Shared Autonomous Vehicles Research Articles

Benefits and Costs of Ride-Sharing in Shared Automated Vehicles across Austin, Texas: Opportunities for Congestion Pricing

A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.

Carsharing with shared autonomous vehicles: Uncovering drivers, barriers and future developments – A four-stage Delphi study

Autonomous vehicles are expected to disrupt mobility but face consumer reluctance. Diffused through carsharing services, the technology could become more accessible and overcome initial skepticism. Consequently, carsharing with shared autonomous vehicles (SAVs) can provide the combined benefits of autonomous driving technology and access-based consumption. Whereas first investigations into the topic provide projections in adoption preferences and first impacts, the literature lacks a holistic understanding of drivers, barriers, and future developments in carsharing with SAVs over the next ten years. We conducted a four-stage exploratory Delphi-study with 40 international experts to elicit these factors. Key findings include the strong perception of technological aspects, consumer acceptance considerations, and legislative concerns. Remarkably, the factors of sustainability and ethics were perceived as secondary. We conclude our paper with implications and recommendations for managers, policy makers, and future academic research.

The morning commute problem with endogenous shared autonomous vehicle penetration and parking space constraint

Morning commuting trips commonly involve a number of transportation modes and departure times. This study extends the standard bottleneck model by considering both regular and shared autonomous vehicles with and without parking space constraint, taking into account the travel time-dependent fuel cost that is associated with each vehicle. In this study the parking space constraint has no effect on shared autonomous vehicles and only commuters who share autonomous vehicles exhibit ridesharing behavior, differing from those utilizing regular vehicles. The dynamic departure patterns and endogenous penetration rates associated with shared autonomous vehicles are determined with respect to parking capacity. Analytical results not only provide several important propositions, but also include the optimal solutions for parking capacity and ridesharing occupancy from the perspective of the system optimum in terms of various indicators, under which parking capacity is a binding constraint, and the first shared autonomous vehicle arrives at the bottleneck as the last regular vehicle leaves. This work is expected not only to motivate related research, but also to promote the development of new strategies and methods for allocating a limited number of parking spaces and regulating the travel behavior of commuters in urban areas.

A Decision Support System for On-Demand Goods Delivery Using Shared Autonomous Electric Vehicles

This article presents a strategic decision support system (DSS) for on-demand delivery companies in urban areas. This DSS is designed and developed for the promising new concept of goods delivery based on a fleet of Shared Autonomous Electric Vehicles (SAEVs). A simulation-based optimization model is proposed to solve the fleet sizing and composition problems. The efficiency of the developed strategic DSS in determining best fleet size and composition under different scenarios is demonstrated. This article provides managerial insights to help goods delivery companies, who intend to use SAEVs, in determining the type and number of vehicles to acquire.

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied to transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous vehicle (SAV). To promote AV technology, it is important to understand which factors influence travelers’ intention to use AVs and SAVs. This paper collected literature from databases such as Scopus, Web of Science and ScienceDirect, and made a systematic review. The study aims to explore the determinants that influence travelers’ behavioral intentions towards use AVs and SAVs based on an extended version of the theory of planned behavior, which incorporates knowledge and perceived risk. This study was tested empirically using a valid survey sample collected from 906 respondents in China. Structural equation model was conducted to investigate the predictors of intentions to use AVs and SAVs. Results showed that knowledge about AV technology and perceived risk are the two main potential obstacles for travelers to use AVs and SAVs. Attitude significantly affects AVs and SAV choice intentions. Subjective norm is the most critical factor affecting the travelers’ intention to use AVs. Perceived behavioral control potentially stymie the travelers’ intention to use SAVs. The findings will enhance the understanding of travelers’ choice motivation from psychological and service perspectives, and provide data support for governments and companies in improving travel management strategies and product services.

Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas

Shared Autonomous Vehicles (SAVs) have gained significant public interest as a possible less expensive, safer and more efficient version of today’s transportation networking companies (TNCs) and taxis. One way to expand on the possible benefits of an SAV fleet is through electric vehicles (EVs), which tend to be more energy efficient, more reliable, quicker, and may reduce system-wide emissions when coupled with renewable power. EVs are quickly becoming more financially viable as the price of these vehicles drops and charging infrastructure is appearing in more and more locations across the world. EVs are disadvantaged by their relatively short range and long recharge times, so it is important to understand how these factors will affect an electrified SAV (SAEV) fleet in terms of vehicle miles traveled (VMT), vehicle productivity, and response times.Perhaps the most important factor to consider before implementation is cost, since it is quite unlikely that a fleet operator will elect to use an EV fleet when a gasoline fleet is more profitable. This study makes in-depth estimates of the cost of this SAEV fleet based on vehicle purchasing costs, vehicle maintenance, batteries, electricity, charger construction (including land acquisition and paving), charger maintenance, insurance, registration and general administrative costs. These costs are estimated at low-, high- and mid-cost scenarios, where mid-cost is the most expected.This study performed a simulation of SAEVs across the Austin, Texas 6-county region under 6 different fleet scenarios to assess what factors make the fleet the most profitable and provide the best customer experience. The simulation process features thoughtful charging strategies, dynamic ridesharing, mode choice, and a multi-step search algorithm. Results showed that for all metrics studied, the gasoline hybrid-electric (HEV) fleet performed better than EV fleets, while remaining more profitable, providing response times of 4.5 min compared to 5.5 min. The HEV fleet is the more profitable option until the cost of gasoline exceeds $10 per gallon or the cost of a long-range EV falls below $16,000 through subsidies. Of all the EVs studied, the long-range fast-charging scenario not only provides the best service in terms of all metrics studied, but is by far the most profitable. Even though EVs may not be financially advantageous in the near term, the environmental benefits could be substantial; EVs have the potential to provide zero-carbon transportation when coupled with a renewable power grid. Gasoline vehicles have no such potential. Environmentalism tends to have little effect on financial decisions, but a carbon tax could change that perspective.

Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

The emergence of autonomous vehicles (AVs) may shape the future landscape of urban mobility. Although there is a growing literature on public opinions with regard to self-driving, little attention has been explicitly given to the commuters and their preferences for their commute trips. Using data from the 2015 and 2017 Puget Sound Regional Household Travel Studies, this study investigates the factors associated with employees’ propensity for using AVs. We develop a bivariate ordered probit model to jointly estimate the determinants of levels of interest in (i) commuting alone using AVs and (ii) commuting with others using shared AVs. Not surprisingly, it was found that current solo drivers are more likely to commute alone using AVs compared with other mode users. Significant differences were not found between current drivers and other commuters when it comes to the potential use of shared AVs. The results also reveal that, controlling for other factors, commuters surveyed in 2017 are less likely to be interested in shared AVs compared with their 2015 counterparts. The conclusion is that more planning efforts are needed to support the market penetration of shared AVs.

Optimization of shared autonomous electric vehicles operations with charge scheduling and vehicle-to-grid

Shared autonomous electric vehicles, also known as autonomous mobility on demand systems, are expected to become commercially available by the next decade. In this work we propose a methodology for the optimization of their charging with vehicle-to-grid in parallel with optimized routing and relocation. The methodology presented is based on previous work expanded to include charge optimization. The proposed model optimizes transport service and charging at two different time scales by running two model-predictive control optimization algorithms in parallel. Charging is optimized over longer time scales to minimize both approximate waiting times and electricity costs. Routing and relocation are optimized at shorter time scales to minimize waiting times, with the results of the long-time-scale optimization as charging constraints. This approach allows efficient optimization of both aspects of system operation. The problem is solved as a mixed-integer linear program. A case study using transport and electricity price data for Tokyo is used to test the model. Results show that the system can substantially reduce charging costs without significantly affecting waiting times, with cost reduction dependent on electricity price variability. Vehicle-to-grid is shown to be unsuitable for current electricity and battery prices, however offering substantial savings with price profiles with higher variability.

Exploring the Impact of User Preferences on Shared Autonomous Vehicle Modal Split: A Multi-Agent Simulation Approach

Abstract Shared autonomous vehicles are considered to have a transformative impact on future urban transportation and especially Shared Mobility. In order to assess the transport-related impact of this new mode, various models and simulations are under development. The majority of recently proposed simulations are based on activity-based multi-agent approaches. Thanks to the disaggregated level of data in multi-agent simulation, the traveler decision-making mechanisms might be individualized according the attributes. In this paper, we try to take in advantage of this granularity in order to explore the impact of user preferences on the modal split of shared autonomous vehicles. To illustrate the proposed methodology the transport system of Paris is simulated by using an activity-based multi-agent tool called MATSim. The traveler preferences toward shared autonomous vehicles use are also summarized based on the literature review.

Determining an Optimal Fleet Size for a Reliable Shared Automated Vehicle Ride-Sharing Service

Shared Automated Vehicles (SAVs) have the potential to revolutionize the urban transport landscape by reducing congestions, air pollution, and traffic accidents. However, the rejection rate for the travelers’ requests can jeopardize the potential adoption of SAVs as a new sustainable mode. We present MATSim simulations of SAVs service requests and rejections in the Tel-Aviv Metropolitan Area (TAMA) in Israel and demonstrate that fleets of 50-150K vehicles could well serve the entire intra-metropolitan travel demand, with an average occupancy of ~2 compared to 1.1 passengers per vehicle today. Minimal fleet size of 50K SAVs is sufficient for serving TAMA users’ activities but carries a high level of daily rejections 6%. An increase to 100K vehicles reduces the overall rejection rate to 1.66% with the rejection rate for trips between the TAMA core and outskirts remaining higher than 20%. A larger fleet size does not seem to improve the level of service significantly. The operational implications for optimal fleet size determination are further discussed.

Should autonomous shared taxis replace buses? A simulation study

Abstract The introduction of shared autonomous vehicles (SAV) will likely reduce operation cost per vehicle and might thereby allow to enhance conventional public transit systems with numerous small SAVs offering flexible ridesharing-like feeder services. These demand-responsive services could replace conventional bus lines limited by their fixed routes and their fixed schedules. This simulation study explores the potential of replacing conventional bus lines with shared autonomous vehicles in a suburban area of Berlin. Several scenarios with different fleet sizes and vehicle sizes are simulated using the multi agent transport simulation MAT-Sim. The simulation suggests for all evaluated scenarios higher operating costs and only slight travel time savings in comparison to conventional buses. Door-to-door service allows for significant reductions in average walk time, but causes numerous detours which consume a high share of the time gained. A fleet of 150 SAVs with 4 seats each seemed appropriate for the simulated area with approximately 24 000 inhabitants.

Impact assessment of autonomous DRT systems

Abstract The market entrance of shared autonomous vehicles (SAV) may have disruptive effects on current transport systems and may lead to their total transformation. For many small and medium-sized cities, a full replacement of public transport services by these systems seems to be possible. For a transport system operator, such a system requires a bigger fleet of vehicles than before, however, vehicles are less expensive and fewer staff is needed for the actual operation. In this paper, we are using a simulation-based approach to evaluate the service quality and operating cost of a demand responsive transit (DRT) system for the city of Cottbus (100 000 inhabitants), Germany. The simulation model used is based on an existing MATSim model of the region that depicts a typical work day. Results suggest, that the current public transport system may be replaced by a system of 300 to 400 DRT vehicles, depending on their operational mode. Compared to previous, schedule based public transport, passengers do not need to transfer, and their overall travel times may be reduced significantly. Results for the cost comparison are preliminary, but results suggest that an autonomous DRT system is not necessarily more expensive than the current public transport system.

Costs and carbon emissions of shared autonomous electric vehicles in a Virtual Power Plant and Microgrid with renewable energy

Shared autonomous electric vehicles (SAEVs) are expected to become commercially available within the next decade. This technology could transform transport paradigms and alter the availability of controllable storage from electrified transportation. This work describes a novel simulation methodology for investigating the potential for SAEVs to act as storage in the framework of a Virtual Power Plant or a microgrid with intermittent renewable energy. The model simulates aggregate storage availability from vehicles based on transport patterns and optimizes charging. We study the case of a grid-connected VPP with rooftop solar and the case of a isolated microgrid with solar, wind, and dispatchable generation. The results show that SAEVs offer significantly lower costs compared to private vehicles. SAEVs can also substantially increase renewable energy utilization in a microgrid.

Modular dynamic ride-sharing transport systems

It is widely recognised that digital technology – and in particular dynamic ride sharing systems (DRS) – is leading to fundamental changes to vehicular transport and in particular, offers the prospect of substantially more cost effective forms of transport. A much anticipated transformational wave is that of autonomous vehicles (AVs) which carry the prospect of considerably cheaper and far more convenient individualised transport. That eventuality when married to DRS in the form of shared AVs (SAVs) — is seen as individualised transport’s ‘killer app’ with the attendant risk of its convenience and low cost drawing clients away from public transport systems.To address this problem digital technology is now enabling hybrid modular transport systems (MTS) designed to act variously as a rapid (bus like) transit system, a feeder role for mass transit systems or, alternatively, a low density urban DRS service.In this article a road based modular DRS (MDRS) is described which is comprised of low capacity (up to 10 people) pods which can be articulated into a single vehicle or dynamically separated into individual transport units. There is, therefore, the potential when the pods are detached, to be used for off-peak low capacity last mile feeder services. When the pods are coupled together in bus length vehicles, they can act as a quasi-rapid transit system or as a feeder to them. Such a modular DRS system (MDRS) is shown to have the potential to reduce congestion through decreasing the size and number of vehicles at particular periods of the urban transport cycle. A further major increase in cost effectiveness is envisaged to be an urban based autonomous MDRS system which captures the key cost and convenience benefits of AVs and SAVs while curbing excess use of individual vehicles and abandonment of public transport.

CONGESTION EFFECTS OF AUTONOMOUS TAXI FLEETS

Fleets of shared Autonomous Vehicles (AVs) could replace private cars by providing a taxi-like service but at a cost similar to driving a private car. On the one hand, large Autonomous Taxi (AT) fleets may result in increased road capacity and lower demand for parking spaces. On the other hand, an increase in vehicle trips is very likely, as travelling becomes more convenient and affordable, and additionally, ATs need to drive unoccupied between requests. This study evaluates the impact of a city-wide introduction of ATs on traffic congestion. The analysis is based on a multi-agent transport simulation (MATSim) of Berlin (Germany) and the neighbouring Brandenburg area. The central focus is on precise simulation of both real-time AT operation and mixed autonomous/conventional vehicle traffic flow. Different ratios of replacing private car trips with AT trips are used to estimate the possible effects at different stages of introducing such services. The obtained results suggest that large fleets operating in cities may have a positive effect on traffic if road capacity increases according to current predictions. ATs will practically eliminate traffic congestion, even in the city centre, despite the increase in traffic volume. However, given no flow capacity improvement, such services cannot be introduced on a large scale, since the induced additional traffic volume will intensify today’s congestion.

Shared versus private mobility: Modeling public interest in autonomous vehicles accounting for latent attitudes

Emerging autonomous vehicles (AVs) and shared mobility systems per se will transform urban passenger transportation. Coupled together, shared AVs (SAVs) can facilitate widespread use of shared mobility services by providing flexible public travel modes comparable to private AV. Hence, it may be conjectured that future urban mobility is likely an on-demand service and AV private ownership is unappealing. Nonetheless, it is still unclear what observable and latent factors will drive public interest in (S)AVs, the answer to which will have important implications on transportation system performance. This paper aims to jointly model public interest in private AVs and multiple SAV configurations (carsharing, ridesourcing, ridesharing, and access/egress mode) in daily and commute travels with explicit treatment of the correlations across the (S)AV types. To this end, multivariate ordered outcome models with latent variables are employed, whereby latent attitudes and preferences describing traveler safety concern about AV, green travel pattern, and mobility-on-demand savviness are accounted for using structural and measurement equations. Drawing from a stated preference survey in the State of Washington, important insights are gained into the potential user groups based on the socio-economic, built environment, and daily/commute travel behavior attributes. Key policies are also offered to promote public interest in (S)AVs by scrutinizing the marginal effects of the latent variables.

The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid

The introduction of shared autonomous electric vehicles (SAEVs), expected within the next decade, can transform the car into a service, accelerate electrification of the transport sector, and allow for large scale control of electric vehicle charging. In this work, we investigate the potential for this system to provide aggregated storage when combined with intermittent renewable energy sources. We develop a simulation methodology for the optimization of vehicle charging in the context of a virtual power plant or microgrid, with and without grid connection or distributed dispatchable generators. The model considers aggregate storage availability from vehicles based on transport patterns taking into account the necessary vehicle redistribution. We investigate the case of a grid-connected VPP with rooftop solar and the case of a isolated microgrid with solar, wind, and dispatchable generation. We conduct a comprehensive sensitivity analysis to study the effect of several parameters on the results for both cases.

Analyzing the dynamic ride-sharing potential for shared autonomous vehicle fleets using cellphone data from Orlando, Florida

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.

Impact of ridesharing on operational efficiency of shared autonomous electric vehicle fleet

• Proposes a Tabu Search based optimization framework to simulate shared autonomous electric vehicles. • Case study shows that allowing multiple occupants improves service rate and system savings. • The average wait time per trip increases exponentially as ridesharing percentage increases. • Compared to traditional ride-hailing service, the fleet size and the number of charging stations significantly reduce. • Private vehicle ownership may reduce as vehicle capacity and range increase in the context of ridesharing operations.

Modeling shared autonomous electric vehicles: Potential for transport and power grid integration

One-way car-sharing systems are becoming increasingly popular, and the introduction of autonomous vehicles could make these systems even more widespread. Shared Autonomous Electric Vehicles could also allow for more controllable charging compared to private electric vehicles, allowing large scale demand response and providing essential ancillary services to the electric grid. In this work, we develop a simulation methodology for evaluating a Shared Autonomous Electric Vehicle system interacting with passengers and charging at designated charging stations using a heuristic-based charging strategy. The influence of fleet size is studied in terms of transport service quality and break-even prices for the system. We test the potential of the system to supply operating reserve by formulating an optimization problem for the optimal deployment of vehicles during a grid operator request. The results of the simulations for the case study of Tokyo show that a fleet of Shared Autonomous Electric Vehicles would only need to be about 10%–14% of a fleet of private cars providing a comparable level of transport service, with low break-even prices. Moreover, we show that the system can provide operating reserve under several operational conditions even at peak transport demand without significant disruption to transport service.