft Line). However, among those who have used ridesharing services before, rider-to-rider discriminatory attitudes are strongly negatively predictive of an individual's level of satisfaction with the sharing option, and marginally negatively predictive of an individual's percentage of shared TNC trips. Furthermore, among those who have not yet used ridesharing services, rider-to-rider discriminatory attitudes are strongly negatively predictive of willingness to consider using uberPOOL or Lyft Line in the future. These associations between rider-to-rider discriminatory attitudes and multiple aspects of ridesharing behavior suggest that such attitudes may persistently discourage sharing. In fact, we find no statistically significant difference in rider-to-rider discrimination or in its relations with ridesharing behavior across the two survey years. Further research is required to identify strategies for addressing discriminatory attitudes in the ridesharing context and overcoming reluctance to sharing.

  • Gender, Social Interaction, and Mobility Sharing

    Transportation Research Board 98th Annual Meeting
    Washington, D.C.
    ,
    (
    2019
    )

    In this paper we answered three questions: 1) Does social interaction in mobility sharing impact the usage and satisfaction level with it? 2) Is there gender deference in considering social interaction as motivation or deterrent for mobility sharing? 3) Is there gender difference in the usage and satisfaction with mobility sharing services. With a survey (n=997) in the U.S. cities where Uber or Lyft is available, we combined data of sociodemographic variables, social interaction indicators, and the usage and satisfaction with mobility sharing services. Using factor analysis and structural equation models we found that 1) positive and negative social interactions both have significant impacts on the usage and satisfaction levels of mobility sharing services, with the former increasing usage and satisfaction, and the latter one reducing them; 2) there is significant gender difference in the agreement on considering social interaction and lack of fellow passenger information as motivation or deterrents for using mobility sharing, but the gender difference is smaller among users than non-users; and 3) based on the previous two findings, there is significant indirect gender effect on the usage and satisfaction with mobility sharing. Nonetheless, this effect is canceled by the direct effect of gender on mobility sharing usage and satisfaction, and the total effect is insignificant.

  • Association of Rider-to-Rider Discriminatory Attitudes and Ridesharing Behavior

    Transportation Research Board 98th Annual Meeting
    Washington, D.C.
    ,
    (
    2019
    )

    Using electronic 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 in ridesharing. We then incorporate this measure into three structural models to investigate associations between rider-to-rider discriminatory attitudes and ridesharing behavior. We find that discriminatory attitudes in the ridesharing context do not significantly predict whether a TNC user has used a ridesharing service, suggesting that this decision is dominated by utilitarian considerations rather than attitudes. However, among those who have used ridesharing services (such as uberPOOL or Lyft Line) before, we find that discriminatory attitudes in ridesharing are marginally negatively predictive of an individual's percentage of shared trips and strongly negatively predictive of an individual's level of satisfaction with the sharing option. Finally, among those who have not yet used dynamic ridesharing services, we find discriminatory attitudes in ridesharing are strongly negatively predictive of willingness to consider using uberPOOL or Lyft Line in the future. We find no statistically significant difference in these relations across the two survey years. These findings indicate a significant association between rider-to-rider discriminatory attitudes and ridesharing behavior, and suggest that such attitudes may persistently discourage sharing. Further research is required to identify strategies for addressing discriminatory attitudes in the ridesharing context and overcoming reluctance to share.

  • Induction_bus_and_car

    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 characteristics on discriminatory attitudes toward fellow passengers in ridesharing, and how such influence varies by the targets of discrimination, i.e., race and class. The second model examines the influence of one's generic social dominance orientation on discriminatory attitudes in the ridesharing context. We find that discriminatory attitudes toward fellow passengers of differing class and race in the shared ride are positively correlated with respondents that are male or are women with children. A respondent's race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences). The same is true of respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election. Conversely, higher-income respondents appear more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences. We also find that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes. Further research is required to identify policy interventions that mitigate such attitudes in the context of ridesharing.

  • Induction_bus_and_car

    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, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social benefits should be traded off with transportation efficiency. By incorporating traveler’s social characteristics into a recently introduced network-based approach to model ride-sharing opportunities, we define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally efficient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.

  • Induction_bus_and_car

    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 orientation on discriminatory attitudes in the ridesharing context. To that end, this paper uses data from a survey of 1110 TNC users to create two structural equation models. We find that discriminatory attitudes toward passengers of differing class and race in the shared ride are positively correlated with respondents that are male and older, as well as women with children. Black respondents are less likely to hold discriminatory attitudes with regard to race, while white respondents in majority white counties are more likely to hold such attitudes. Higher-income respondents appear marginally more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences. We find that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes. Further research is required to determine whether these platforms exacerbate or mitigate such attitudes.

  • Induction_bus_and_car

    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 spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.

  • Induction_bus_and_car

    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 traveled), they do not explicitly consider passengers' preference for each other as the matching objective. We propose a preference-based passenger matching model, formulating ridesharing as a maximum stable matching problem. We illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1,000 randomly generated preference orders, and the other considering five sets of group-based preference orders. In both scenarios, compared with efficiency-based matching models, preference-based matching improves the average ranking of paired fellow passenger to the near-top position of people's preference orders with only a small efficiency loss at the individual level, and a moderate loss at the aggregate level. The near-top-ranking results fall in a narrow range even with the random variance of passenger preference as inputs.

    Cite as: Zhang, Hongmou, and Jinhua Zhao. 2018. “Mobility Sharing as a Preference Matching Problem.” IEEE Transactions on Intelligent Transportation Systems.

  • Induction_bus_and_car

    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 policy and utilizing Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to search for optimal parameter.

    We evaluate our algorithm with a case study in Langfang, China. We develop a simulation system for both the MoD service operations and the city transportation dynamics, and design scenarios with varying supply size, demand size, congestion level, and fare structure. In this case study, the optimal pricing strategy generates considerably more profit than basic strategies (those without assortment or dynamic pricing) and myopic strategies (dynamic pricing at each request level), but it increases the congestion level and reduces the capacity in the transportation system. We also compare two policy interventions to improve the system efficiency, i.e. congestion based taxation and demand based taxation, and find that the congestion based taxation is more effective. 

  • Induction_bus_and_car

    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 technology in general. The survey asks respondents if they identify as users or non-users of ridesourcing, why or why not, how they rank Uber and Lyft among their other travel modes, and their attitudes toward the companies and toward the technology in general, among other questions. The survey returned 394 completed questionnaires from the most populous 15 metropolitan statistical areas in the U.S. with a response rate of 27%. Analysis of the results includes descriptive statistics, bivariate correlation analyses of relationships between variables, and logistic regressions to identify factors that impact user identification and attitude. The findings indicate that about 70% of respondents use some form of ridesourcing, mostly for special-purpose trips such as avoiding driving while intoxicated and getting to and from the airport. There are relationships between transportation needs and user identification and attitude, but demographics are the best predictor of user identification, which in turn predicts attitude, which can predict individuals’ policy preferences. The study suggests potential for policymakers to leverage constituent perspectives to change aspects of ridesourcing that have low public approval.

  • Induction_bus_and_car

    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 services optimize the matching of fellow passengers based on efficiency criteria, like maximal paired trips, or minimal VMT, they do not consider passengers’ preference for each other—or only use it as a restriction—especially for the preference from sociodemographic characteristics, like gender. We propose a preference-based matching model, which optimizes fellow passenger pairing by using the most stably favorable fellow passengers as a system objective, and evaluate the tradeoff between it and efficiency-based matching models. In the model, we implement two types of synthetic preference: 1) random preference, to show efficacy of the model and the range of tradeoff between different matching schemes, and 2) group-based preference of five scenarios, to illustrate how preference coming from sociodemographic characteristics lead to different matching outcomes. We use the taxi trips of Manhattan to put this model in real-world scenarios. The results of the paper show that compared to the matchings under different efficiency-based optimization goals, the preference-based model can increase the average ranking of paired fellow passenger to the top of preference lists at the compensation of only a moderate amount of efficiency loss. The model can be used for ridesharing service design, and as a reference for policy makers of ridesharing regulation.

  • Induction_bus_and_car

    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 interactions in contrast with the prior impersonal one-time encountering. These connections, in turn, enable and generate more humanly sensible communications between both parties. At the neighborhood scale, the mutual trust between drivers and passengers empowers marginalized neighborhoods, which were often seen as dangerous and consequently underserved by taxi, to re-connect their residents to the city at large. This project aims to diagnose the stakeholder dynamics in six major U.S. taxi markets and identify the bottlenecks and weaknesses in the relationship structure; examine how e-hailing is poised to disrupt the status quo and transform these relationships; and analyze the implications on the quality of service, drivers' work environment, and the neighborhoods that they serve. The primary method of this project will be in-depth stakeholder interviews with key stakeholders in the industry – namely, taxi drivers, passengers, regulators, and app developers. The US taxi industry is at a crossroad. Regulators across the country are debating the fate of e-hailing at the moment – whether to allow it, regulate it, or promote it. By documenting the potential of e-hail apps in fostering a more humanizing and service-oriented taxi market, this research provides regulators with concrete evidence in their policy decisions, facilitates developers to improve their apps, and enlightens transportation professionals and urban planners on the deeper and qualitative impact of the new technology on accessible and livable communities.

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