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With the rising of e-hailing services in urban areas, ride sharing is becoming a common mode of transportation. This paper presents a mathematical model to design an enhanced ridesharing system with meet points and users' preferable time windows. The introduction of meet points allows ridesharing operators to trade off the benefits of saving en-route delays and the cost of additional walking for some passengers to be collectively picked up or dropped off. This extension to the traditional door-to-door ridesharing problem brings more operation flexibility in urban areas (where potential requests may be densely distributed in neighborhood), and thus could achieve better system performance in terms of reducing the total travel time and increasing the served passengers. We design and implement a Tabu-based meta-heuristic algorithm to solve the proposed mixed integer linear program (MILP). To evaluate the validation and effectiveness of the proposed model and solution algorithm, several scenarios are designed and also resolved to optimality by CPLEX. Results demonstrate that (i) detailed route plan associated with passenger assignment to meet points can be obtained with en-route delay savings; (ii) as compared to CPLEX, the meta-heuristic algorithm bears the advantage of higher computation efficiency and produces good quality solutions with 8%~15% difference from the global optima; and (iii) introducing meet points to ridesharing system saves the total travel time by 2.7%-3.8% for small-scale ridesharing systems. More benefits are expected for ridesharing systems with large size of fleet. This study provides a new tool to efficiently operate the ridesharing system, particularly when the ride sharing vehicles are in short supply during peak hours. Traffic congestion mitigation will also be expected.

The present study aims to explore the factors that influence the utilization of intercity transportation services, with a specific focus on ridesharing and bus services, assessing the dimensions of reliability, comfort, responsiveness, safety, social interaction, driver empathy, and value. To this end, it uses responses from 110 users of ridesharing, and 200 users of conventional intercity bus services. Using a non-linear method, Penalty-reward Contrast Analysis, the results show that the investigated dimensions have a distinct influence on the propensity to continue using each of these services, explaining 32% of using conventional road transport, and 65% of ridesharing. We confirmed the non-linearity between customer perception of the surveyed dimensions' performance and their propensity to use the service, with differences and similarities between bus and ridesharing services. A low performance of the dimensions of safety and comfort, and a high performance of reliability, impact both services. But there are significant differences in the impact of perceived value, responsiveness, driver empathy, and social interaction. These findings allow service providers to better understand customers' needs and provide better service.

Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations.

Purpose The purpose of this paper is to empirically test the antecedents and outcome of perceived value and trust toward on-demand ridesharing services (ODRS). The antecedents are perceived innovativeness, perceived personalization, perceived usefulness of rating system and service personal values. The outcome is the continuance intention toward ODRS. This study also aims to uncover the mediating role of trust and the moderating role of technology readiness. Design/methodology/approach The ODRS considered in this research are Grab and Uber in the context of Malaysia. A questionnaire was constructed, and responses were obtained from 280 Malaysian consumers who have experienced ODRS. The authors tested the framework using partial least square structural equation modeling technique. Findings The result indicates several significant relationships: perceived personalization, perceived usefulness of rating system and service personal values significantly influence perceived value and trust; trust mediates the relationships between perceived personalization, perceived usefulness of rating system, service personal values and perceived value; perceived value significantly influences continuance intention; and technology readiness moderates the relationship between perceived personalization and perceived value. Originality/value The current study adds significantly to the body of knowledge about ODRS by examining the direct determinants of trust and perceived value, and exhibiting how trust mediates the mechanism. This study also illustrates the interplay of moderator (technology readiness) and perceived value.

Ridehailing services such as Uber have been promoted as viable interventions for curbing alcohol-involved driving fatalities. However, evidence of ridehailing's impact has been mixed, with some studies finding no association but others finding either an increase or a decrease in fatalities. We contribute to this literature by examining more recent years of data, which capture a period during which Uber ridership has grown substantially and alcohol-involved fatalities have increased. Furthermore, we test whether the relationship between Uber availability and traffic fatalities depends on local characteristics. We employ multivariate regression models to test the association between Uber availability and total, alcohol-involved, and weekend and holiday-specific traffic fatalities in the 100 most populated metropolitan areas in the United States between 2009 and 2017. We find that Uber availability is not associated with changes in total, alcohol-involved, and weekend and holiday-specific traffic fatalities in aggregate, yet it is associated with increased traffic fatalities in urban, densely populated counties.

The first step back, he said, was going to graduate school on the Gl Bill at the University of Arkansas at Fayetteville, where he heard two female students discussing Uber. Shelby, who has moved from Fayetteville to Bentonville, now has his master's degree and has fielded offers to become an athletic trainer, but with earnings of up to $1,200 in a good week, he says he's sticking with Über.

Nowadays, ride-hailing services have been established as a part of the urban transportation. Their arrival has remade the profit structure and resulted in the unbalance of interest in taxi market. Here, we establish the service models of taxis, carpooling, and car-hailing under \"Internet +\" from the perspective of profit margins, to perform a comparative analysis among the different services. First, Profit margins are generally higher for short trips than for long trips, though empty cruise fee to a certain degree make up for the driver's decreased profit margins. Second, the profit margin for carpooling is roughly 1.85 times that of ride-hailing, and 1.75 times that of taxis. This shows that the sharing economy has a certain advantage. Third, Profit margins are higher and fluctuations are lower on non-work days than on work days. At last, Profit margins are roughly 1.3 times higher on non-congested roads than on congested roads. The reduced profitability on congested roads makes it even harder to catch a ride during rush hours and on congested roads. We suggest that the relevant departments make appropriate efforts to make it more attractive for drivers to take on passengers during rush hours and on congested roads, and promote the sharing in the taxi market.

Digital data have become a form of “objectivation”, which affect how we construct social knowledge and organize social space (Couldry & Hepp, 2017). The workplace is one sphere that is increasingly datafied. This study explores how Uber drivers, a form of digitally-enabled service workers, contribute to the normalization of the social production of space through their interpretative practices of digital data in an online forum. Drawing on Uber’s corporate discourse and an Uber driver online forum, we analyze two facets of the Uber app and drivers’ mediated experiences: (1) the quantification and discipline of drivers’ performance through Uber’s rating system and (2) the coordination of spatial movement through location-related metrics. We argue that the underlying workings of the Uber app premediate expectations of service encounters and spatial movement. Uber drivers meanwhile develop practices which respond to and circumvent their own data contributions to the system. Drivers’ practices, we argue, are largely in compliance with the calculative logics set by Uber. The article addresses implications of Uber drivers’ practices for the reproduction of social space and power-relations in digitally-enabled service work and the gig economy.

Autonomous vehicles (AVs) have generated excitement for the future of transportation. Public transit agencies and companies (i.e., Uber) have begun developing shared autonomous transportation services. Most AV surveys focus on public opinion of perceived benefits and concerns of AVs but are not directly tied to field implementation of AVs. Experience and exposure to new technology affect adults’ perceptions and level of technology acceptance. As such, the Autonomous RideShare Services Survey (ARSSS) was developed to assess adults’ perceptions of AVs before and after being exposed to AVs. Face validity and content validity were established via focus groups and subject-matter experts (CVI = 0.95). Adults in the U.S. (N = 553) completed the ARSSS, and a subsample (N = 100) completed the survey again after two weeks. An exploratory and confirmatory factor analysis demonstrated that the ARSSS consists of three factors that can be used to reliably quantify users’ perceptions of AVs: (a) Intention to Use, Trust, and Safety (r = 0.85, p < 0.001, ICC = 0.99); (b) Potential Benefits (r = 0.70, p < 0.001, ICC = 0.97); and (c) Accessibility (r = 0.78, p < 0.001, ICC = 0.96) of AVs. These are key factors in predicting intention to use and acceptance of AVs. Results from the ARSSS may inform the acceptance among users of these AV technologies.

The popular ride-sharing service Uber introduced a program to provide transportation to and from medical appointments, but the complexities of health care could make it a more difficult sector to disrupt than other industries. Uber Health, so far available only in the United States, allows doctors and hospitals to book rides for patients to attend medical appointments. It has been tested by more than 100 US health facilities, some of which pay for the service to avoid the downstream costs of missed health appointments, particularly in primary care. According to one study, the traditional Uber service already diverts low-risk patients from using ambulances. The authors found that the introduction of Uber to a city reduces overall per capita ambulance usage by 7%.