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Dynamic Fleet Management for Autonomous Vehicles: Learning- and optimization-based strategies
Abstract
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles (AVs), however, the characteristics of mobility services change, and new opportunities to overcome the prevailing limitations arise. This thesis proposes a series of learning- and optimization-based strategies to help autonomous transportation providers meet the service quality expectations of diversified user bases. We show how autonomous mobility-on-demand (AMoD) systems can develop to revolutionize urban transportation, improving reliability, efficiency, and accessibility.
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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.
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service level contracts (SLCs) to different user groups featuring both delay and rejection penalties.
Equity considerations in transportation planning literature have received increasingly more attention in the previous decades. While there have been theoretical suggestions to base transportation planning methods on the philosophical principle of “sufficientarianism” (whereby everyone is entitled to a minimum level of a good or service), the proposed approaches have not yet been developed enough to be usable for policy decision-making. In this paper we aim to bridge this gap by operationalizing in a case study an indicator of equity based on the theoretical work of Martens (2017) which argues for sufficientarianism. The presented formalised methodology can identify and quantify equity issues in transportation, is flexible to different contexts, and is a transparent way to assess equity in transportation. The case study shows that data availability is an important constraint and that careful attention must be paid to various assumptions and choices made.
We consider a service network design problem for the tactical planning of parcel delivery in a city logistics setting. A logistics service provider seeks a repeatable plan to transport commodities from distribution centers on the periphery to inner-city satellites. In a heterogeneous infrastructure, autonomous vehicles in level 4 may only drive in feasible streets but need to be pulled elsewhere by manually operated vehicles in platoons. We formulate an integer program to determine the fleet mix, schedule transportation services, and decide on the routing or outsourcing of commodities. Platooning requires a high level of synchronization between vehicles which demands the time-expanded networks to contain narrow time intervals. Thus, we develop an algorithm based on the dynamic discretization discovery scheme which refines partially time-expanded networks iteratively without having to enumerate the fully time-expanded network a priori. We introduce valid inequalities and provide two enhanced versions of the algorithm that exploit linear relaxations of the problem. Further, we propose heuristic ideas to speed up the search for high-quality solutions. In a computational study, we analyze the efficacy of the algorithm in different versions and observe improvements of computational performance in comparison to a commercial solver. Finally, we solve a case study on a real-world based network to obtain insights into the deployment of a mixed autonomous fleet in an existing heterogeneous infrastructure.
New forms of shared mobility such as free-floating car-sharing services and shared automated vehicles have the potential to change urban travel behaviour. In this paper, we identify potential user classes for these new modes. For this, a stated choice experiment on mode choice among a sample of the Dutch urban population has been conducted, which features free-floating car-sharing and shared automated vehicles next to private vehicles, bus, and taxi. The experimental design allows disentangling the effects of vehicle ownership, vehicle sharing and vehicle automation on the perceived utility of these modes. Further contributions lie in the identification of user classes for shared and automated mobility services and their potential migration from their current modes to the these services. Latent class choice models were estimated to capture the heterogeneity in these preferences among the respondents. The most explanatory mode choice model is obtained by estimating a 3-class nested logit model capturing the impact of vehicle ownership. The results show that higher educated and more time-sensitive respondents are more inclined than others to favour the (automated) car-sharing options. By simulating a scenario that directly compares car with free-floating car-sharing and taxi with shared automated vehicles, a migration analysis has been performed. This analysis shows that the preferences towards shared automated vehicles and free-floating car-sharing is highest for those currently combining car and public transport for their commute. Commuters using the car showed a high preference towards free-floating car-sharing, in particular as for the latter no parking fees are issued. Respondents currently commuting by public transport showed the lowest preference for the new modes.
The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.
Background:
Automated Vehicles (AVs) are central to the new mobility paradigm that promises to transform transportation systems and cities across the globe. To date, much of the research on AVs has focused on technological advancements with little emphasis on how this emerging technology will impact population-level health. This scoping study examines the potential health impacts of AVs based on the existing literature.
Methods:
Using Arksey and O'Malley's scoping protocol, we searched academic and 'grey' literature to anticipate the effects of AVs on human health.
Results:
Our search captured 43 information sources that discussed a least one of the five thematic areas related to health. The bulk of the evidence is related to road safety (n = 37), followed by a relatively equal distribution between social equity (n = 24), environment (n = 22), lifestyle (n = 20), and built environment (n = 18) themes. There is general agreement that AVs will improve road safety overall, thus reducing injuries and fatalities from human errors in operating motorized vehicles. However, the relationships with air quality, physical activity, and stress, among other health factors may be more complex. The broader health implications of AVs will be dependent on how the technology is adopted in various transportation systems. Regulatory action will be a significant determinant of how AVs could affect health, as well as how AVs influence social and environmental determinants of health.
Conclusion:
To support researchers and practitioners considering the health implications of AVs, we provide a conceptual map of the direct and indirect linkages between AV use and health outcomes. It is important that stakeholders, including public health agencies work to ensure that population health outcomes and equitable distribution of health impacts are priority considerations as regulators develop their response to AVs. We recommend that public health and transportation officials actively monitor trends in AV introduction and adoption, regulators focus on protecting human health and safety in AV implementation, and researchers work to expand the body of evidence surrounding AVs and population health.
This paper studies the supply curve of ride-hailing systems under different market conditions. The curve defines a relationship between the throughput of trips of the system and the cost of riders it serves. We first focus on isotropic markets by revisiting a matching failure identified recently that matches a requesting rider with an idle driver very far away. The failure will cause the supply curve of an e-hailing market to be backward bending, but it is proved that the backward bend does not arise in the street-hailing market. By constructing a double-ended queuing model, we prove that the supply curve of an e-hailing system with finite matching radius is always backward bending, but a smaller matching radius leads to a weaker bend. We further reveal the possibility of completely avoiding the bend by adaptively adjusting the matching radius. We then turn to the anisotropic markets and identify another type of matching failure due to indiscriminate matching between drivers and riders, which again causes a backward bending supply. Given the prevalence of such a matching failure in real-world operations, we discuss how to avoid it using price or rationing discrimination. A conceptualized two-node network is constructed to facilitate the discussion.
In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four times faster than the state-of-the-art, even if it is implemented on a less dedicated hardware, and achieves similar service quality. Current literature showcases the ability of state-of-the-art ridesharing algorithms to tackle very large fleets and customer requests in almost near real-time, but the benefits of ridesharing seem limited to centralized systems. Our algorithm suggests that this does not need to be the case. The algorithm that we propose is fully distributable among multiple ridesharing companies. By leveraging two datasets, the New York city taxi dataset and the Melbourne Metropolitan Area dataset, we show that with our algorithm, real-time ridesharing offers clear benefits with respect to more traditional taxi fleets in terms of level of service, even if one considers partial adoption of the system. In fact, e.g., the quality of the solutions obtained in the state-of-the-art works that tackle the whole customer set of the New York city taxi dataset is achieved, even if one considers only a proportion of the fleet size and customer requests. This could make real-time urban-scale ridesharing very attractive to small enterprises and city authorities alike. However, in some cases, e.g., in multi-company scenarios where companies have predefined market shares, we show that the number of vehicles needed to achieve a comparable performance to the monopolistic setting increases, and this raises concerns on the possible negative effects of multi-company ridesharing.
The rise of research into shared mobility systems reflects emerging challenges, such as rising traffic congestion, rising oil prices and rising environmental concern. The operations research community has turned towards more sharable and sustainable systems of transportation. Shared mobility systems can be collapsed into two main streams: those where people share rides and those where parcel transportation and people transportation are combined. This survey sets out to review recent research in this area, including different optimization approaches, and to provide guidelines and promising directions for future research. It makes a distinction between prearranged and real-time problem settings and their methods of solution, and also gives an overview of real-case applications relevant to the research area.
In this paper we focus on the development of a new service model for accessing transport, namely Mobility as a Service (MaaS) and present one of the first critical analyses of the rhetoric surrounding the concept. One central assumption of one prevalent MaaS conceptualization is that transport services are bundled into service packages for monthly payment, as in the telecommunication or media service sectors. Various other forms of MaaS are being developed but all tend to offer door-to-door multi-modal mobility services, brokered via digital platforms connecting users and service operators. By drawing on literature concerned with socio-technical transitions, we address two multi-layered questions. First, to what extent can the MaaS promises (to citizens and cities) be delivered, and what are the unanticipated societal implications that could arise from a wholesale adoption of MaaS in relation to key issues such as wellbeing, emissions and social inclusion? Second, what are de facto challenges for urban governance if the packaged services model of MaaS is widely adopted, and what are the recommended responses? To address these questions, we begin by considering the evolution of intelligent transport systems that underpin the current vision of MaaS and highlight how the new business model could provide a mechanism to make MaaS truly disruptive. We then identify a set of plausible unanticipated societal effects that have implications for urban planning and transport governance. This is followed by a critical assessment of the persuasive rhetoric around MaaS that makes grand promises about efficiency, choice and freedom. Our conclusion is that the range of possible unanticipated consequences carries risks that require public intervention (i.e. steering) for reasons of both efficiency and equity.
Autonomous vehicles (AVs) are expected to widely re-define mobility in the future, transforming many solutions into autonomous services. Nonetheless, this development requires an expected transition phase of several decades in which some regions will provide sufficient infrastructure for AV movements, while others will not support AVs yet. In this work, we propose an operational planning model for mobility services operating in regions with AV-ready and not AV-ready zones.
To this end, we model detailed automated driving areas and consider a heterogeneous fleet comprised of three vehicle types: autonomous, conventional, and dual-mode. While autonomous and conventional vehicles can only operate in their designated areas, dual-mode vehicles service zone-crossing demands in which both human and autonomous driving are required. For such a hybrid network, we introduce a new mathematical planning model based on a site-dependent variant of the heterogeneous dial-a-ride problem (HDARP). With a numerical study for the city of Delft, The Netherlands, we provide insights into how operational costs, service levels, and fleet utilization develop under 405 scenarios of multiple infrastructural settings and technology costs.
This study explores a network configuration concept for vehicle automation levels 3–4 (according to SAE classifications) in an urban road network having mixed traffic and demonstrates its potential impacts. We assume automated driving will be allowed on a selection of roads. For the remaining roads, manual driving (although supported by assisting driving automation systems) will be compulsory. Accordingly, we introduce an approach for road selection and present relevant operational concepts. To evaluate the impacts of this configuration and model different vehicles’ route choice behavior in mixed traffic, a static multi-class stochastic user equilibrium traffic assignment with a path-size logit route choice model and a Monte Carlo labeling route-set generation is adapted. Two user-classes are distinguished: vehicles with automation levels 0–2 and vehicles with automation levels 3–4 having a different passenger car unit to account for lower driving headways, lower value of time, and higher fuel efficiency. The results indicate a decrease in total travel cost with the increase in market penetration rate of higher automation levels, a decrease in total travel time, and a minor increase in total travel distance. Although in most cases vehicles with higher automation levels benefit more from the improvements, no deterioration in travel conditions is observed for the rest of the vehicles in any scenario. Furthermore, a noticeable shift of traffic from roads with access function to roads with flow function and distributors is observed. Sensitivity analysis shows that the extent of changes in the impacts is not strongly dependent on the input parameters.
Two-tier city logistics is a well-established concept to achieve high levels of consolidation in urban freight distribution. With the recent shift towards offering same day delivery, service providers in parcel delivery are looking at new solutions to deal with the resulting challenges. We introduce autonomous vehicles as an upcoming technology to this field of research and consider a mixed fleet in the first tier of city logistics. Because autonomy cannot be ensured on all roads of the network, we handle the heterogeneous infrastructure with manually operated vehicles serving as platoon leaders. In our proposed MILP formulation of service network design for autonomous vehicles in platoons (SNDAVP), we show how platooning can be incorporated into this tactical planning problem. Computational experiments on first instances are conducted using CPLEX.
Same-day delivery has become a very important challenge for e-commerce providers. However, effective business models are still not established. In this paper, we analyze the potential of combining parcel pickup stations and autonomous vehicles for same-day delivery. This combination may allow for consolidation of goods, automated delivery processes, and reduced operation costs. We consider the problem on the operational planning level. A depot, a set of pickup stations, and an autonomous delivery fleet are given. Customers dynamically order goods to a preferred pickup station and expect fast service. The goods need to be delivered from the depot to the preferred pickup station or another pickup station in the close neighborhood. Vehicles are directly dispatched between the depot and the stations. To decide where and when to dispatch a vehicle and what goods to load, we present a policy function approximation (PFA). Our intuitive PFA allows real-time decision making while balancing the tradeoff between fast delivery and consolidation. We conduct an extensive computational study for the pickup station network of the city of Braunschweig. We show how our business model allows fast delivery for up to 100 delivered goods per vehicle and day. We further derive important managerial insights for strategical and tactical planning and present an extensive outlook on promising future research directions.
Transportation network companies (TNCs) are regularly demonstrating the economic and operational viability of dynamic ride-sharing (DRS) to any destination within a city (e.g., uberPOOL or Lyft Line), thanks to real-time information from smartphones. In the foreseeable future, fleets of shared automated vehicles (SAVs) may largely eliminate the need for human drivers, while lowering per-mile operating costs and increasing the convenience of travel. This may dramatically reduce private vehicle ownership resulting in extensive use of SAVs. This study anticipates DRS matches across different travelers and identifies optimum fleet sizes required using AirSage's cellphone-based trip tables across 1267 zones over 30 days. Assuming that the travel patterns do not change significantly in the future, the results suggest significant opportunities for DRS-enabled SAVs. Nearly 60% of the single-person trips could be shared with other individuals traveling solo and with less than 5 min of added travel time (to arrive at their destinations), and this value climbs to 80% for 15 to 30 min of added wait or travel time. 60,000 SAVs will be required to meet nearly 50% of Orlando's 2.8 million single-traveler trips each day. With maximum ride-sharing delays of 15 minutes, and when focused on serving solo travelers, the average SAV is able to serve 25 person-trips per day, reducing parking demands while filling up passenger vehicle seats.
Information and communication technologies have opened the way to new solutions for urban mobility that provide better ways to match individuals with on-demand vehicles. However, a fundamental unsolved problem is how best to size and operate a fleet of vehicles, given a certain demand for personal mobility. Previous studies¹⁻⁵ either do not provide a scalable solution or require changes in human attitudes towards mobility. Here we provide a network-based solution to the following 'minimum fleet problem', given a collection of trips (specified by origin, destination and start time), of how to determine the minimum number of vehicles needed to serve all the trips without incurring any delay to the passengers. By introducing the notion of a 'vehicle-sharing network', we present an optimal computationally efficient solution to the problem, as well as a nearly optimal solution amenable to real-time implementation. We test both solutions on a dataset of 150 million taxi trips taken in the city of New York over one year⁶. The real-time implementation of the method with near-optimal service levels allows a 30 per cent reduction in fleet size compared to current taxi operation. Although constraints on driver availability and the existence of abnormal trip demands may lead to a relatively larger optimal value for the fleet size than that predicted here, the fleet size remains robust for a wide range of variations in historical trip demand. These predicted reductions in fleet size follow directly from a reorganization of taxi dispatching that could be implemented with a simple urban app; they do not assume ride sharing⁷⁻⁹, nor require changes to regulations, business models, or human attitudes towards mobility to become effective. Our results could become even more relevant in the years ahead as fleets of networked, self-driving cars become commonplace¹⁰⁻¹⁴.
This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problems of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.
Parking is an important part of transportation planning because a typical vehicle spends 95% of its lifetime sitting in a parking spot. The increasing need to store vehicles has transformed a lot of valuable real-estate into parking garages in many countries. Realizing the high social cost of parking provision, Autonomous Vehicle (AV) industry leaders are rethinking how to reduce the parking footprint by converting traditional parking lots into automated parking facilities that can store more AVs (compared to regular vehicles) in smaller areas. An essential strategy to increase AV car-park space efficiency is to stack the AVs in several rows, one behind the other. While this type of layout reduces parking space, it can cause blockage if a certain vehicle is barricaded by other vehicles and cannot leave the facility. To release barricaded vehicles, the car-park operator has to relocate some of the vehicles to create a clear pathway for the blocked vehicle to exit. The extent of vehicle relocation depends on the layout design of the car-park. To find the optimal car-park layout with minimum relocations, we present a mixed-integer non-linear program that treats each island in the car-park as a queuing system. We solve the problem using Benders decomposition for an exact answer and we present a heuristic algorithm to find a reasonable upper-bound of the mathematical model. We show that autonomous vehicle car-parks can decrease the need for parking space by an average of 62% and a maximum of 87%. This revitalization of space that was previously used for parking can be socially beneficial if car-parks are converted into commercial and residential land-uses.
We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems.
Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.
Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared, a quantity that we call shareability. Using data on millions of taxi trips in New York City, San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in any city, using a few basic urban quantities and no adjustable parameters. Accurate extrapolations of this type will help planners, transportation companies, and society at large to shape a sustainable path for urban growth.
The coming of automated vehicles (AVs) and Mobility-on-Demand (MoD services) is expected to reduce urban parking demand and correspondingly alter the urban parking landscape in a significant way. Multiple modeling efforts have already demonstrated that Shared AVs (SAVs) have promising potential to decrease urban parking demand. However, previous studies have only examined SAV parking demand at one point in time, with various market penetrations. It remains unclear what the demand reduction trajectory will be like during the transition period when there is a mix of SAVs, Privately-Owned AVs (PAVs), Shared Conventional Vehicles (SCVs), and Conventional Private Vehicles (CPVs). This study fills this gap by developing an agent-based simulation model to examine the spatially and temporally explicit parking reduction trends with mixed travel modes from 2020–2040. The results indicate that in the most optimal AV and MoD adoption scenario, the parking demand will decrease by over 20% after 2030, especially in core urban areas. Meanwhile, the parking demand in residential zones may double, which could lead to transportation equity concerns. Additionally, parking relocation may also induce environmental issues by generating a considerable amount of empty Vehicle Miles Traveled (VMT). To reap the benefits brought by AVs and MoD systems and to mitigate the accompanying social and environmental issues, our results suggest that proactive policymakers in the next decade will need to modify land use regulations for both new developments and existing parking infrastructure in commercial and residential zones, as well as update travel demand management policies.
We address a comprehensive ride-hailing system taking into account many of the decisions required to operate it in reality. The ride-hailing system is formed of a centrally managed fleet of autonomous electric vehicles which is creating a transformative new technology with significant cost savings. This problem involves a dispatch problem for assigning riders to cars, a surge pricing problem for deciding on the price per trip and a planning problem for deciding on the fleet size. We use approximate dynamic programming to develop high-quality operational dispatch strategies to determine which car is best for a particular trip, when a car should be recharged, when it should be re-positioned to a different zone which offers a higher density of trips and when it should be parked. These decisions have to be made in the presence of a highly dynamic call-in process, and assignments have to take into consideration the spatial and temporal patterns in trip demand which are captured using value functions. We prove that the value functions are monotone in the battery and time dimensions and use hierarchical aggregation to get better estimates of the value functions with a small number of observations. Then, surge pricing is discussed using an adaptive learning approach to decide on the price for each trip. Finally, we discuss the fleet size problem.
Crowd-shipping promises social, economic, and environmental benefits covering a range of stakeholders. Yet, at the same time, many crowd-shipping initiatives face multiple barriers, such as network effects, and concerns over trust, safety, and security. This paper reviews current practice, academic research, and empirical case studies from three pillars of supply, demand, and operations and management. Drawing on the observed gaps in practice and scientific research, we provide several avenues for promising areas of applications, operations and management, as well as improving behavioral and societal impacts to create and enable a crowd-shipping system that is complex, yet, integrated, dynamic and sustainable.
Technological advances in the past decades have sparked numerous creative and innovative solutions to lower costs for transportation companies. One such solution, adopted by Walmart and Amazon among others, is crowdshipping, i.e. getting ordinary people, who already have a planned route, to take a detour to pick up and deliver packages in exchange for a small compensation. We consider a setting in which a company not only has its own fleet of vehicles to service requests, but may also use the services of occasional drivers. These drivers are willing to take a detour to serve one or more transportation requests for a small compensation. This leads to a new extension of the pickup and delivery problem with time windows. Comparison between a compact and an extended formulation for the problem is performed, and the impact of reduction tests and symmetry breaking constraints is tested. The problem is solved to optimality for up to 70 requests. Three compensation schemes are introduced, and it is shown that the model gives cost savings of about 10–15%. Adding more complexity to the compensation schemes may yield larger savings.
As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded. © National Academy of Sciences: Transportation Research Board 2019.
We propose a service network design problem for the tactical planning of parcel delivery with autonomous vehicles in SAE level 4. We consider a heterogeneous infrastructure wherein such vehicles may only drive in feasible zones but need to be guided elsewhere by manually operated vehicles in platoons. Our model decides on the fleet size and mix as well as on the routing of vehicles and goods. We observe cost savings and show that the strategies to coordinate a fleet using platooning depend upon the infrastructure, demand, and fleet mix. We discuss our results and identify areas for future research.
This study analyzes the potential benefits and drawbacks of taxi sharing using agent-based modeling. New York City (NYC) taxis are examined as a case study to evaluate the advantages and disadvantages of ride sharing using both traditional taxis (with shifts) and shared autonomous taxis. Compared to existing studies analyzing ride sharing using NYC taxi data, our contributions are that (1) we proposed a model that incorporates individual heterogeneous preferences; (2) we compared traditional taxis to autonomous taxis; and (3) we examined the spatial change of service coverage due to ride sharing. Our results show that switching from traditional taxis to shared autonomous taxis can potentially reduce the fleet size by 59% while maintaining the service level and without significant increase in wait time for the riders. The benefit of ride sharing is significant with increased occupancy rate (from 1.2 to 3), decreased total travel distance (up to 55%), and reduced carbon emissions (up to 866 metric tonnes per day). Dynamic ride sharing, wich allows shared trips to be formed among many groups of riders, up to the taxi capacity, increases system flexibility. Constraining the sharing to be only between two groups limits the sharing participation to be at the 50–75% level. However, the reduced fleet from ride sharing and autonomous driving may cause taxis to focus on areas of higher demands and lower the service levels in the suburban regions of the city.
Automated vehicle technology presents an opportunity to remake urban mobility in a way that maximizes access, efficiency, and equity. One of the roles for policymakers is to ensure that future governance of automated vehicles (AVs) achieves this. When considering governance, the current literature centers on issues related to the safe operation and deployment of AVs but has not fully considered the implications of AV ownership and ridesourcing platform data propriety on achieving the most desirable urban mobility outcomes. Specifically, the literature has not considered: a future scenario where individually owned AVs are shared when not in use; and the implications of ridesourcing platform data remaining proprietary in future. This paper analyzes why: the future of AV ownership may not be a binary choice between owning an AV/not sharing and sharing an unowned fleet, which is the current consensus in the literature; the incentives for consumers to simultaneously own an AV and share it when they are not using it could be high; the way ridesourcing platform data is collected, used, and shared could be a very influential factor for urban mobility outcomes, but its implications have not been robustly analyzed in the literature; and future scenario-building and modeling should consider the implications of widespread sharing of individually owned AVs, as well as the implications of ridesourcing platform data propriety on urban mobility outcomes. Developing a foundation for future good governance of AV ownership and ridesourcing platform data propriety should be an immediate priority for researchers, policymakers, and practitioners.
Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.
The trend toward shorter delivery lead times reduces operational efficiency and increases transportation costs for Internet retailers. However, mobile technology creates new opportunities to organize the last mile. In this paper, we study the concept of crowdsourced delivery that aims to use excess capacity on journeys that already take place. We consider a service platform that automatically creates matches between parcel delivery tasks and ad hoc drivers. The platform also operates a fleet of dedicated vehicles to serve the tasks that cannot be served by the ad hoc drivers. The matching of tasks, drivers, and dedicated vehicles in real time gives rise to a new variant of the dynamic pickup and delivery problem. We propose a rolling horizon framework and develop an exact solution approach to solve the matching problem each time new information becomes available. To investigate the potential benefit of crowdsourced delivery, we conduct a wide range of computational experiments. The experiments provide insights into the viability of crowdsourced delivery under various assumptions about the behavior of the ad hoc drivers. The results suggest that the use of ad hoc drivers has the potential to make the last mile more cost-efficient and can provide system-wide vehicle-mile savings up to 37% compared to a traditional delivery system with dedicated vehicles.
Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.
There has been a resurgence of interest in demand-responsive shared-ride systems, motivated by concerns for the environment and also new developments in technologies which enable new modes of operations. This paper surveys the research developments on the Dial-A-Ride Problem (DARP) since 2007. We provide a classification of the problem variants and the solution methodologies, and references to benchmark instances. We also present some application areas for the DARP, discuss some future trends and challenges, and indicate some possible directions for future research.
Dial-a-ride services provide collective on-demand transportation, usually tailored to the needs of people with reduced mobility. This paper investigates the operational effects of horizontal cooperation among dial-a-ride providers. The current practice is that users choose a particular provider to submit their requests. Providers operating in the same area create their routing solutions independently of each other, given their own set of customers. In contrast, horizontal cooperation through joint route planning implies that customer requests can be exchanged among providers in order to minimize the overall routing cost. In addition to quantifying the operational benefits generated by such a horizontal cooperation, this paper identifies operational characteristics that influence the magnitude of the savings. A real-life case study reveals the reasons why providers benefit from certain request exchanges, as well as the extent to which these exchanges are predictable in advance. The solutions are obtained using a large neighborhood search algorithm that performs well on benchmark data.
Technological advances, such as smart phones and mobile internet, allow for new and innovative solutions for transportation of goods to customers. We consider a setting where a company not only uses its own fleet of vehicles to deliver products, but may also make use of ordinary people who are already on the road. This may include people who visit the store, who are willing to take a detour on their way home for a small compensation. The availability of these occasional drivers is naturally highly uncertain, and we assume that some stochastic information is known about their appearance. This leads to a stochastic vehicle routing problem, with dynamic appearance of vehicles. The contribution of this paper is a mixed integer programming formulation, and insights into how routes for the company vehicles could be planned in such a setting. The results of the stochastic model are compared with deterministic strategies with reoptimization.
This paper introduces a new fully time-dependent model of a public transportation system in the urban context that allows sharing a taxi between one passenger and parcels with speed widows consideration. The model contains many real-life case features and is presented by a mathematical formulation. We study both static and dynamic scenarios in comparison to traditional strategies, i.e.,the direct delivery model. Moreover, we classify speed windows by different zones and congestion levels during a day in the urban context. Different speed windows induce the dynamic graph model for road networks and make the problem much more difficult to solve. Because of the complex model, the preprocessing steps on data as well as on dynamic graphs are very important. We use a greedy algorithm to initiate the solution and then use some local search techniques to improve the solution quality. The experimental data set is recorded by Tokyo-Musen Taxi company. The data set includes more than 20,000 requests per day, more than 4,500 used taxis per day and more than 130,000 crossing points on the Tokyo map. Experimental results are analyzed on various factors such as the total benefit, the accumulating traveling time during the day, the number of used taxis and the number of shared requests.
Autonomous vehicles are expected to offer a higher comfort of traveling at lower prices and at the same time to increase road capacity - a pattern recalling the rise of the private car and later of motorway construction. Using the Swiss national transport model, this research simulates the impact of autonomous vehicles on accessibility of the Swiss municipalities. The results show that autonomous vehicles could cause another quantum leap in accessibility. Moreover, the spatial distribution of the accessibility impacts implies that autonomous vehicles favor urban sprawl and may render public transport superfluous except for dense urban areas.
This paper presents a taxonomy for classifying vehicle fleet management
problems, across several dimensions, to inform future research
on autonomous vehicle (AV) fleets. Modeling the AV fleet management
problem will bring about new classes of vehicle routing, scheduling, and
fleet management problems; nevertheless, the existing literature related
to vehicle routing, scheduling, and fleet management is a valuable
foundation for future research on the AV fleet management problem.
This paper classifies the broadly defined AV fleet management problem
by using existing taxonomic categories in the literature; adds additional,
or more nuanced, dimensions to existing taxonomic categories; and
presents new taxonomic categories to classify specific AV fleet management
problems. The broadly defined AV fleet management problem can
be classified as a dynamic multivehicle pickup and delivery problem
with explicit or implicit time window constraints. Existing studies that fit
into this class of fleet management problems are reviewed. New taxonomy
categories presented in this paper include fleet size elasticity, reservation
structure, accept–reject decision maker, reservation time frame,
ridesharing, vehicle repositioning, underlying network structure, and
network congestion. Two goals of the taxonomy presented in this study
are to provide researchers with a valuable reference as they begin to
model AV fleet management problems and to present novel AV fleet
management problems to spur interest from researchers.
This paper advocates the need for infrastructure planning to adapt to and further promote the deployment of autonomous vehicle (AV) technology. It is envisioned that in the future government agencies will dedicate certain areas of road networks to AVs only to facilitate the formulation of vehicle platoons to improve throughput and hopefully improve the performance of the whole network. This paper aims to present a mathematical framework for the optimal design of AV zones in a general network. With the presence of AV zones, AVs may apply different routing principles outside of and within the AV zones. A novel network equilibrium model (we refer to it as the “mixed routing equilibrium model”) is thus firstly proposed to capture such mixed-routing behaviors. We then proceed to formulate a mixed-integer bi-level programming model to optimize the deployment plan of AV zones. Numerical examples are presented to demonstrate the performance of the proposed models.
Stochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost 50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems. In this paper, we first briefly sketch the state-of-The-Art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution methods that have been proposed to tackle them. We then identify and discuss two groups of critical issues and challenges that need to be addressed to advance research in this area. These revolve around the expression of stochastic phenomena and the development of new recourse strategies. Based on this discussion, we conclude the paper by proposing a number of promising research directions.
Automated vehicles (AVs) promise many benefits for future mobility. One of them is a reduction of the required total vehicle fleet size, especially if AVs are used predominantly as shared vehicles. This paper presents research on this potential reduction for the greater Zurich, Switzerland, region. Fleets of shared AVs serving a predefined demand were simulated with a simulation framework introduced in the paper. Scenarios combining levels of demand for AVs with levels of supply (i.e., AV fleet sizes) were created. An important contribution of this study is the use of travel demand at highly detailed spatial and temporal resolutions that goes beyond the simplifications used in previous studies on the topic. This detailed travel demand provides a more solid basis for the ongoing discussion about the future fleet size. It was found that for a given fleet performance target (here, the target was for 95% of all transport requests to be served within 5 min), the relationship between served demand and required fleet size was nonlinear and the ratio increased as demand increased. A scale effect was detected. This effect has the important implication that for different levels of demand the fleet is used more or less efficiently. This study also found that if waiting times of up to 10 min were accepted, a reduction of up to 90% of the total vehicle fleet could be possible even without active fleet management, like vehicle redistribution. Such effects require, however, that a large enough share of the car demand be served by AVs.