Shared Autonomous Vehicles Research Articles

The City-Wide Impacts of the Interactions between Shared Autonomous Vehicle-Based Mobility Services and the Public Transportation System

When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.

Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

The opportunity of shared autonomous vehicles to improve spatial equity in accessibility and socio-economic developments in European urban areas

BackgroundThis paper provides insight into the opportunity offered by shared autonomous vehicles (SAVs) to improve urban populations’ spatial equity in accessibility. It provides a concrete implementation model for SAVs set to improve equity in accessibility and highlights the need of regulation in order for SAVs to help overcome identified spatial mismatches.MethodologyThrough the formulation of linear regression models, the relationship between land-use and transportation accessibility (by car and public transport) and socio-economic well-being indicators is tested on district-level in four European cities: Paris, Berlin, London and Vienna. Accessibility data is used to analyse access to points of interest within given timespans by both car and public transport. To measure equity in socio-economic well-being, three district-level proxies are introduced: yearly income, unemployment rate and educational attainment.ResultsIn the cities of Paris, London and Vienna, as well as partially in Berlin, positive effects of educational attainment on accessibility are evidenced. Further, positive effects on accessibility by yearly income are found in Paris and London. Additionally, negative effects of an increased unemployment rate on accessibility are observed in Paris and Vienna. Through the comparison between accessibility by car and public transportation in the districts of the four cities, the potential for SAVs is evidenced. Lastly, on the basis of the findings a ‘SAV identification matrix’ is created, visualizing the underserved districts in each of the four cities and the need of equity enhancing policy for the introduction of SAVs is emphasized.

Multi-modal morning commute with endogenous shared autonomous vehicle penetration considering parking space constraint

Autonomous vehicles are poised to generate revolutionary impact on urban mobility and parking availability. This study aims to model the morning commute dynamics in a multi-modal transportation system and examine how to regulate the market in the presence of parking space constraint and shared autonomous vehicles (SAVs). Other than the SAVs, commuters can also choose to take regular vehicles (RVs) or public transit (PT) to travel from home to workplace. SAVs will compete the bottleneck capacity with RVs. The dynamic departure patterns and endogenous penetration rate of SAVs are determined depending on parking supply and other related factors. The conditions for various portfolios of travel modes are derived in details. Analytical propositions, numerical examples, as well as policy implications are given with respect to different market factors such as the additional SAV cost, and parking supply. The results show that there are two possible system optimal solutions depending on the value of transit fare. When the transit fare is low, the system optimum occurs in Scenario CS II&III, wherein all three travel modes are used, the first SAV commuter arrives at the bottleneck just when the last RV commuter leaves, and the parking supply and the additional SAV cost should be controlled to a suitable value; when the transit fare is relatively high, the system optimum appears in Scenario IV, an extreme scenario wherein the parking supply is zero, and only the travel modes of PT and SAVs are used. This paper is helpful in quantifying the impacts of new vehicle technologies and shedding lights on future mobility and parking management.

A study of the scheduling effect on shared autonomous vehicles adoption

Adopting the concept of ridesharing, shared autonomous vehicles (SAVs) are introduced to provide low-cost and convenient mobility-on-demand services. Previous studies have investigated the trip purposes and user's characteristics in adopting on-demand ride services and SAVs. However, very little attention has been paid on the traveler's trip making decisions, including departure time choice and mode choice. Hence, this study aims to conduct the Revealed Preference (RP) and Stated Preference (SP) surveys and to develop a joint model of mode and departure time choice to determine the contributing factors affecting traveler’s choice decisions, particularly concerning SAV adoption. Both the RP and SP survey suggest that middle-income travelers with an age range between 20 and 39 years are more likely to adopt SAVs. It was found that the travel time and walking time were the two most significant service attributes in SAV adoption. The increase of 10% of SAV travel time and the introduction of 2 min walking time reduce the SAV adoption by 15% and 90%, respectively. In addition to that, the results suggest that the value of schedule delay early (SDE) is much lower than the value of time and schedule delay late (SDL), as more than 80% of travelers prefer to depart early. These findings can be used to further assist in developing dynamic traffic modeling to aid travel demand management (i.e., congestion charging) that involves SAVs adoption.

A flow-based integer programming approach to design an interurban shared automated vehicle system and assess its financial viability

Shared automated vehicles (SAV) are expected to become part of on-demand transport systems in the future. In an interurban context, SAV systems may potentially contribute to significantly improve the availability and quality of public transport. In this paper, we propose a flow-based integer programming approach to design and assess the financial viability of a SAV interurban system. An integer programming model, based on profit maximization and allowing vehicle pooling, was developed to optimize the fleet size and vehicle movements of a typical working day. This model and its optimal results were the basis for assessing the financial viability of an interurban transport network in the central area of Portugal. Different levels of demand were tested and three different fleet compositions were considered: cars, minibuses, and a mixed fleet of both. The results showed that a mixed fleet of vehicles is the best option for profit maximization. The optimal share of each vehicle type, in this fleet composition, asymptotically converges to approximately 1/3 of cars and 2/3 of minibuses, as demand increases. The introduction of a SAV system in such a region for interurban trips can replace up to 18 private vehicles per SAV vehicle, and should be done with sequential expansions to guarantee a profit at the different stages. The break-even point, for the conditions of the case study, is between 1.5 and 2 passengers per vehicle, for a fleet of cars, and between 4 and 5 passengers per vehicle, for a fleet of minibuses.

Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach

Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.

Maximum-stability dispatch policy for shared autonomous vehicles

Shared autonomous vehicles (SAVs) have been widely studied in the recent literature. Agent-based simulations and theoretical models have extensively explored the effects on travel service, fleet size, and congestion using heuristic dispatching strategies to match SAVs with on-demand passengers. A major question that simulations have sought to address is the service rate or replacement rate: the number of passengers each SAV can serve. Thus far, the service rate has mostly been estimated through simulation. This paper investigates an analytical max-pressure dispatch policy, which aims to maximize passenger throughput under any stochastic demand pattern, which takes the form of a model predictive control algorithm. An analytical proof using Lyapunov drift techniques is presented to show that the dispatch policy achieves maximum stability. The service rate and minimum fleet sizes are derived analytically in this paper and can be achieved with the proposed dispatch policy. Simulation results show that the maximum stable demand is linearly related to the fleet size given. Also, it demonstrates how asymmetric demand necessitates rebalancing trips that affect service rates. Even though decreasing average waiting time is not the primary goal of this paper, stability ensures bounded waiting times, which is demonstrated in simulation.

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs’ dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

Competition between shared autonomous vehicles and public transit: A case study in Singapore

Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it. This study examines the competitive perspective where both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one focuses on unconstrained competition to find the Nash Equilibrium. We evaluate the competition process as well as the system performance from the standpoints of four stakeholders—the AV operator, the PT operator, passengers, and the transport authority. We also examine the impact of PT subsidies on the competition results including both demand-based and supply-based subsidies. A heuristic algorithm is proposed to update supply strategies for AV and PT based on the operators’ historical actions and profits. An agent-based simulation model is implemented in the first-mile scenario in Tampines, Singapore. We find that the competition can result in higher profits and higher system efficiency for both operators compared to the status quo. After the supply updates, the PT services are spatially concentrated to shorter routes feeding directly to the subway station and temporally concentrated to peak hours. On average, the competition reduces the travel time of passengers but increases their travel costs. Nonetheless, the generalized travel cost is reduced when incorporating the value of time. With respect to the system efficiency, the bus supply adjustment increases the average vehicle load and reduces the total vehicle kilometers traveled measured by the passenger car equivalent (PCE), while the AV supply adjustment does the opposite. The results suggest that PT should be allowed to optimize its supply strategies under specific operation goals and constraints, and AV operations should be regulated to reduce their system impacts, including potentially limiting the number of licenses, operation time, and service areas, which makes AV operate in a manner more complementary to the PT system. Providing subsidies to PT results in higher PT supply, profit, and market share, lower AV supply, profit, and market share, and increased passenger’s generalized cost and total system PCE.

Assessing the Access to Jobs by Shared Autonomous Vehicles in Marysville, Ohio: Modeling, Simulating and Validating

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting. To simulate the aforementioned applications, a simulation environment that has a realistic rendering of the chosen real-world environment with realistic traffic generated around the SAVs is developed first using a geo-fenced area centered at the city of Marysville in Ohio as an example. This paper, then, presents an algorithm to optimally utilize multiple autonomous vehicles for shared rides based on modeling of pickup locations corresponding to affordable housing at the periphery of the geo-fenced area connected to destination locations corresponding to jobs and other locations of opportunity. The presented work showcases SAV operation as a solution to the spatial mismatch between affordable housing and job locations in a realistic simulation environment in an urban setting.</div></div>

Guest Editorial Diversification in Urban Transportation Systems and Beyond: Integrating People and Goods for the Future of Mobility

The increasing growing need for optimization of transportation in a sustainable and green environment is fundamental for the future of mobility. Companies, governments, and non-governmental institutions are trying to find new ways to contribute to this challenge. In particular, the approach is to improve the efficiency and the quality of the movement of diversification of an integrated way of transportation of people and goods. Many people may need to be at some location at some point in time, or many goods may need to be transported to nearby destinations with the same packaging conditions. The main goal of this Special Issue was to integrate the most recent advances in transportation that connect the hybrid point of view. In particular, key contributions integrate several interesting topics related to earning algorithms for the integration of passengers and goods, gamification techniques, semantic data, blockchain, and edge computing. Other topics in urban mobility, railway, and highways were considered, and hot topics on shared autonomous vehicles (SAVs) brought huge value. Moreover, the Internet of Things (IoT) opens the doors for new development in Vehicle to Everything (V2X), which are crucial for the development of new services that generate business and social impact in the future of a diversified way of the integration of passengers and goods mobility.

V2G-capable shared autonomous electric vehicles fleet: Economic viability and environmental co-benefits

V2G-capable shared autonomous electric vehicles fleet: Economic viability and environmental co-benefits

Sustainable Renewal Methods of Urban Public Parking Spaces under the Scenario of Shared Autonomous Vehicles (SAV): A Review and a Proposal

Shared autonomous vehicles (SAVs) will be an important force to in reshaping urban morphology. The high operation rate and sharing degree of SAV are considered to result in a great reduction in parking area in future cities. Parking space is now a huge and widely distributed urban stock space type, which is bound to become a major challenge and opportunity for sustainable urban renewal in the digital era. Based on the SAV scenario, this paper reviews the current research on the sustainable renewal of urban public parking spaces, and proposes the four key issues involved: how much to renew (i.e., demand forecast analysis), when to renew (i.e., update time series evaluation), what to renew (i.e., function replacement decision) and how to update (i.e., design empirical research). Furthermore, it puts forward a preliminary idea on, and constructs a research framework for, the sustainable renewal methods of parking space under the SAV scenario. Finally, the theoretical, practical and policy implications of the research on sustainable renewal methods of urban public parking space are discussed. It will have great reference value for the redevelopment and reuse of the urban space types including fragmented, widely distributed and large-scale.

Identifying the Potential for Partial Integration of Private and Public Transportation

In this paper, we explore the potential for a partial integration of private and public transportation as provided by taxi and bus services on the level of individual trips. While these modes are typically separated for economic reasons, in the future with shared Autonomous Vehicles (AVs) providing cheap and efficient transportation services, such distinctions will blur. Consequently, optimization based on real-time data will allow exploiting parallels in demand in a dynamic way, such as the proposed approach in the current work. New operational and pricing strategies will then need to be considered, along with regulatory tools to limit congestion, encourage sharing and provide services in a more sustainable way by utilizing a dynamic landscape of urban transportation. In the current work, we evaluate existing parallels between individual bus and taxi trips in two Asian cities and show how exploiting these synergies could form the basis of achieving some of the above goals in the future.

The role of urban form in the performance of shared automated vehicles

The technology of Shared Automated Vehicles (SAVs) has advanced significantly in recent years. However, existing SAV studies primarily focus on the system design, while limited studies have examined the impacts of exogenous variables, especially urban form, on SAV performance. Therefore, it remains unclear what key urban form measurements may influence SAV systems’ sustainability. This study fills the research gap by conducting simulation experiments using data collected from 286 cities. This study identifies critical urban form measurements correlated with the simulated SAV performance using fixed effects regression models. The results suggest that SAVs are more efficient and generate less VMT in denser cities with more connected networks and diversified land use development patterns. The model results can provide insights on land use and transportation policies to curb the adverse effects of SAVs in the future and generalize existing SAV simulation results to the rest of U.S. cities.

Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method

The use of autonomous vehicles (AVs) has the potential to transform users’ behaviour and urban space management. Quantitative and qualitative analyses of impacts require a scenario building method. We considered the fleet size, modal share, car ownership, parking preferences, and urban space repurposing during the elaboration of a novel method. Existing scenarios and results of a questionnaire survey have been used as sources. The method was applied to build scenarios in a case study in Budapest, Hungary. The results were used to calculate the impacts on urban space management, including environmental savings. The key findings are: scenarios with significant shared AV use show that parking demand may be minimised (almost 83%) and urban space repurposing has the highest potential; furthermore, AV use and sharing acceptability may decrease the fleet size and alter the type of shared mode to multiple occupancies. The developed scenario building method serves as a base for future studies. The produced scenarios allow the researchers to focus on the analysis of the impacts caused.

Compatibility of Automated Vehicles in Street Spaces: Considerations for a Sustainable Implementation

Automated Vehicles (AVs) will bring a fundamental change in the mobility sector in the coming years. Whereas many studies emphasize opportunities with AVs, studies on the impacts of AVs on travel behavior particularly show an overall increase in traffic volume. This increase could impair the needs of other uses and users within street spaces and decrease the permeability of the street space for pedestrians and cyclists. However, only a few studies, so far, have looked at the changes of traffic volume due to AVs at the street level, and to what extent these impair the needs of other uses and users within different street spaces was not in the focus at all. This paper investigates the compatibility of AVs in street spaces, building on different modeling results of scenarios with AVs based on the Multi-Agent Traffic Simulation (MATSim) framework. Using the so-called compensatory approach and the whole street network of Vienna, Austria, as a case study, we examine how compatible AVs and their related changes in traffic volume are with the needs of other uses and users, i.e., pedestrians and cyclists, within different street spaces, by specifically considering the various characteristics of the latter. Results show that the effects of AVs on the compatibility of street spaces would be unevenly distributed across the city. For Shared Automated Vehicles (SAVs), a deterioration in compatibility is observable, especially in inner-city dense areas, because of an increase in traffic volume and an already high amount of competing uses. In contrast, especially (on main roads) in the outskirts, improvements in compatibility are possible. This particularly applies to SAVs with a stop-based service. However, private AVs interlinked with an overall capacity increase would lead to a deterioration in compatibility, especially in parts of the higher-level street network that already have incompatible traffic volumes, further increasing the separating or barrier effect of such streets. The results can provide insights for policymakers and stakeholders about where and how to facilitate AVs, to reach an implementation that is compatible with the different uses and needs of users within street spaces: While SAVs should be implemented particularly in the outskirts, as a complement for public transport, an implementation of AVs in the lower-level street network in inner parts of the city should not be facilitated, or it should at least be linked to measures that make street spaces more compatible with the needs of pedestrians and cyclists, e.g., implementation of walking and cycling infrastructure.

Multi-objective framework for optimum configuration of human-driven and shared or privately owned autonomous vehicles

Private Autonomous Vehicles (PAV) and Shared Autonomous Vehicles (SAV) are known to improve safety, mobility, roadway capacity, driver productivity and reduce parking costs (due to better utilization of space and self-parking to less expensive spots). However, the increased vehicle miles traveled (VMT), might increase overall emission production, system travel time, and operating costs. Also, the purchase price of autonomous vehicle (AV) is expected to be higher than that of human-driven (conventional) vehicles. A multi-objective mathematical model is proposed to minimize the purchase and operating costs, time spent, and emission production. The proposed model captures the tradeoff between the benefits of increased mobility, reduction in the value of travel of time (VOTT), efficient driving pattern, and the negative impacts of increased VMT, ownership cost due to the adoption of AVs. The proposed framework assists with the development of simplified adoption models that can be used by the policymakers and/or investors. SAVs would be the optimal solution if the replacement rate or CO2 costs are significantly low, or CO costs are sufficiently high. SAVs can also be the optimal solution if the travel time is used efficiently, or the purchase price is below certain relative threshold while minimizing system cost. Considering the private mobility system, PAVs can be the optimal solution only if the on-board amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and is being adopted by people with high VOTT.

Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study

Most existing shared automated mobility (SAM) services assume the door-to-door manner, i.e., the pickup and drop-off (PUDO) locations are the places requested by the customers (or demand-side). While some mobility services offer more affordable riding costs in exchange for a little walking effort from customers, their rationales and induced impacts (in terms of mobility and sustainability) from the system perspective are not clear. This study proposes a demand-side cooperative shared automated mobility (DC-SAM) service framework, aiming to fill this knowledge gap and to assess the mobility and sustainability impacts. The optimal ride matching problem is formulated and solved in an online manner through a micro-simulation model, Simulation of Urban Mobility (SUMO). The objective is to maximize the profit (considering both the revenue and cost) of the proposed SAM service, considering the constraints in seat capacities of shared automated vehicles (SAVs) and comfortable walking distance from the perspective of customers. A case study on a portion of a New York City (NYC) network with a pre-defined fleet size demonstrated the efficacy and promise of the proposed system. The results show that the proposed DC-SAM service can not only significantly reduce the SAV’s operating costs in terms of vehicle-miles traveled (VMT), vehicle-hours traveled (VHT), and vehicle energy consumption (VEC) by up to 53, 46 and 51%, respectively, but can also considerably improve the customer service by 30 and 56%, with regard to customer waiting time (CWT) and trip detour factor (TDF), compared to a heuristic service model. In addition, the demand-side cooperation strategy can bring about additional system-wide mobility and sustainability benefits in the range of 4–10%.