Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use

Vosooghi, Reza; Kamel, Joseph; Puchinger, Jakob; Leblond, Vincent; Janković, Marija

doi:10.1007/s11116-019-10013-x

Cited by 37 publications

(13 citation statements)

References 22 publications

(23 reference statements)

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“…The activity chains are then allocated to each synthetic individual according to their socio-professional attributes. Based on transport survey analysis conducted for two French case study areas (Paris and Rouen Metropolitan area), we found that the activity chains are significantly correlated to those attributes Vosooghi et al, 2019). The socio-professional category consists of six groups of persons: employed, unemployed, students, people under 14 years, retired, and homemakers.…”

Section: Inputs and Model Setupmentioning

confidence: 98%

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Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

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104

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Today, driverless cars, as a new technology that allows a more accessible, dynamic and intelligent form of Shared Mobility, are expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars is shared autonomous vehicles (SAVs). This service could merge cabs, carsharing, and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on their operational models, which are linked intrinsically to the service configuration and fleet specification. In addition, any change in operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multi-modal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. Also, the impact of ridesharing and rebalancing strategies on service performance is investigated.Research results suggest that the performance of SAV is strongly correlated with the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the pricing scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results also underline that enabling vehicle-rebalancing strategies may have an important effect on both user and service-related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.

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Section: Inputs and Model Setupmentioning

confidence: 98%

“…Thus, they are incorporated into utility scoring. The detailed list of parameters as well as the estimations is available in Vosooghi et al (2019).…”

Section: Inputs and Model Setupmentioning

confidence: 99%

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

Self Cite

104

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“…Activity chains are extracted from a recent transport survey (EMD 2017) and an analysis of population census data (INSEE 2014), and are allocated to each individual of the synthetic population. Once the transportation system of the case study is simulated and calibrated according to the actual modal splits, the SAEV mode and its users' taste variations in terms of mode choice, based on our previous work (Vosooghi et al, 2019a) and a local survey (Al-Maghraoui, 2019), are integrated into the model. In order to allocate the SAEVs more efficiently, a dispatching algorithm developed by Bischoff and Maciejewski (2016) is used.…”

Section: Simulation Frameworkmentioning

confidence: 99%

Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

Vosooghi

Puchinger

Bischoff

et al. 2020

Transportation Research Part D: Transport and Environment

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A B S T R A C TShared autonomous vehicles (SAVs) are the next major evolution in urban mobility. This technology has attracted much interest of car manufacturers aiming at playing a role as transportation network companies (TNCs) in order to gain benefits per kilometer and per ride. The majority of future SAVs will most probably be electric. It is therefore important to understand how limited vehicle range and the configuration of charging infrastructure will affect the performance of shared autonomous electric vehicle (SAEV) services. We aim to explore the impacts of charging station placement, charging type (rapid charging, battery swapping) as well as vehicle range onto service efficiency and customer experience in terms of service availability and response time. We perform an agent-based simulation of SAEVs across the Rouen Normandie metropolitan area in France. The simulation process features impact assessment by considering dynamic demand responsive to the network and traffic.Research results suggest that the performance of SAEVs is strongly correlated to the charging infrastructure. Importantly, faster charging infrastructure and optimized placement of charging locations in order to minimize distances between demand hubs and charging stations result in a higher performance. Further analysis indicates the importance of dispersing charging stations across the service area and how this affects service effectiveness. The results also underline that SAEV battery capacity has to be carefully selected to avoid the overlaps between demand and charging peak times. Finally, the simulation results show that by providing battery swapping infrastructure the performance indicators of SAEV service are significantly improved.

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“…With the development of artificial intelligence and 5G technology in the past 5 years, the market for autonomous vehicles is extremely hot, but there is still a relatively long way to reach real industrialization. 1,2 At present, the industry generally agrees that the technologies that can take the lead to carry out are all autonomous driving technologies in specific scenarios, such as robo taxi, 3 auto valet parking, 4 and so on. What bothers researchers are complex traffic scenarios and the unpredictable behavior of traffic participants.…”

Section: Introductionmentioning

confidence: 99%

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Jiang

Zhou

et al. 2020

International Journal of Advanced Robotic Systems

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Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

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Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use

Cited by 37 publications

References 22 publications

Shared autonomous vehicle simulation and service design

Shared autonomous vehicle simulation and service design

Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

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