Efficient and Privacy-Preserving Ride Sharing Organization for Transferable and Non-Transferable Services

Ride-sharing allows multiple persons to share their trips together in one vehicle instead of using multiple vehicles. This can reduce the number of vehicles in the street, which consequently can reduce air pollution, traffic congestion and transportation cost. However, a ride-sharing organization requires passengers to report sensitive location information about their trips to a trip organizing server (TOS) which creates a serious privacy issue. In addition, existing ride-sharing schemes are non-flexible, i.e., they require a driver and a rider to have exactly the same trip to share a ride. Moreover, they are non-scalable, i.e., inefficient if applied to large geographic areas. In this paper, we propose two efficient privacy-preserving ride-sharing organization schemes for Non-transferable Ride-sharing Services (NRS) and Transferable Ride-sharing Services (TRS). In the NRS scheme, a rider can share a ride from its source to destination with only one driver whereas, in TRS scheme, a rider can transfer between multiple drivers while en route until he reaches his destination. In both schemes, the ride-sharing area is divided into a number of small geographic areas, called cells, and each cell has a unique identifier. Each driver/rider should encrypt his trip's data and send an encrypted ride-sharing offer/request to the TOS. In NRS scheme, Bloom filters are used to compactly represent the trip information before encryption. Then, the TOS can measure the similarity between the encrypted trips data to organize shared rides without revealing either the users' identities or the location information. In TRS scheme, drivers report their encrypted routes, an then the TOS builds an encrypted directed graph that is passed to a modified version of Dijkstra's shortest path algorithm to search for an optimal path of rides that can achieve a set of preferences defined by the riders

Learning Riders\u27 Preferences in Ridesharing Platforms

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\u27 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: ε-greedy and UCB1, for learning utilities of the riders, maximizing social welfare, and the number of blocking pairs in the socially optimal matching. The ε-greedy algorithm with ε=0.1 provides the maximum accuracy in learning the utilities of the riders as compared to ε=0.0, ε=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

Mobility Sharing as a Preference Matching Problem

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 most ridesharing algorithms optimize fellow passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time, or vehicle-distance traveled), very few explicitly consider passengers' preference for their peers as the matching objective. The existing literature either considers the bipartite driver-passenger matching problem, which is structurally different from the monopartite passenger-passenger matching, or only considers the passenger-passenger problem in a simplified one-origin-multiple-destination setting. We formulate a general monopartite passenger matching model in a road network and illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1000 randomly generated preference orders and the other considering four 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.Singapore-MIT Alliance (Future Mobility Program)Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Seed Fun

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Sharing economy and socio-economic transitions: an application of the multi-level perspective on a case study of carpooling in the USA (1970-2010)

The study deals with the emerging concept of sharing economy using the development of carpooling as example. It is based on the multi-level perspective framework, developed by Frank Geels, which is designed to explain and analyze processes of novel technology development. The present paper analyzes the new institution, carpooling, through the lens of this framework in order to understand its potential to be a landscape-changing innovation. This case study also attempts to illustrate how the multi-level perspective can be used to analyze not only technological innovations, but also novel ways of doing business, which can arguably be viewed as radical innovations on their own. The aim is thus to find out whether the emergence of carpooling follows the same patterns and shows the same features as emergence of conventional technological radical innovations

Mobility on Demand in the United States

The growth of shared mobility services and enabling technologies, such as smartphone apps, is contributing to the commodification and aggregation of transportation services. This chapter reviews terms and definitions related to Mobility on Demand (MOD) and Mobility as a Service (MaaS), the mobility marketplace, stakeholders, and enablers. This chapter also reviews the U.S. Department of Transportation’s MOD Sandbox Program, including common opportunities and challenges, partnerships, and case studies for employing on-demand mobility pilots and programs. The chapter concludes with a discussion of vehicle automation and on-demand mobility including pilot projects and the potential transformative impacts of shared automated vehicles on parking, land use, and the built environment