"Ridesharing platforms allow people to commute more efficiently. Ridesharing can be beneficial since it can reduce the travel expenses for individuals as well as decrease the overall traffic gridlocks. One of the key aspects of ridesharing platforms is for riders to find suitable partners to share the ride. Thus, the riders need to be matched to other riders/drivers. From the social perspective, a rider may prefer to share the ride with certain individuals as opposed to other riders. This leads to the rider having preferences over the other riders. A matching based on social welfare indicates the quality of the rides. Our goal is to maximize social welfare or the quality of rides for all riders. In order to match the riders, we need to know the preferences of the riders. However, the preferences are often unknown. To tackle these situations, we introduce a ridesharing model that implements reinforcement learning algorithms to learn the utilities of the riders based on the riders' previous experiences. We investigate a variety of measures for assessing social welfare, including utilitarian, egalitarian, Nash, and leximin social welfare. Additionally, we also compute the number of strong and weak blocking pairs in each socially optimal matching to compare the stability of these matchings. We provide a comparison between two reinforcement learning algorithms: [epsilon]-greedy and UCB1, for learning utilities of the riders, maximizing social welfare, and the number of blocking pairs in the socially optimal matching. The [epsilon]-greedy algorithm with [epsilon]=0.1 provides the maximum accuracy in learning the utilities of the riders as compared to [epsilon]=0.0, [epsilon]=0.01, and UCB1 algorithm. It also provides a fewer number of blocking pairs suggesting more stability in the socially optimal matching than other reinforcement learning algorithms. However, the UCB1 algorithm outperforms all other reinforcement learning algorithms to provide maximum welfare in socially optimal matchings."--Abstract.

Ride-hailing platforms such as Uber offer service suppliers more flexible work conditions but lower security than permanent jobs. This study examines the preferences of ride-hailing drivers when they are offered contract and platform design options related to flexibility, financial security, and information features. The sample design and the discrete choice experiment approach we use permit the exploration of differences in the preferences of sub-groups of suppliers, including active and inactive drivers, drivers with and with- out other jobs, and those who multi-home. We find that the utility and willingness of drivers to work for ride-hailing platforms generally increase when the ride-hailing company provides a minimum wage guarantee, a company-sponsored benefit plan, and information fea- tures that protect drivers' privacy and allow them to screen for undesired passengers. These attributes are important enough for drivers so that they are willing to sacrifice scheduling flexibility by committing to minimum working hours in exchange for those attri- butes. We identified significant heterogeneity in driver preferences. For drivers with a primary job or jobs other than ride-hailing, the perception of working conditions in the primary job affects their willingness to restart ride-hailing services. Preferences also vary depending on whether a driver participates in one or more platforms. These results suggest that offering a menu of contract options could provide novel tools for ride-hailing companies to improve the stabi- lity and predictability of supply. It may also alleviate some of the concerns about driver working conditions that have led to increasing calls for the regulation of ride-hailing services to protect drivers.

This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas. It showcases emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users’ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. Chapters highlight ways to approach such technical challenges in service science and are based on submissions from the 2020 INFORMS International Conference on Service Science.

Supply and Demand Management in Ride-Sourcing Markets offers a fundamental modeling framework for characterizing ride-sourcing markets by spelling out the complex relationships among key endogenous and exogenous variables in the markets. This book establishes several economic models that can approximate matching frictions between drivers and passengers, describes the equilibrium state of ride-sourcing markets, and more. Based on these models, the book develops an optimum strategy (in terms of trip fare, wage and/or matching) that maximizes platform profit. While the best social optimum solution (for maximizing the social welfare) is generally unsustainable, this book provides options governments can use to encourage second-best solutions. In addition, the book's authors establish models to analyze ride-pooling services, with traffic congestion externalities incorporated into models to see how both new platforms and government designs can optimize operating strategies in response to the level of traffic congestion. - Serves as a foundation for subsequent research studies that investigate ride-sourcing services through mathematical modeling - Offers valuable managerial insights for ride-sourcing platforms and helps them develop more efficient and effective operating strategies - Assists the governments or social planners in designing appropriate regulatory schemes to achieve more sustainable and societally beneficial market outcomes

Key Features: *The book's unique feature is its behavioral science perspective on the threats to quality of urban life from car use and policy measures to reduce car use *It provides a consolidated reference in car ownership and use *Key topical area backed up by international policy e.g. The Kyoto Protocol *The book's unique feature is its behavioral science perspective on the threats to quality of urban life from car use and policy measures to reduce car use *It provides a consolidated reference in car ownership and use *Key topical area backed up by international policy e.g. The Kyoto Protocol.

This book contains a collection of latest research developments on the urban transportation systems. It describes rail transit systems, subways, bus rapid transit (BRT) systems, taxicabs, automobiles, etc. This book also studies the technical parameters and provides a comprehensive overview of the significant characteristics for urban transportation systems, including energy management systems, wireless communication systems, operations and maintenance systems, transport serviceability, environmental problems and solutions, simulation, modelling, analysis, design, safety and risk, standards, traffic congestion, ride quality, air quality, noise and vibration, financial and economic aspects, pricing strategies, etc. This professional book as a credible source can be very applicable and useful for all professors, researchers, students, experienced technical professionals, practitioners and others interested in urban transportation systems.