Shared Autonomous Vehicles Research Articles

Dispatching prebooking-based shared autonomous vehicles with hybrid ownership: an approach of vehicle trip planning with uncertain travel times

Dispatching prebooking-based shared autonomous vehicles with hybrid ownership: an approach of vehicle trip planning with uncertain travel times

Use of Remote Focus Groups to Improve Inclusive Design Practice

This study examines the use of remote focus groups to obtain wheeled mobility device user’s ideas regarding Shared Autonomous Vehicles (SAVs). Given the global increase in the aging population and disabled people, inclusive design is essential for accessible transportation. Using remote focus groups, this research collected statements from nine wheeled mobility device users about their design preferences, needs, and challenges with SAVs. Participants attended four virtual sessions featured with presentations and videos of SAVs. Discussions were video recorded in Zoom, with transcripts reviewed for accuracy. Data analysis included cleaning, coding, and assessing interrater reliability using the ICC. The findings not reported here highlighted the diverse needs and preferences of participants, emphasizing the importance of collecting information from this group early in the design process. This study shows well-planned remote focus groups can be an effective tool to gather human factors system design information from user populations that face transportation barriers.

Fostering User Acceptance in Shared Autonomous Vehicles: A Framework for HMI Design

Fostering User Acceptance in Shared Autonomous Vehicles: A Framework for HMI Design

Optimal management of park-and-ride points and shared parking spaces in the context of autonomous driving

This paper studies how to set the location of park-and-ride and the pricing of shared parking spaces to achieve the desired goals in the context of autonomous vehicles (AV). Assuming that a linear city does not allow single-occupancy private cars to enter the city center, travelers can only choose to park and ride or take a shared autonomous vehicle to reach the city center. Under the condition of autonomous driving, since the car can park autonomously, there are three ways to park: park the car home, park the car in a free parking space near the transfer point, and park the car in a shared parking space provided by residents near the transfer point. Starting from the two goals of profit maximization and social cost minimization, this paper explores the optimal parameter settings under the two goals respectively. The results show that the optimal park-and-ride point and the optimal shared parking space pricing under the two goals are both obtained when the supply of shared parking spaces is equal to the demand. This study can provide theoretical reference and decision-making basis for the optimal allocation of resources in the era of autonomous driving.

Exploring the influence of a virtual reality experience on user acceptance of shared autonomous vehicles: A quasi-experimental study in Brussels

This paper investigates the intention to use shared autonomous vehicles in Brussels before and after an experience in virtual reality, in which a scenario of a city with shared autonomous vehicles was visualized taking into account transformations that are not possible to implement in real life such as urban form changes. 51 participants took part in a quasi-experiment and their acceptance of autonomous vehicles was measured before and after, using surveys based on the unified theory of acceptance and use of technology (UTAUT). Using partial least squares structural equation modeling, we model user acceptance pre- and post-VR experience and compare the change in influencing factors. Results show that, under the conditions we defined, the intention to use autonomous shuttles was influenced by the virtual reality experience, as there was an increase in the behavioral intention and a change in the significant predictors. Overall, we found evidence that the virtual reality experience did influence the acceptance of autonomous vehicles, and can then be an effective test bed to visualize future impacts of autonomous vehicles that are impossible to project in real life. Our study constitutes then a first step into exploring potential usages of virtual reality to project long-term visions of future developments of cities with shared autonomous vehicles.

Perceptions of Service Enhancements in Shared Autonomous Vehicles: A Demographic Perspective

As the pressure on urban mobility rises, shared autonomous vehicles (SAVs) offer enhanced transportation efficiency and safety. This study investigates the valuations of additional services in SAVs, examining demographics’ impact on preferences for different service features. We use data from 1723 Norwegian respondents to an online survey. We find that women put increased value on a safety host. Younger respondents value fast travel time. More tech-savvy individuals showing a higher valuation for services enhancing personal utility and comfort. Intention to use strongly predicts valuation of utilities, but not social factors.

Exploring attitudes and perceptions regarding a self-driving demonstration

Purpose Public opinion regarding autonomous vehicles (AV) heavily influences how quickly the technology will be implemented and adopted in the future. However, there is a dearth of empirical evaluations in the literature about riders' perceptions toward service characteristics of shared autonomous vehicles (SAVs) and their attitudes toward developing AVs. Therefore, the aim of this study is to identify attitudes, views and concerns regarding a self-driving demonstration called RAPID (Rideshare, Automation and Payment Integration Demonstration) incorporated with an already-existing on-demand ridesharing service in Arlington, Texas. Design/methodology/approach This study developed a ridership survey to collect data from those who had experience using the service at least once during the service deployment. As the RAPID service operations were restricted to the areas near the University of Texas at Arlington (UTA) campus, sample population of this study is highly skewed with all participants being affiliated with UTA. Findings Findings indicated that survey respondents positively perceived the service features, including comfort, boarding the vehicles, ride safety, booking and scheduling, vehicle speed, climate control and service cost. To complement the survey results, the authors conducted interviews and a focus group study and used conventional content analysis to gain more in-depth insights about RAPID service operations from the perspectives of users and non-users in the post-implementation period. The results indicated that geographic accessibility, service availability and trip cost were the primary concerns of the focus group participants. Originality/value This study offers critical insights into individual attitudes and perceptions toward shared AVs (SAVs) that will assist local, state and federal transit authorities and planners in formulating policies and transportation strategies to target SAV ridership when the service is more widespread.

Are shared automated vehicles good for public- or private-transport-oriented cities (or neither)?

Simulation studies suggest that Shared Automated Vehicles (SAVs) could reduce the total vehicle kilometres travelled (VKT) thanks to efficiently pooling multiple users in one vehicle. However, mode choice studies indicate that SAVs would attract mostly public transport users, leading to an increase in VKT. This paper is among the first to combine these operational and behavioural expectations and the first to do so analytically. In our theoretical set-up, travellers choose between car, public transport, and SAVs, depending on their individual valuation of private travel and other attributes of each mode. We find that the introduction of SAVs lead to a VKT change in public-transport-oriented cities ranging from a small decrease to a large increase, where the latter is true for plausible parameter settings and hence is a cautionary point for SAV-introduction policies. Conversely, SAVs would attract only few travellers in private-transport-oriented cities and therefore would not significantly impact VKT.

Convergence of Emerging Transportation Trends: A Comprehensive Review of Shared Autonomous Vehicles

Convergence of Emerging Transportation Trends: A Comprehensive Review of Shared Autonomous Vehicles

Human or robot? Exploring different avatar appearances to increase perceived security in shared automated vehicles

Shared Automated Vehicles (SAVs) promise to make automated mobility accessible to a wide range of people while reducing air pollution and improving traffic flow. In the future, these vehicles will operate with no human driver on board, which poses several challenges that might differ depending on the cultural context and make one-fits-all solutions demanding. A promising substitute for the driver could be Digital Companions (DCs), i.e. conversational agents presented on a screen inside the vehicles. We conducted interviews with Colombian participants and workshops with German and Korean participants and derived two design concepts of DCs as an alternative for the human driver on SAVs: a human-like and a robot-like. We compared these two concepts to a baseline without companion using a scenario-based online questionnaire with participants from Colombia (N = 57), Germany (N = 50), and Korea (N = 29) measuring anxiety, security, trust, risk, control, threat, and user experience. In comparison with the baseline, both DCs are statistically significantly perceived as more positively. While we found a preference for the human-like DC among all participants, this preference is higher among Colombians while Koreans show the highest openness towards the robot-like DC.

Distributional equity impacts of automated vehicles: A disaggregated approach

Better understanding of the equity impacts of automated vehicles (AV) is needed to design equitable deployment plans for AV technology. The goal of this study was to develop and demonstrate a modeling framework to support distributional equity assessments of AV systems prior to their wide adoption. This modeling framework takes a disaggregated approach that integrates agent-based simulation, diverse transportation outcomes, and equity analyses. The framework uses individual-level data to capture detailed disparities in transportation outcomes, applies a state-of-the-art multi-agent traffic simulation model (MATSim) to simulate privately-owned and shared AVs simultaneously, and considers distributional equity from multiple perspectives. To test and demonstrate the framework, a case study was conducted for the Tampa Bay region. Five scenarios were considered with different AV market shares and integration strategies based on scenario planning by the U.S. Federal Highway Administration. Results reveal that high AV penetration rates were required for substantial reductions in inequality. The introduction of AVs led to a more even distribution of accessibility, but slightly more uneven distribution of traditional mobility and affordability. Impacts of disparities in outcomes for disadvantaged groups were mixed. These results suggest that AVs will likely perpetuate existing inequity in the transportation system as long as its fundamental structure remains the same as today. Results highlight the importance of planning and design strategies that directly address the distributional impacts to ensure that AV technology is deployed equitably.

Modelling Passengers’ Travel Behaviour for Shared Autonomous Vehicle and Bus Considering Heterogeneity

The popularisation of autonomous vehicles will give rise to a new business model called shared autonomous vehicle (SAV). SAVs may attract a large number of passengers and lead to a decline in the share of buses, which can be interpreted by exploring passengers’ travel behaviour when confronting the SAV and bus modes. Thus, this paper addresses the SAV and bus passengers’ travel behaviour, aiming to examine the factors influencing travel behaviour and revealing the characteristics of SAV passengers. We classified passengers using latent class cluster analysis and modelled passengers’ travel behaviour based on confirmatory factor analysis and mixed logit model. The findings indicate a variation in travel preferences among different classes of travellers. Short-distance travellers pay less attention to travel time. Non-short-distance PT travellers are most likely to be affected by service attributes (waiting time, travel time and travel costs). Non-short-distance private car travellers are more likely to become early SAV adopters. Passengers travelling for short distances may be more likely to choose SAV, which reveals the potential of SAVs to become a first and last mile connection for public transport. Passengers lack trust in SAVs, which will affect their promotion.

Shared low-speed autonomous vehicles for short-distance trips: agent-based modeling with mode choice analysis

ABSTRACT Previous research has envisioned behavior changes with the availability of shared autonomous vehicle (SAV) services. It is expected that SAVs will compete with conventional human-driven vehicles and lead to a shift in mode share. If designed as single-occupancy, low-speed, and lightweight, SAVs can also be considered a micro-mobility mode targeting short-distance trips in geofencing areas, which has been lacking in discussion in the state-of-the-art literature. To address this gap, this study envisions changes in mode choice with the implementation of a single-occupancy Shared Low-speed Light-weight Autonomous Mobility (SLLAM) system deployed, serving trips at speeds below 25 mph. Agent-based simulations are conducted to evaluate the mode share of the SLLAM system. The findings demonstrate that the SLLAM system has the potential to serve 36.98% of the trip demand in the study area. Increased operating speeds enhance the SLLAM mode share, making it a more attractive option for existing private vehicle trips.

Shared autonomous vehicle with pooled service, a modal shift approach

Shared autonomous vehicle with pooled service, a modal shift approach

Investigating Mode Choice Behavior for the Introduction of Underground Shared Autonomous Vehicle Service: Case Study in the city of Seoul

Investigating Mode Choice Behavior for the Introduction of Underground Shared Autonomous Vehicle Service: Case Study in the city of Seoul

Shared autonomous vehicle operational decisions with vehicle movement and user travel behaviour

Shared Autonomous Vehicle (SAV) has many impacts on the transport development, such as saving parking space. However, SAV meets a huge challenge in terms of vehicle supply and user demand imbalance. The traditional mathematical optimization method cannot be well used due to the computational burden. Hence, this paper proposes a Reinforcement Learning (RL) based SAV relocation approach. First, two types of RL agents, car-based and zone-based agents, are developed as agents for vehicles and stations, respectively. Then, the RL scheme is trained by using historical demand data to facilitate real-time carsharing relocation. Finally, to compare the proposed two types of RL methods, three scenarios are used: small-scale, middle-scale, and large-scale networks. Solutions indicate that the enhanced zone-based method achieves an additional 146% profit compared to traditional threshold-based relocation strategies. The user travel behaviour impacts are provided by analyzing parking demand and travel movements among residential, industrial and commercial zones.

Charging infrastructure assessment for shared autonomous electric vehicles in 374 small and medium-sized urban areas: An agent-based simulation approach

This research examines the use of Shared Autonomous Electric Vehicles (SAEVs) in 374 U.S. small and medium-sized urban areas, focusing on fleet and infrastructure needs through agent-based simulations. It assesses metrics such as fleet size, trips per vehicle, and charging station requirements, considering two charger types: Level 2 and Level 3. The findings show significant spatial differences in SAEV operations and infrastructure across these cities. Statistical analysis links these variations to regional road networks and travel patterns. The study finds Level 3 chargers more efficient, requiring fewer stations and enabling more trips per vehicle compared to Level 2 chargers. Furthermore, Level 3 chargers exhibit a greater number of trips per SAEV and a higher ratio of vehicles to charging stations. These findings highlight the significance of considering charging infrastructure characteristics to optimize SAEV fleet performance and promote sustainable transportation systems in urban areas. This study significantly contributes by identifying the spatial variation and correlates of the SAEVs' operational and charging infrastructural performance. Policymakers, urban planners, and transportation service providers can leverage these insights to design and implement effective charging infrastructure for SAEV fleets, thereby advancing the transition to cleaner and more efficient mobility solutions.

Optimal management of coupled shared autonomous electric vehicles and power grids: Potential of renewable energy integration

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems.

Smart Insertion Strategies for Sustainable Operation of Shared Autonomous Vehicles

As shared autonomous vehicles (SAV) emerge as an economical and feasible mode of transportation in modern cities, effective optimization models are essential to simulate their service. Traditional optimization approaches, based on first-come-first-served principles, often result in sub-optimal outcomes and, more notably, can impact public transport (PT) operations by creating unnecessary competition. This study introduces four insertion strategies within the MATSim model of the Melbourne Metropolitan Area, addressing these challenges. Two strategies optimize SAV operations by considering overall network costs, and the other two make insertion decisions based on the available PT service in the network. The findings show that strategic insertions of the requests can significantly enhance SAV service quality by improving the vehicle load and decreasing vehicle and empty kilometers traveled per ride. The analysis indicates that these strategies are particularly effective for smaller fleet sizes, leading to an increased number of served rides and a more equitable distribution of wait times across the network, reflected in an improved Gini Index. The findings suggest that prioritization-based insertions significantly enhance service quality by prioritizing users with limited access to PT, ensuring that those with fewer PT options are served first, and encouraging a more integrated and sustainable urban transportation system.

Competitive pricing for ride-sourcing platforms with MARL

With the advancement of autonomous driving, the competition between shared autonomous vehicles (SAVs) and the existing human-driven vehicles (HVs) ride-sourcing platforms has emerged as a prominent research area within the fields of transportation and management. Solving the pricing problem, considering the competition between HV and SAV platforms, is critical for the platform’s operation. The competitive pricing problem is hard to solve since it is a partially observable Markov decision process (POMDP), that is, only the strategies of other platforms (such as price and wage) are observable, while their states are unobservable. It is also a multivariable sequential problem pricing since spatial-temporal pricing varies with region and time. To tackle this complex problem, this paper implements the multi-agent reinforcement learning (MARL) approach of multi-agent deep deterministic policy gradient (MADDPG) to an mesoscopic simulation system. The simulation replicates the intricate dynamics within a competitive environment where SAV and HV platforms coexist. MADDPG effectively resolves the competitive pricing issue using data derived from simulation. The findings indicate that the SAV platform secures greater profits with a smaller fleet, as it eliminates the need to compensate drivers. Meanwhile, for the same reason, even with fewer vehicles, the SAV platform can set lower prices to attract more passengers and thereby generate higher profits. We further evaluate the efficacy of dynamic pricing versus spatial-temporal pricing and discover that spatial-temporal pricing yields greater profits.