Autonomous Mobility-on-demand System Research Articles

Designing an Autonomous Mobility-on-Demand Service for Transit Last-Mile Access

Public transportation has the potential to provide a safe, convenient, affordable, and eco-friendly mobility service. However, because of its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last-mile connectivity problem.” Such a lack of connectivity can force travelers to drive, consequently increasing vehicle ownership and vehicle miles traveled (VMT) on the roads. The autonomous mobility-on-demand (AMoD) service, with characteristics such as quick fleet repositioning and demand responsiveness, has the potential to provide last-mile coverage where fixed-route transit can only provide limited coverage. This study aims to address the last-mile problem by developing an online demand-responsive AMoD service integrated with fixed-route transit. A linear time-delay dynamical system is proposed to model the AMoD system, and a model predictive control methodology is adopted to regulate the system around an equilibrium point with minimum vehicle rebalancing. To assess the impact of this new mobility service on travel demand, a simulation study is developed and integrated with a mode choice model capturing a combined transit-AMoD model. The experiment results reveal the potential to enhance transit efficiency, reduce VMT, and accommodate the increased transit demand while maintaining the quality of service.

Charging autonomous electric vehicle fleet for mobility-on-demand services: Plug in or swap out?

This paper compares two prevalent charging strategies for electric vehicles, plug-in charging and battery swapping, to investigate which charging strategy is superior for electric autonomous mobility-on-demand (AMoD) systems. To this end, we use a queueing-theoretic model to characterize the vehicle waiting time at charging stations and battery swapping stations, respectively. The model is integrated into an economic analysis of the electric AMoD system operated by a transportation network company (TNC), where the incentives of passengers, the charging/operating shift of TNC vehicles, the operational decisions of the platform, and the planning decisions of the government are captured. Overall, a bi-level optimization framework is proposed for charging infrastructure planning of the electric AMoD system. Based on the proposed framework, we compare the socio-economic performance of plug-in charging and battery swapping, and investigate how this comparison depends on the evolving charging technologies (such as charging speed, battery capacity, and infrastructure cost). At the planning level, we find that when choosing plug-in charging, increased charging speed leads to a transformation of infrastructure from sparsely distributed large stations to densely distributed small stations, while enlarged battery capacity transforms the infrastructure from densely distributed small stations to sparsely distributed large stations. On the other hand, when choosing battery swapping, both increased charging speed and enlarged battery capacity will lead to a smaller number of battery swapping stations. At the operational level, we find that improved charging speed leads to increased TNC profit when choosing plug-in charging, whereas improved charging speed may lead to smaller TNC profit under battery swapping. The above insights are validated through realistic numerical studies.

Autonomous mobility on demand: from case studies to standardized evaluation

We present an overview of ten case studies of Autonomous Mobility on Demand (AMoD) transportation systems, which are based on realistic data from different urban contexts. Comparing AMoD systems with Conventionally Driven Vehicles (CDV), the limits of reduction of vehicles, the cutting-back of parking spaces, and the increase of empty miles are investigated. As a result of introducing a shared fleet of autonomous vehicles (AV), the analysis demonstrated that 88%–93% of CDV are not required to meet realistic requirements. Parking spaces can be reduced by 83%–97%, while empty miles could be increased by 6%–15%. Nonetheless, fleet dispatching techniques that use the advanced optimization algorithms can reduce the ratio of empty miles by as much as 40%. Consequently, we propose a standard procedure for conducting intelligent transportation system studies (ITS) that can assist in the planning of traffic on urban environments at operational, tactical, and strategic levels. Furthermore, the case studies enabled us to design an Intelligent Transportation System Readiness Level (ITS-RL) scale to assess the realism of case studies, facilitate risk assessment, and provide guidance on how to incorporate AMoD system within a local context.

Coordinated flow model for strategic planning of autonomous mobility-on-demand systems

ABSTRACT High-quality strategic planning of autonomous mobility-on-demand (AMOD) systems is critical for the success of the subsequent phases of AMOD system implementation. To assist in strategic AMOD planning, we propose a dynamic and flexible flow-based model of an AMOD system. The proposed model is computationally fast while capturing the state transitions of two coordinated flows (i.e. co-flows): the AMOD service fleet vehicles and AMOD customers. Capturing important quantity dynamics and conservations through a system of ordinary differential equations, the model can economically respond to a large number and a wide range of scenario-testing requests. The paper illustrates the model efficacy through a basic example and a more realistic case study. The case study envisions replacing Manhattan's existing taxi service with a hypothetical AMOD system. The results show that even a simple co-flow model can robustly predict the systemwide AMOD dynamics and support the strategic planning of AMOD systems.

Quality-of-Service Aware Battery Swapping Navigation and Pricing for Autonomous Mobility-on-Demand System

The autonomous mobility-on-demand (AMoD) system is regarded as a promising shared mobility method for the sustainable transportation system. In addition, battery swapping could become an efficient energy-refueling means for electric vehicles (EVs). The quality-of-service aware battery swapping price determined by the battery swapping stations (BSSs) considers the interaction between AMoD fleet and BSSs, which is formulated as a bilevel optimization problem in this article. The interdependence between two systems is bridged by the swapping price that is both time-varying and location-varying. In the upper level, the swapping pricing is optimized to reflect the battery inventory so that the EV fleet could be navigated for the BSS strategically, the queue time would be reduced consequently. BSS operator optimizes swapping pricing scheme and battery charging management with the objective of minimizing the cost induced by the queue process and battery charging. In the lower level, a unified network flow model incorporating queue procedure in BSSs is proposed in order to characterize the operational decision of AMoD fleet. The AMoD operator develops fleet scheduling strategies with the objective of maximizing the AMoD system profit. An iteration-based algorithm is applied to attain the results and the effectiveness of the algorithm is validated by the real-world data from New York.

Joint Charging Infrastructure and Autonomous Fleet Planning Considering Distributed Renewable Resources

Considering its unique operational characteristics, the autonomous mobility-on-demand (AMoD) system is playing a more and more critical role in the future transportation system. In this article, we consider a joint planning problem of autonomous fleet (i.e., vehicle type and fleet size) and charging infrastructure (i.e., the size of charging piles with various charging rates, distributed renewable resources) while accounting for the spatial–temporal demand-side flexibility of the AMoD system. We adopt an augmented network flow model to capture the autonomous fleet's charging, order serving, and rebalancing decisions. The proposed planning problem is modeled as a two-stage stochastic program and solved by multicut Benders decomposition techniques. Simulation experiments based on real-world data are performed to demonstrate the effectiveness of the proposed planning scheme.

A business class for autonomous mobility-on-demand: Modeling service quality contracts in dynamic ridesharing systems

With the popularization of transportation network companies (TNCs) (e.g., Uber, Lyft) and the rise of autonomous vehicles (AVs), even major car manufacturers are increasingly considering themselves as autonomous mobility-on-demand (AMoD) providers rather than individual vehicle sellers. However, matching the convenience of owning a vehicle requires providing consistent service quality, taking into account individual expectations. Typically, different classes of users have different service quality (SQ) expectations in terms of responsiveness, reliability, and privacy. Nonetheless, AMoD systems presented in the literature do not enable active control of service quality in the short term, especially in light of unusual demand patterns, sometimes allowing extensive delays and user rejections. This study proposes a method to control the daily operations of an AMoD system that uses the SQ expectations of heterogeneous user classes to dynamically distribute service quality among riders. Additionally, we consider an elastic vehicle supply, that is, privately-owned freelance AVs (FAVs) can be hired on short notice to help providers meeting user service-level expectations. We formalize the problem as the dial-a-ride problem with service quality contracts (DARP-SQC) and propose a multi-objective matheuristic to address real-world requests from Manhattan, New York City. Applying the proposed service-level constraints, we improve user satisfaction (in terms of reached service-level expectations) by 53% on average compared to conventional ridesharing systems, even without hiring additional vehicles. By deploying service-quality-oriented on-demand hiring, our hierarchical optimization approach allows providers to adequately cater to each segment of the customer base without necessarily owning large fleets.

Rebalancing Autonomous Vehicles using Deep Reinforcement Learning

The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.

Modeling and Optimization of Multiaction Dynamic Dispatching Problem for Shared Autonomous Electric Vehicles

The fusion of electricity, automation, and sharing is forming a new Autonomous Mobility-on-Demand (AMoD) system in current urban transportation, in which the Shared Autonomous Electric Vehicles (SAEVs) are a fleet to execute delivery, parking, recharging, and repositioning tasks automatically. To model the decision-making process of AMoD system and optimize multiaction dynamic dispatching of SAEVs over a long horizon, the dispatching problem of SAEVs is modeled according to Markov Decision Process (MDP) at first. Then two optimization models from short-sighted view and farsighted view based on combinatorial optimization theory are built, respectively. The former focuses on the instant and single-step reward, while the latter aims at the accumulative and multistep return. After that, the Kuhn–Munkres algorithm is set as the baseline method to solve the first model to achieve optimal multiaction allocation instructions for SAEVs, and the combination of deep Q-learning algorithm and Kuhn–Munkres algorithm is designed to solve the second model to realize the global optimization. Finally, a toy example, a macrosimulation of 1 month, and a microsimulation of 6 hours based on actual historical operation data are conducted. Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.

Integrated Operation Model for Autonomous Mobility-on-Demand Fleet and Battery Swapping Station

The autonomous mobility-on-demand (AMoD) system provides an alternative solution for sustainable and economical transportation system. Meanwhile, battery swapping could become a promising approach to sustain the efficient operation of EV fleet. This article proposes a combined operation scheme for battery swapping station (BSS) and AMoD system. To maximizing the profit of AMoD system, an expanded network flow model is employed to determine swapping scheduling and vehicle rebalancing for EV fleet in AMoD system. The interaction between the shared mobility-on-demand system and BSS is modeled by the swapping demand and swapping price. Specifically, swapping demand is determined in the AMoD system and introduced as known parameters in the BSS problem. Fleet management operation in AMoD system will be influenced by swapping price provided by BSS. The BSS management problem is formulated as a mixed-integer linear programming model, which optimizes refueling decisions for depleted batteries. Simulation experiments based on real-world data from New York City are provided. The results demonstrate the effectiveness of this proposed integrated operation model.

Intermodal Autonomous Mobility-on-Demand

In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows the system to recover a social optimum under the assumption of a perfect market with selfish agents. Third, we present real-world case studies for the transportation networks of New York City and Berlin, which allow us to quantify the general benefits of intermodal AMoD, as well as the societal impact of different vehicles. In particular, we show that vehicle size and powertrain type heavily affect intermodal routing decisions and, thus, system efficiency. Our studies reveal that the cooperation between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation, whilst our proposed tolling policies appear to be in line with recent discussions for the case of New York City.

Comparing Future Autonomous Electric Taxis With an Existing Free-Floating Carsharing System

When considering autonomous mobility on demand (AMoD) trends, it is probably safe to assume that they will have a large market share in the near future. In the introductory phases, users of current non-autonomous mobility on demand services such as ride-hailing and carsharing are expected to be among the first users of AMoD systems. The research presented in this paper aims to estimate fares for an AMoD system in this early stages based on rental and financial data provided by a free-floating CS provider. It demonstrates that an autonomous taxi (aTaxi) model requires less vehicles to serve the same demand resulting in the possibility to lower fares. In our model, user behavior is represented by defining three maximal waiting times and three monetary values reflecting their dissatisfaction in the case, where they cannot be served in due time. Two bipartite optimization problems for vehicle-to-user and relocation assignments build the core of the introduced aTaxi model. Fleet size and relocation parameters are chosen according to a utility function representing profit and opportunity costs of users not being served. We compute the reduction in fares to break-even with the current CS profit. Results of a case-study in Munich, Germany, indicate that one aTaxi can replace 2.8–3.7 CS vehicles. The aTaxi operator can therefore reduce fares by 29%–35% to achieve the same profit assuming the same cost structures as in free-floating CS.

Value of demand information in autonomous mobility-on-demand systems

Effective management of demand information is a critical factor in the successful operation of autonomous mobility-on-demand (AMoD) systems. This paper classifies, measures and evaluates the demand information for an AMoD system. First, the paper studies demand information at both individual and aggregate levels and measures two critical attributes: dynamism and granularity. We identify the trade-offs between both attributes during the data collection and information inference processes and discuss the compatibility of the AMoD dispatching algorithms with different types of information. Second, the paper assesses the value of demand information through agent-based simulation experiments with the actual road network and travel demand in a major European city, where we assume a single operator monopolizes the AMoD service in the case study area but competes with other transportation modes. The performance of the AMoD system is evaluated from the perspectives of travelers, AMoD operators, and transportation authority in terms of the overall system performance. The paper tests multiple scenarios, combining different information levels, information dynamism, and information granularity, as well as various fleet sizes. Results show that aggregate demand information leads to more served requests, shorter wait time and higher profit through effective rebalancing, especially when supply is high and demand information is spatially granular. Individual demand information from in-advance requests also improves the system performance, the degree of which depends on the spatial disparity of requests and their coupled service priority. By designing hailing policies accordingly, the operator is able to maximize the potential benefits. The paper concludes that the strategic trade-offs of demand information need to be made regarding the information level, information dynamism, and information granularity. It also offers a broader discussion on the benefits and costs of demand information for key stakeholders including the users, the operator, and the society.

Exploring the performance of autonomous mobility on-demand systems under demand uncertainty

ABSTRACTThis paper aims to explore the performance of Autonomous Mobility on-Demand (AMoD) systems under uncertain travel demands in an urban environment using a case study of Melbourne, Australia. The results of the simulation model developed in this study showed that an AMoD system could reduce the current fleet size by 84% while still meeting the same demand for travel. This, however, comes at a cost of more Vehicle-Kilometres Travelled (VKT). The increase in VKT is significant and amounts to around 77% for scenarios in which the vehicles are used in car-sharing systems, and 29% for the scenarios in which vehicles are used as ride-sharing systems. These findings show that the benefits reported in other studies have mainly been overestimated. The current study has also discovered a strong quadratic relationship between AMoD fleet size and VKT.

A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems

In this paper we present a queuing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand (AMoD) systems. We first cast an AMoD system into a closed, multi-class Baskett–Chandy–Muntz–Palacios (BCMP) queuing network model capable of capturing the passenger arrival process, traffic, the state-of-charge of electric vehicles, and the availability of vehicles at the stations. Second, we propose a scalable method for the synthesis of routing and charging policies, with performance guarantees in the limit of large fleet sizes. Third, we explore the applicability of our theoretical results on a case study of Manhattan. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which provides a large set of modeling options (e.g. the inclusion of road capacities and charging constraints), and subsumes earlier Jackson and network flow models.

Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study

Abstract: This paper presents results from a simulation-based study which aimed to demonstrate the feasibility of using agent-based simulation tools to model the impacts of shared autonomous vehicles. First, the paper outlines a research framework for the development and evaluation of low carbon mobility solutions driven by two disruptive forces which are changing the mobility landscape and providing consumers with more choices to meet their transport needs: automated self-driving and on-demand shared mobility services. The focus of this paper is on development of rigorous models for understanding the demand for travel in the age of connected mobility, and assessing their impacts particularly under scenarios of autonomous or self-driving on-demand shared mobility. To demonstrate the feasibility of the approach, the paper provides initial results from a pilot study on a small road network in Melbourne, Australia. A base case scenario representing the current situation of using traditional privately owned vehicles, and two autonomous mobility on-demand (AMoD) scenarios were simulated on a real transport network. In the first scenario (AMoD1), it was assumed that the on-demand vehicles were immediately available to passengers (maximum waiting times is zero). This constraint was relaxed in the second scenario (AMoD2) by increasing the allowable passenger waiting times up to a maximum of 5 minutes. The results showed that using the AMoD system resulted in a significant reduction in both the number of vehicles required to meet the transport needs of the community (reduction of 43% in AMoD1, and 88% in AMoD2), and the required on-street parking space (reduction of 58% in AMoD1 and 83% in AMoD2). However, the simulation also showed that this was achieved at the expense of a less significant increase in the total VKT (increase of 29% in AMoD1 and 10% in AMoD2). The paper concludes by describing how the model is being extended, the remaining challenges that need to be overcome in this research, and outlines the next steps to achieve the desired outcomes.