Optimal Online Dispatch For High-Capacity Shared Autonomous Mobility-on-Demand Systems

Shared autonomous mobility-on-demand systems hold great promise for improving the efficiency of urban transportation, but are challenging to implement due to the huge scheduling search space and highly dynamic nature of requests. This paper presents a novel optimal schedule pool (OSP) assignment approach to optimally dispatch high-capacity ride-sharing vehicles in real time, including: (1) an incremental search algorithm that can efficiently compute the exact lowest-cost schedule of a ride-sharing trip with a reduced search space; (2) an iterative online re-optimization strategy to dynamically alter the assignment policy for new incoming requests, in order to maximize the service rate. Experimental results based on New York City taxi data show that our proposed approach outperforms the state-of-the-art in terms of service rate and system scalability

Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility

Autonomous mobility is emerging as a new mode of urban transportation for moving cargo and passengers. However, such fleet coordination schemes face significant challenges in scaling to accommodate fast-growing fleet sizes that vary in their operational range, capacity, and communication capabilities. We introduce the concept of partially observable advanced air mobility games to coordinate a fleet of aerial vehicle agents accounting for their heterogeneity and self-interest inherent to commercial mobility fleets. We propose a novel heterogeneous graph attention-based encoder-decoder (HetGAT Enc-Dec) neural network to construct a generalizable stochastic policy stemming from the inter- and intra-agent relations within the mobility system. We train our policy by leveraging deep multi-agent reinforcement learning, allowing decentralized decision-making for the agents using their local observations. Through extensive experimentation, we show that the fleets operating under the HetGAT Enc-Dec policy outperform other state-of-the-art graph neural network-based policies by achieving the highest fleet reward and fulfillment ratios in an on-demand mobility network.Comment: 12 pages, 12 figures, 3 table

Optimal trajectory planning meets network-level routing: Integrated control framework for emerging mobility systems

In this paper, we introduce a hierarchical decision-making framework for emerging mobility systems. Despite numerous studies focusing on optimizing vehicle flow, practical feasibility has often been overlooked. To address this gap, we present a route-recovery method and energy-optimal trajectory planning tailored for connected and automated vehicles (CAVs) to ensure the realization of optimal flow. Our approach identifies the optimal vehicle flow to minimize total travel time while considering consistent mobility demands in urban settings. We deploy a heuristic route-recovery algorithm that assigns routes to CAVs and departure/arrival time at each road segment. Furthermore, we propose an efficient coordination method that rapidly solves constrained optimization problems by flexibly piecing together unconstrained energy-optimal trajectories. The proposed method has the potential to effectively generate optimal vehicle flow, contributing to the reduction of travel time and energy consumption in urban areas.Comment: 17 pages, 11 figure

Resilience and operational benefits of electric vehicle and grid integration

Shared autonomous electric vehicles (SAEVs) present new opportunities to control and optimize vehicle movements. Future deployment of these vehicles may reduce the need for individuals to own a personal vehicle and can have traffic flow and environmental benefits. However, to fully realize the benefits of this technology, vehicle dispatch needs to be optimized. Fleet operators will want to own and operate as few vehicles as possible while still maintaining a reasonable level of service for passengers. SAEVs can also be used for many purposes beyond moving individual passengers across the system. They could also be used to deliver food, provide last-mile delivery for packages, and interact with the electric grid. These services must be balanced to ensure that as many people are served as possible. SAEV dispatch can be of particular interest in the aftermath of a natural disaster when there may be failures in the electric grid. In this case, vehicles can be used to transport power across broken lines to power critical facilities or reduce the number of blackouts. However, this important service must be weighed against the continued need to provide transportation to critical workers and vulnerable populations that may be reliant on SAEV service. We develop a dispatch policy that is proven to serve all demands (for both electricity and transportation service) if any policy can serve those demand. This maximum throughput policy also enables an analytical characterization of the minimum fleet size (or minimum cost fleet if it can be heterogeneous) such that queues of passengers and energy will remain bounded in the long run. Based on the stable dispatch policy we relax some assumptions and develop a policy that is more realistic for implementation. We pay particular attention to constraints on power flow in the electric grid to ensure realistic charging and discharging behavior (which is important for distribution system service restoration). The analysis and simulation also distinguishes between several potential objective functions which have important equity and stability impacts. We demonstrate how serving passengers from the longest queues first (a technique based on the 'pressure' from maximum stability dispatch) can lead to more equitable outcomes for passengers. Finally, we examine the impact of the time horizon needed for the model predictive control algorithm. A long time horizon is needed to incorporate charging and discharging as well as longer term trends in electric demand. We suggest that future research should examine heuristics to solve this problem more quickly than commercial solvers to enable real-time implementation.Civil, Architectural, and Environmental Engineerin

Vehicle dispatch in high-capacity shared autonomous mobility-on-demand systems

Ride-sharing is a promising solution for transportation issues such as traffic congestion and parking land use, which are brought about by the extensive usage of private vehicles. In the near future, large-scale Shared Autonomous Mobility-on-Demand (SAMoD) systems are expected to be deployed with the realization of self-driving vehicles. It has the potential to encourage a car-free lifestyle and create a new urban mobility mode where ride-sharing is widely adopted among people. This thesis addresses the problem of improving the efficiency and quality of vehicle dispatch in high-capacity SAMoD systems. The first part of the thesis develops a dispatcher which can efficiently explore the complete candidate match space and produce the optimal assignment policy when only deterministic information is concerned. It uses an incremental search method that can quickly prune out infeasible candidates to reduce the search space. It also has an iterative re-optimization strategy to dynamically alter the assignment policy to take into account both previous and newly revealed requests. Case studies of New York City using real-world data shows that it outperforms the state-of-the-art in terms of service rate and system scalability. The dispatcher developed in this part can serve as a foundation for the next two parts, which consider two kinds of uncertain information, stochastic travel times and the dynamic distribution of requests in the long-term future, respectively. The second part of the thesis describes a framework which makes use of stochastic travel time models to optimize the reliability of vehicle dispatch. It employs a candidate match search method to generate a candidate pool, uses a set of preprocessed shortest path tables to score the candidates and provides an assignment policy that maximizes the overall score. Two different dispatch objectives are discussed: the on-time arrival probabilities of requests and the profit of the platform. Experimental studies show that higher service rates, reliability and profits can be achieved by considering travel time uncertainty. The third part of the thesis presents a deep reinforcement learning based approach to optimize assignment polices in a more far-sighted way. It models the vehicle dispatch problem as a Markov Decision Process (MDP) and uses a policy evaluation method to learn a value function from the historic movements of drivers. The learned value function is employed to score candidate matches to guide a dispatcher optimizing long-term objective, and will be continually updated online to capture the real-time dynamics of the system. It is shown by experiments that the value function helps the dispatcher to yield higher service rates