CADIS: Aspect-Oriented architecture for collaborative modeling and simulation

Valadares, Arthur; Lopes, Cristina V.; Achar, Rohan; Bowman, Mic

doi:10.1109/wsc.2016.7822162

Cited by 3 publications

(2 citation statements)

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“…STARS is a multi-agent simulation platform on top of a simulation framework called Spacetime (Valadares et al 2016;Lopes et al 2017). Spacetime follows a proof-of-concept paradigm called Predicate Collection Classes (PCCs) (Lopes et al 2017) to increase modularity.…”

Section: System Designmentioning

confidence: 99%

“…It assumes a distributed simulation environment using pass-by-copy to fulfill data requests, and handles the operation of pushing state updates back. In the original design of Spacetime, each application is encapsulated and is independent of each other (Valadares et al 2016;Lopes et al 2017). However, multi-agent simulations typically require interactions between different applications (agents) (Macal and North 2010).…”

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confidence: 99%

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Toward Understanding the Impact of User Participation in Autonomous Ridesharing Systems

Shen

Achar

Lopes

2018

2018 Winter Simulation Conference (WSC)

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Autonomous ridesharing systems (ARS) promise many societal and environmental benefits, including decreased accident rates, reduced energy consumption and pollutant emissions, and diminished land use for parking. To unleash ARS' potential, stakeholders must understand how the degree of passenger participation influences the ridesharing systems' efficiency. To date, however, a careful study that quantifies the impact of user participation on ARS' performance is missing. Here, we present the first simulation analysis to investigate how and to what extent user participation affects the efficiency of ARS. We demonstrate how specific configurations (e.g., fleet size, vehicle capacity, and the maximum waiting time) of a system can be identified to counter the performance loss due to users' uncoordinated behavior on ridesharing participation. Our results indicate that stakeholders of ARS should base decisions regarding system configurations on insights from data-driven simulations and make tradeoffs between system efficiency and price of anarchy for desired outcomes.

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Section: System Designmentioning

confidence: 99%

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confidence: 99%