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

Kamel, Joseph; Vosooghi, Reza; Puchinger, Jakob; Ksontini, Feirouz; Sirin, Göknur

doi:10.1016/j.trpro.2018.12.173

Cited by 47 publications

(23 citation statements)

References 9 publications

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“…Second, the population (about 500,000 inhabitants) and the metropolitan area network sizes allow us to perform the simulation with an acceptable downscaling rate (10%), which results in quite accurate outputs compared to the full-scale model (Bischoff and Maciejewski, 2016). Actually, in some studies relying on agent-based simulation and utility scoring, the population of case study areas is highly downscaled (1%) due to the high computational time (Hörl et al, 2019;Kamel et al, 2019). This extensive downscaling may potentially affect the service performance evaluations considering the spatiotemporal interaction of supply and demand in large study areas.…”

Section: Simulated Scenariosmentioning

confidence: 99%

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

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108

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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: Simulated Scenariosmentioning

confidence: 99%

Shared autonomous vehicle simulation and service design

Vosooghi

Puchinger

Janković

et al. 2019

Transportation Research Part C: Emerging Technologies

Self Cite

108

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“…Our findings also emphasize the impact of the user's preferences and neighbor's influence on personal decisions. Several prior studies have examined the relationship between user preferences and decision-making based on multi-agent models [50,51]. They also seek to take in the advantage of this granularity in order to explore the impact of user preferences on the modal split of shared autonomous vehicles in the area of transport.…”

Section: Discussionmentioning

confidence: 99%

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Zhou

Lin

et al. 2020

Sustainability

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Mobile application innovation diffusion can be used to enhance the reputation and competitiveness of Internet enterprises. However, few works have explored the process of mobile application innovation diffusion from the individual perspective. Therefore, this paper employs multi-agent methods to simulate the innovation diffusion of mobile applications from the perspective of social networks. Specifically, we combine psychology, sociology, game theory and network effect theory to model user behaviors of adoption and rejection decisions for mobile applications. The multi-agent model was built in Anylogic 8 to simulate the communication and interaction between individual users. Then, this paper discusses the evolution of decision-making of social network user groups with different network structures and network effects. We also investigate the impact of different firms' promotion on innovation diffusion. Our findings suggest firms could make better strategies and achieve better diffusion effects from mobile applications. IntroductionNowadays, market competition in mobile applications is increasingly fierce. The success of the mobile application in market promotion not only helps Internet enterprises to seize the market and create business value, but also allows firms establish a good reputation and gain a competitive advantage [1][2][3].The innovation diffusion of mobile applications always occurs in user groups. Since users can communicate and interact with others, most of the diffusion of mobile applications is based on each user in a social network [4,5]. Numerous studies indicate that the development of mobile internet and technology social networks, significantly reducing the cost of communication between users, makes communication and interaction very convenient [6,7]. As various mobile applications allow users to share happiness and dissatisfaction with others on the Internet, users' decisions on mobile applications are also influenced by social relations and social network structure [8,9]. In addition, research evidence also supports the fact that there are network effects in the diffusion of mobile applications; that is, the utility of mobile applications to users will increase with an increase in the number of applications [10]. Specifically, network effects can be divided into global and local network effects. Global network effect means that, when more users adopt innovative technology in the global environment, the value of mobile applications to users will be higher. While the local network effect refers to the effect of related users on the utility value, which emphasizing the interaction between users. The decision-making of users will largely depend on the decision-making of their surroundings. As the number of users adopting mobile applications increases, their neighbors also would like to adopt the same applications. Then, the value and utility of the mobile applications to the users will also increase, which ultimately Sustainability 2020, 12, 2884 2 of 17 affects user decision-making. In additio...

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“…Based on real life experiences, such a database could then serve to generate a synthetic population of travellers for a given multi-agent simulation for assessing new mobility solutions in the light of the most frequent problems they meet. This is indeed a challenge in the transport domain to develop richer simulation models integrating preference attributes to the mode choice mechanism of agents (Kamel et al, 2019). First results show that taking into account user preferences in the case of shared mobility simulations substantially change the outputs of the future scenarios (Kamel et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…This is indeed a challenge in the transport domain to develop richer simulation models integrating preference attributes to the mode choice mechanism of agents (Kamel et al, 2019). First results show that taking into account user preferences in the case of shared mobility simulations substantially change the outputs of the future scenarios (Kamel et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Stimulating usage problem generation: An urban mobility case study

et al. 2019

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Designers improve urban mobility solutions by investigating archetypal usage problems in existing mobility systems. User-centred design methods help accomplish this task, but lack effectiveness when not supported by appropriate tools. Here we posit that the use of a traveller-centred stimulus improves the effectiveness of travel problem generation. To test this hypothesis, an experiment is conducted with two control groups as a baseline for non-stimulated problem generation and two experimental groups that are provided with a traveller-centred stimulus. The two sets of groups are composed of one group of urban mobility experts and one group of non-experts. Results show that stimulated groups generate novel ideas with a greater variety covering most of the traveller experience dimensions than non-stimulated groups.

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

Cited by 47 publications

References 9 publications

Shared autonomous vehicle simulation and service design

Shared autonomous vehicle simulation and service design

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Stimulating usage problem generation: An urban mobility case study

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