Autonomous Mobility-on-demand Systems Research Articles

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.

Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties

Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service and the charging process. The optimization goal is to minimize the worst-case expected cost under both passenger mobility demand uncertainties and EV supply uncertainties. We then propose a novel distributional uncertainty sets construction algorithm that guarantees the produced parameters are contained in desired confidence regions with a given probability. To solve the proposed DRO AMoD EV balancing problem, we derive an equivalent computationally tractable convex optimization problem. Based on real-world EV data of a taxi system, we show that with our solution the average total balancing cost is reduced by 14.49%, and the average mobility fairness and charging fairness are improved by 15.78% and 34.51%, respectively, compared to solutions that do not consider uncertainties.

A Scientometric Review of Mobility-on-Demand Car-Sharing Systems

This article gives a scientometric analysis of research advances in mobility-on-demand (MoD)systems. By reviewing 191 articles with diverse perspectives from the past 10 years, a scientometric study is conducted using CiteSpace, giving an integrated knowledge mind mapping of MoD systems. The results indicate that the major contributions in MoD systems field are mainly from the United States, Singapore, China, and Germany. Meanwhile, pivotal institutions, journals, and article keywords are discussed. MoD system knowledge domains are concentrated in the categories of demand and service, system optimization, impacts and factors, and response and choice. The development of MoD systems is evolving from taxis and minibuses to autonomous vehicles. In particular, research interest in autonomous MoD systems is rapidly growing. This analysis gives a comprehensive and integral knowledge mind mapping, which contributes to the MoD system information network and provides valuable insights into future research directions and developments for investigators and software developers in the field of vehicle-sharing and traffic scheduling.

Reinforcement Learning for Autonomous Mobility-on-Demand Systems

Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles. Given a graph representation of the transportation network - one where, for example, nodes represent areas of the city, and edges the connectivity between them - we argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. In this paper, we propose a deep reinforcement learning framework1 to control the rebalancing of AMoD systems through graph neural networks. Crucially, we demonstrate that graph neural networks enable reinforcement learning agents to recover behavior policies that are significantly more transferable, generalizable, and scalable than policies learned through other approaches. Empirically, we show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks such as inter-city generalization, service area expansion, and adaptation to potentially complex urban topologies.

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.

Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour

Autonomous mobility on demand systems (AMODS) are predicted to face a bright future for providing convenient individual mobility. Yet, increasing the occupancy rate of AMODS by ridesharing is an essential prerequisite for sustainable future mobility. The online study (N = 150) assessed travelers’ willingness to share rides with unknown fellow travelers in shared AMODS (SAMODS) depending on the factors travel distance and detour factor that are affected by the ridesharing scheme. The findings reveal great acceptability of SAMODS and underline the importance of the travel time and detour for respondents’ willingness to share rides. The cumulative distribution function of the Willingness to Accept (WTA) of sharing rides provides an indication of how much discount in price of a shared ride is needed to attract a critical mass of travelers. The findings indicate that 90% of respondents would prefer to share a ride of 10 min if the discount was 50% or more compared to a private ride. A higher discount is needed to attract travelers to share rides if travel time and detour factor increase. The results highlight the importance of a price system that is adjustable to ride-specific travel time and detour factor to attract travelers to share rides in future SAMODS.

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.

On the Interaction Between Autonomous Mobility on Demand Systems and Power Distribution Networks—An Optimal Power Flow Approach

In future transportation systems, the charging behavior of electric Autonomous Mobility on Demand (AMoD) fleets, i.e., fleets of electric self-driving cars that service on-demand trip requests, will likely challenge power distribution networks (PDNs), causing overloads or voltage drops. In this paper, we show that these challenges can be significantly attenuated if the PDNs' operational constraints and exogenous loads (e.g., from homes or businesses) are accounted for when operating an electric AMoD fleet. We focus on a system-level perspective, assuming full coordination between the AMoD and the PDN operators. From this single entity perspective, we assess potential coordination benefits. Specifically, we extend previous results on an optimization-based modeling approach for electric AMoD systems to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints. For a case study of Orange County, CA, we show that the coordination between the electric AMoD fleet and the PDNs eliminates 99% of the overloads and 50% of the voltage drops that the electric AMoD fleet would cause in an uncoordinated setting. Our results show that coordinating electric AMoD and PDNs can help maintain the reliability of PDNs under added electric AMoD charging load, thus significantly mitigating or deferring the need for PDN capacity upgrades.

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.

Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems

We are observing a disruption in the urban transportation worldwide. The number of cities offering shared-use on-demand mobility services is increasing rapidly. They promise sustainable and affordable personal mobility without a burden of owning a vehicle. Despite growing popularity, on-demand services, such as carsharing, remain niche products due to small scale and rebalancing issues. We are proposing an extension to the traditional carsharing, which is Autonomous Mobility on Demand (AMOD). AMOD provides a one-way carsharing with self- driving electric vehicles. Autonomous vehicles can make the carsharing more attractive to customers as they (i) reduce the operating cost, which is incurred when a manually driven system is unbalanced, and (ii) release people from the burden of driving.This study is built upon our previous work on Autonomous Mobility on Demand (AMOD) systems. Our methodology is simulation-based and we make use of SimMobility, an agent-based microscopic simulation platform. In the current work we focus on the framework for testing different rebalancing policies for the AMOD systems. We compare three different rebalancing methods: (i) no rebalancing, (ii) offline rebalancing, and (iii) online rebalancing. Simulation results indicate that rebalancing reduces the required fleet size and shortens the customers’ wait time.