In recent years, we have witnessed a remarkable surge of usage in shared vehicles in our cities. Shared mobility offers a future of no congestion in busy city roads with increasing populations of travelers, passengers, and drivers. Given the behavioral decision-making of travelers and the shared vehicles' operators, however, the question is "how can we ensure a socially-acceptable assignment between travelers and vehicles?" In other words, how can we design a shared mobility system that assigns each traveler to the "right" vehicle? In this paper, we design a shared mobility market consisted of travelers and vehicles in a transportation network. We formulate a linear programming problem and derive the optimal assignment between travelers and vehicles. In addition, we provide the necessary and sufficient conditions for the stable traveler-vehicle profit allocation. Our goal is twofold: maximize the social welfare of all travelers with the optimal assignment while ensuring the feasibility and stability of the traveler-vehicle profit allocation while respecting the decision-making of both the travelers and the vehicles' operators.
Emerging mobility systems such as connected and automated vehicles (CAVs) provide the most intriguing opportunity for more accessible, safe, and efficient transportation. CAVs are expected to significantly improve safety by eliminating the human factor and ensure transportation efficiency by allowing users to monitor transportation network conditions and make better operating decisions. However, CAVs could alter the users' tendency-to-travel, leading to a higher traffic demand than expected, thus causing rebound effects (e.g., increased vehicle-miles-traveled). In this chapter, we focus on tac…
We examine how introduction of Shared Connected and Automated vehicles (SCAVs) as a new mobility mode could affect travel demand, welfare, as well as traffic congestion in the network. To do so, we adapt an agent-based day-to-day adjustment process and develop a central dispatching system, which is implemented on an in-house traffic microsimulator. We consider a two-sided market in which demand and SCAV fleet size change endogenously. For dispatching SCAV fleet size, we take changing traffic conditions into account. There are two available transport modes: private Connected Automated Vehicles …
Recent advances in communication technologies and automated vehicles have opened doors for alternative mobility systems (taxis, carpool, demand-responsive services, peer-to-peer ridesharing, and car sharing, shared autonomous vehicles/shuttles). These new mobility services have gathered interest from researchers, public and private sectors as potential solutions to address last-mile problem--especially in low density areas where implementation of high frequency buses is not feasible. In this study we investigate the effects of ride-sharing service on travel demand and welfare, as it complement…
Shared mobility on demand (MoD) services are receiving increased attention as many high volume ride-hailing companies are offering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises further operational opportunities to benefit from these developments as AVs enable a centrally operated and fully connected fleet. There are two fundamental tasks for a shared MoD service: ride-matching and vehicle rebalancing. Traditionally, these two functions are performed sequentially and independently. In this paper, we propose and formulate …
Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size, or some combination of these factors, compared to door-to-door (D2D) shared-ride services. This paper provides a review of shared-ride services with walking legs (SRSWL), particularly the studies in the literature that model the operational problem(s) associated with SRSWL. The paper describes the operational and societal benefits of SRSWL as well as compares the SRSWL to circuitous D2D shared-ride services, ride-h…
On-demand shared mobility is a promising and sustainable transportation approach that can mitigate vehicle externalities, such as traffic congestion and emission. On-demand shared mobility systems require matching of one (one-to-one) or multiple riders (many-to-one) to a vehicle based on real-time information. We propose a novel Graph-based Many-to-One ride-Matching (GMOMatch) algorithm for the dynamic many-to-one matching problem in the presence of traffic congestion. GMOMatch, which is an iterative two-step method, provides high service quality and is efficient in terms of computational comp…
Mobility-on-Demand (MoD) systems have become a fixture in urban transportation networks, with the rapid growth of ride-hailing services such as Uber and Lyft. Ride-hailing is typically complemented with ridepooling options, which can reduce the negative externalities associated with ride-hailing services and increase the utilization of vehicles. Determining optimal policies for vehicle dispatching and pricing, two key components that enable MoD services, are challenging due to their massive scale and online nature. The challenge is amplified when the MoD platform offers exclusive (conventional…
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
University of Exeter（埃克塞特大学）；The Alan Turing Institute（阿兰·图灵研究所）；University of Warwick（华威大学）；Tongji University（同济大学）；University of Leeds（利兹大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）
The fast urbanization and climate change challenges require solutions that enable the efficient movement of people and goods in cities. We envision future cities to be composed of high-performing walkable districts where transportation needs could be served by fleets of ultra-lightweight shared and autonomous vehicles. A future in which most vehicles would be autonomous creates a new paradigm for the possible interactions between vehicles. Natural swarms are a great example of how rich interactions can be; they can divide tasks, cluster, build together, or transport cooperatively. The field of…
The goal of this paper is to develop a modeling framework that captures the inter-decision dynamics between mobility service providers (MSPs) and travelers that can be used to optimize and analyze policies/regulations related to MSPs. To meet this goal, the paper proposes a tri-level mathematical programming model with a public-sector decision maker (regulator) at the highest level, the MSP in the middle level, and travelers at the lowest level. The regulator aims to maximize social welfare via implementing regulations, policies, plans, transit service designs, etc. The MSP aims to maximize pr…
A distributed, hierarchical, market based approach is introduced to solve the economic dispatch problem. The approach requires only a minimal amount of information to be shared between a central market operator and the end-users. Price signals from the market operator are sent down to end-user device agents, which in turn respond with power schedules. Intermediate congestion agents make sure that local power constraints are satisfied and any potential congestion is avoided by adding local pricing differences. Our results show that in 20% of the evaluated scenarios the solutions are identical t…
The performance of multimodal mobility systems relies on the seamless integration of conventional mass transit services and the advent of Mobility-on-Demand (MoD) services. Prior work is limited to individually improving various transport networks' operations or linking a new mode to an existing system. In this work, we attempt to solve transit network design and pricing problems of multimodal mobility systems en masse. An operator (public transit agency or private transit operator) determines the frequency settings of the mass transit system, flows of the MoD service, and prices for each trip…
Massachusetts Institute of Technology（麻省理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse …
