Toward using social media to support ridesharing services: challenges and opportunities

Ridesharing has been attracting increasing attention from both academia and industry. One of the challenges posed by the study of ridesharing is to identify the most valuable information for improving the ridesharing decisions taken by participants. Another challenge is to use harvesting techniques to extract specific types of travel-related information. Many methods have been developed by the community in order to solve these issues. However, due to a lack of information sharing between different transit authorities and the difficulty of identifying subjective perceptions of the experience of ridesharing, understanding and evaluating how social media data might support or obstruct goals for mobility, safety and environmental sustainability in ridesharing is a difficult task. In this survey, we first analyze the literature on ridesharing with a focus on the properties and model of service, and introduce a framework to describe the major components required for a ridesharing service. Then, we illustrate the potential value of information extracted from social media and present the rationale for harvesting travel-related data. Finally, we detail some possible directions and different approaches for using social media data, and highlight their assets and drawbacks.     

A traffic assignment model for a ridesharing transportation market

A nascent ridesharing industry is being enabled by new communication technologies and motivated by the many possible benefits, such as reduction in travel cost, pollution, and congestion. Understanding the complex relations between ridesharing and traffic congestion is a critical step in the evaluation of a ridesharing enterprise or of the convenience of regulatory policies or incentives to promote ridesharing. In this work, we propose a new traffic assignment model that explicitly represents ridesharing as a mode of transportation. The objective is to analyze how ridesharing impacts traffic congestion, how people can be motivated to participate in ridesharing, and, conversely, how congestion influences ridesharing, including ridesharing prices and the number of drivers and passengers. This model is built by combining a ridesharing market model with a classic elastic demand Wardrop traffic equilibrium model. Our computational results show that (i) the ridesharing base price influences the congestion level, (ii) within a certain price range, an increase in price may reduce the traffic congestion, and (iii) the utilization of ridesharing increases as the congestion increases. Copyright © 2014 John Wiley & Sons, Ltd.     

Impact of high occupancy vehicle lanes on carpooling behavior

High occupancy vehicle lanes have become an integral part of regional transportation planning. Their purpose is to increase ridesharing by offering a travel time advantage to multiple occupant vehicles. This paper examines the extent to which an HOV facility increases ridesharing. Using data from the Route 55 HOV facility in Orange Country, California, changes in the carpooling rate on Route 55 are compared to that of a control group of freeway commuters. The analysis shows that the carpooling rate among peak period commuters, and particularly those who use the entire length of the facility, has increased. However, there has been no significant increase in ridesharing among the entire population of Route 55 commuters. Results suggest that barriers to increased ridesharing are formidable, that travel time savings must be large in order to attract new carpoolers, and that further increases in capooling will likely require development of extensive HOV lane systems.     

“Not just a taxi”? For-profit ridesharing,driver strategies,and VMT

The spread of GPS-based location services using smartphone applications has led to the rapid growth of new startups offering smartphone-enabled dispatch service for taxicabs, limousines, and ridesharing vehicles. This change in communicative technology has been accompanied by the creation of new categories of car service, particularly as drivers of limousines and private vehicles use the apps to provide on-demand service of a kind previously reserved for taxicabs. One of the most controversial new models of car service is for-profit ridesharing, which combines the for-profit model of taxi service with the overall traffic reduction goals of ridesharing. A preliminary attempt is here made at understanding how for-profit ridesharing compares to traditional taxicab and ridesharing models. Ethnographic interviews are drawn on to illustrate the range of motivations and strategies used by for-profit ridesharing drivers in San Francisco, California as they make use of the service. A range of driver strategies is identified, ranging from incidental, to part-time, to full-time driving. This makes possible a provisional account of the potential ecological impacts of the spread of this model of car service, based on the concept of taxicab efficiency, conceived as the ratio of shared versus unshared miles driven.     

Meeting points in ridesharing: A privacy-preserving approach

Nowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pick-up and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current’s ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data.In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory.     

Ridesharing in North America: Past,Present, and Future

Since the late 1990s, numerous ridematching programmes have integrated the Internet, mobile phones, and social networking into their services. Online ridematching systems are employing a range of new strategies to create “critical mass”: (1) regional and large employer partnerships, (2) financial incentives, (3) social networking to younger populations, and (4) real-time ridematching services that employ “smartphones” and automated ridematching software. Enhanced casual carpooling approaches, which focus on “meeting places”, are also being explored. Today, ridesharing represents approximately 8–11% of the transportation modal share in Canada and the USA, respectively. There are approximately 638 ridematching programmes in North America. Ridesharing's evolution can be categorized into five phases: (1) World War II car-sharing (or carpooling) clubs; (2) major responses to the 1970s energy crises; (3) early organized ridesharing schemes; (4) reliable ridesharing systems; and (5) technology-enabled ridematching. While ridesharing's future growth and direction are uncertain, the next decade is likely to include greater interoperability among services, technology integration, and stronger policy support. In light of growing concerns about climate change, congestion, and oil dependency, more research is needed to better understand ridesharing's impacts on infrastructure, congestion, and energy/emissions.     

Pricing scheme design of ridesharing program in morning commute problem

This paper examines the dynamic user equilibrium of the morning commute problem in the presence of ridesharing program. Commuters simultaneously choose departure time from home and commute mode among three roles: solo driver, ridesharing driver, and ridesharing rider. Considering the congestion evolution over time, we propose a time-varying compensation scheme to maintain a positive ridesharing ridership at user equilibrium. To match the demand and the supply of ridesharing service over time, the compensation scheme should be set according to the inconvenience cost functions and the out-of-pocket cost functions. When the price charged per time unit is higher than the inconvenience cost per time unit perceived by the ridesharing drivers, the ridesharing participants will travel at the center of peak hours and solo drivers will commute at the two tails. Within the feasible region with positive ridership, the ridesharing program can reduce the congestion and all the commuters will be better off. To support system optimum (SO), we derive a time-varying toll combined with a flat ridesharing price from eliminating queuing delay. Under SO toll, the ridesharing program can attract more participants and have an enlarged feasible region. This reveals that the commuters are more tolerant to the inconvenience caused by sharing a ride at SO because of the lower travel time. Compared with no-toll equilibrium, both overall congestion and individual travel cost are further reduced at SO.     

Flexible parking requirements for office developments: New support for public parking and ridesharing

The author examines new, flexible parking requirements in several U.S. cities and Canada. Flexible requirements provide relaxations in zoning code specifications for off-street parking in return for developer commitments to ridesharing measures, support of transit or in-lieu fees for public parking. Issues examined include whether developers take advantage of flexible requirements, whether agreements with developers lead to agreed-upon action, and whether actions lead to desired results. Based on experiences of several cities, implications and cautions are drawn for local planners. The first caution relates to cities using flexible requirements to collect in-lieu fees in support of public parking. Where cities delay providing parking, inflation may make it difficult to provide the desired parking supply. Or, developers may not choose to pay the fee, such as in the case where cities grant relaxations for other measures more attractive than paying the fees.Other implications relate to flexible requirements in support of ridesharing or transit. Developers will not necessarily take advantage of relaxations tied to support of these modes. Generally, developers will take the least-cost ways to gain relaxations. They may choose to provide the parking where transit or ridesharing measures are too costly or complex. Or, they may take relaxations easier to obtain than those for ridesharing and transit. Finally, even where developers do agree to ridesharing measures, the measures may not be effective. On the other hand, developer-sponsored actions have proven effective in some cities where tight or expensive parking prevails, or where neighborhood residents have organized against office commuters parking on neighborhood streets, or where other economic or community variables favor ridesharing and transit.     

Fuel saving and ridesharing in the US: Motivations,limitations, and opportunities

Ridesharing can reduce the fuel consumed in noncommercial passenger highway vehicles by grouping individuals into fewer vehicles and reducing the number of miles that vehicles must travel. We estimate the potential fuel savings that could result from an increase in ridesharing in the US. If no additional travel is required to pick up passengers, adding one additional passenger for every 100 vehicles would reduce annual fuel consumption by 0.80–0.82 billion gallons of gasoline per year; if one passenger were added in every 10 vehicles, the potential savings would be 7.54–7.74 billion gallons per year. However, ridesharing may require extra travel to pick up additional passengers, which can reduce and possibly eliminate potential fuel savings. The tradeoff between saving fuel and spending time to pick up additional passengers is investigated, finding that, on average, ridesharing may not be attractive to travelers, but can be made more attractive by increasing per-vehicle-trip costs such as parking and tolls.     

Network-oriented household activity pattern problem for system optimization

The recently emerging trend of self-driving vehicles and information sharing technologies, made available by private technology vendors, starts creating a revolutionary paradigm shift in the coming years for traveler mobility applications. By considering a deterministic traveler decision making framework at the household level in congested transportation networks, this paper aims to address the challenges of how to optimally schedule individuals’ daily travel patterns under the complex activity constraints and interactions. We reformulate two special cases of household activity pattern problem (HAPP) through a high-dimensional network construct, and offer a systematic comparison with the classical mathematical programming models proposed by Recker (1995). Furthermore, we consider the tight road capacity constraint as another special case of HAPP to model complex interactions between multiple household activity scheduling decisions, and this attempt offers another household-based framework for linking activity-based model (ABM) and dynamic traffic assignment (DTA) tools. Through embedding temporal and spatial relations among household members, vehicles and mandatory/optional activities in an integrated space-time-state network, we develop two 0–1 integer linear programming models that can seamlessly incorporate constraints for a number of key decisions related to vehicle selection, activity performing and ridesharing patterns under congested networks. The well-structured network models can be directly solved by standard optimization solvers, and further converted to a set of time-dependent state-dependent least cost path-finding problems through Lagrangian relaxation, which permit the use of computationally efficient algorithms on large-scale high-fidelity transportation networks.     

The ambivalence of ridesharing

Ridesharing is quite a popular topic of discussion among transport authority personnel. It is perceived to be a viable alternative to classical modes of transportation, and receives a great deal of political support from transport planners. However, not much objective information is available on ridesharing behaviors. We use travel survey data to study the evolution of the ridesharing market in an urban area. Our study is based on data from four large-scale OD surveys conducted in the Greater Montreal Area (1987, 1993, 1998 and 2003). In the latest survey conducted in Montreal, car passengers were asked to identify the driver who gave them the opportunity to travel in this way. Their answers were classified according to the type of driver; for instance, a member of their household, a neighbor or a co-worker. We use this information to calibrate a model matching car passengers and car drivers belonging to the same household. This will be referred to as IHHR (intra-household ridesharing). Preliminary results reveal that approximately 70% of all trips made by car passengers are the result of IHHR. Furthermore, around 15% of those trips are questionable, in that they were exclusively generated for another individual’s purposes, consequently generating an additional trip for the journey back home. Moreover, this percentage increased over time. Objective data regarding ridesharing and its evolution in an urban area will undoubtedly help decision makers gain a clearer profile of this means of travel and help to realign attitudes on the issue.

Shared ride services in North America: definitions,impacts, and the future of pooling

ABSTRACT

Shared ride services allow riders to share a ride to a common destination. They include ridesharing (carpooling and vanpooling); ridesplitting (a pooled version of ridesourcing/transportation network companies); taxi sharing; and microtransit. In recent years, growth of Internet-enabled wireless technologies, global satellite systems, and cloud computing - coupled with data sharing – are causing people to increase their use of mobile applications to share a ride. Some shared ride services, such as carpooling and vanpooling, can provide transportation, infrastructure, environmental, and social benefits. This paper reviews common shared ride service models, definitions, and summarises existing North American impact studies. Additionally, we explore the convergence of shared mobility; electrification; and automation, including the potential impacts of shared automated vehicle (SAV) systems. While SAV impacts remain uncertain, many practitioners and academic research predict higher efficiency, affordability, and lower greenhouse gas emissions. The impacts of SAVs will likely depend on the number of personally owned automated vehicles; types of sharing (concurrent or sequential); and the future modal split among public transit, shared fleets, and pooled rides. We conclude the paper with recommendations for local governments and public agencies to help in managing the transition to highly automated vehicles and encouraging higher occupancy modes.     

Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes

Emerging autonomous vehicles (AVs) and shared mobility systems per se will transform urban passenger transportation. Coupled together, shared AVs (SAVs) can facilitate widespread use of shared mobility services by providing flexible public travel modes comparable to private AV. Hence, it may be conjectured that future urban mobility is likely an on-demand service and AV private ownership is unappealing. Nonetheless, it is still unclear what observable and latent factors will drive public interest in (S)AVs, the answer to which will have important implications on transportation system performance. This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables.     

Of “white crows” and “cash savers:” A qualitative study of travel behavior and perceptions of ridesharing in Denmark

Based on original research collected through semi-structured research interviews and five focus groups throughout Denmark, this study explores Danish perceptions about ad hoc, acquaintance-based, and organization-based ridesharing (“carpooling”). Using a grounded, qualitative factor analysis approach, it investigates the elements that influence the adoption (and non-adoption) of ridesharing and identifies market segments and business models that may enable planners to overcome existing barriers. The article finds that Danish drivers and commuters appear to be split on the topic. Negative perceptions reported by respondents include lack of availability and difficulty finding rides, viewing ridesharing as unsafe or unsecure, and expectations of social awkwardness, among others. Positive perceptions reported include cost savings compared to public and private transport, greater flexibility of travel times, and the ability to socialize with vehicle occupants. These contrasting views lead us to conclude that existing theories and models of ridesharing behavior may need to be fundamentally rethought, both in Denmark and possibly elsewhere. Our results also suggest that ridesharing efforts framed around climate change or environmental sustainability will not likely be successful in Denmark.     

The influence of employer ridesharing programs on employee mode choice

Employer ridesharing programs and employee mode choice were analyzed using Southern California data. Problems in estimating the costs and benefits of employer ridesharing programs were identified. Surveyed firms used a wide variety of information to estimate employee mode split internally. Virtually all surveyed firms offered free or subsidized parking to some or all of their employees. Few responding firms estimated the cost of providing employee parking accurately, if at all. Despite these significant data limitations, factors influencing firm choice of employer ridesharing program components were identified. The influence of employer ridesharing programs on employee mode choice was modeled using weighted least squares logit regression analysis. Firm size was foung to be the single most important variable identified in the analysis. Larger firms were more likely to offer ridesharing incentives to their empolyees, and to report direct employer benefits from ridesharing. Alternative work hours hindered the formation of ridesharing arrangements in some cases. Relatively few firms promoted ridesharing on a purely voluntary basis. A private market for employer ridesharing services was found to exist, however. Personalized matching assistance may be a critical factor in developing more effective employer ridesharing programs. Parking pricing and supply control measures probably would have a larger impact on employee mode split overall. Parking management faces severe obstacles in implementation, some of which might be overcome through the more extensive provision of ridesharing services, such as personalized matching assistance. to employees at specific employment sites by their employers.     

A modeling approach for matching ridesharing trips within macroscopic travel demand models

State of the art travel demand models for urban areas typically distinguish four or five main modes: walking, cycling, public transport and car. The mode car can be further split into car-driver and car-passenger. As the importance of ridesharing may increase in the coming years, ridesharing should be addressed as an additional sub or main mode in travel demand modeling. This requires an algorithm for matching the trips of suppliers (typically car drivers) and demanders (travelers of non-car modes). The paper presents a matching algorithm, which can be integrated in existing travel demand models. The algorithm works likewise with integer demand, which is typical for agent-based microscopic models, and with non-integer demand occurring in travel demand matrices of a macroscopic model. The algorithm compares two path sets of suppliers and demanders. The representation of a path in the road network is reduced from a sequence of links to a sequence of zones. The zones act as a buffer along the path, where demanders can be picked up. The travel demand model of the Stuttgart Region serves as an application example. The study estimates that the entire travel demand of all motorized modes in the Stuttgart Region could be transported by 7% of the current car fleet with 65% of the current vehicle distance traveled, if all travelers were willing to either use ridesharing vehicles with 6 seats or traditional rail.     

Cost-benefit analysis of alternative vehicles in the Philippines using immediate and distant future scenarios

Alternative vehicle technologies promise a sustainable future by reducing carbon emissions and pollution. However, their widespread adoption tends to be slow due to high costs and uncertainties in benefits. Using a life cycle-based approach, this study calculates ownership savings and societal benefits for various alternative vehicle technologies against their baseline vehicle technology (e.g. gasoline or diesel). The assessment is performed from a developing country context – in the Philippines. Furthermore, immediate and distant future scenarios are modeled. The immediate future scenario assesses costs and benefits if the shift is to happen now, while the distant future scenario considers the effect of widespread autonomous driving and ridesharing. The results of the study echo the significant societal benefits from electric- and fuel cell-powered vehicles found in literature, but they are hindered by high ownership costs. In the immediate future, the diesel hybrid electric vehicle can potentially have both positive societal and operational costs for public transportation. For a gasoline-powered private passenger car, a simple shift to diesel, 20% biodiesel or 85% methanol can be beneficial. In the distant future, it is expected that autonomous, rideshared vehicles can potentially lure people away from driving their own vehicles, because of lower costs per passenger-kilometer while sustaining the privacy and comfort of a private car.     

Stochastic optimization approach for the car placement problem in ridesharing systems

With the increasing fuel prices and the pressure towards greener modes of transportation, ridesharing has emerged as an alternative to private car ownership and public transportation. In this paper, we focus on a common destination ridesharing system which is of interest in large organizations such as companies and government offices. Particularly, such organizations are looking at using company owned vehicles to offer a ridesharing service by which employees carpool to work thus leading to several benefits that include decreasing pressure on on-campus parking spaces, lowering localized on-campus congestion, in addition to offering a greener transportation mode while lowering transportation costs for employees. Based on discussions with our industry partners, optimizing the distribution of limited number of company vehicles while insuring robustness against unlikely vehicle unavailability is of critical importance. Thus in this paper, we present a stochastic mixed integer programming model to optimize the allocation of shared vehicles to employees while taking into account the unforeseen event of vehicle unavailability which would require some participants to take own vehicles or rerouting of existing vehicles. Since solving the proposed model to optimality is computationally challenging for problems of large sizes, we also propose a heuristic that is capable of finding good quality solutions in limited computational time. The proposed model and heuristic are tested on several instances of varying sizes showing the computational performance. Finally, a test case based on the city of Rome, Italy is presented and insights related to vehicle distribution and travel time savings are discussed.     

Factors influencing commuting choices in suburban labor markets: A case analysis of Pleasanton,California

Worsening suburban congestion in recent years has sparked considerable interest in the travel behavior of suburban workers. This paper examines workplace characteristics have influenced the modal and temporal travel choices of suburban employees, using Pleasanton, California, a fast-growing suburb of the San Francisco Bay Area, as a case setting. The incidence of ridesharing was found to be highest for large companies of single-tenant sites with predominantly white-collar workers. Employers who offer workers flex-time privileges tend to be smaller firms with professional staffs situated in multitenant complexes. Many opt for flex-time because they do not have a critical mass of workers to successfully launch and sustain ridesharing programs. Flex-time programs were found to hinder the formation of carpools and vanpools in suburban settings like Pleasanton. The preferred traffic management program, it is argued, would encourage the staggering of work schedules across, not within, companies in order to promote more ridesharing.