Review for "nosoi: a stochastic agent‐based transmission chain simulation framework in R"

2020 ◽  
Vol 11 (8) ◽  
pp. 1002-1007 ◽  
Author(s):  
Sebastian Lequime ◽  
Paul Bastide ◽  
Simon Dellicour ◽  
Philippe Lemey ◽  
Guy Baele

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.


2018 ◽  
Vol 46 (6) ◽  
pp. 1079-1096
Author(s):  
Marcello Marini ◽  
Anna P Gawlikowska ◽  
Andrea Rossi ◽  
Ndaona Chokani ◽  
Hubert Klumpner ◽  
...  

Over the next 35 years, the population of Switzerland is expected to grow by 25%. One possible way to accommodate this larger population is to transform smaller cities in Switzerland through the direct intervention of urban planners. In this work, we integrate agent-based simulation models of people flow, mobility and urban infrastructure with models of the electricity and gas systems to examine the increase of the density of existing residential zones and the creation of new workplaces and commercial activities in these urban areas. This novel simulation framework is used to assess, for the year 2050, two different scenarios of urbanization in a region with small urban areas. It is shown that a densification scenario, with a preference for multi-dwelling buildings, consumes 93% less land than a sprawl scenario, with a preference for single-family houses. The former scenario also accommodates 27% more people than the latter scenario, as there is a higher penetration of battery electric vehicles – and therefore reduced air pollution from the transportation sector – and also a larger shift of commuters to the use of public transport. However, in the former scenario, the commuting time is 20% longer. The outcome of this work demonstrates how this novel simulation framework can be used to support the formulation of policies that can direct the transformation of urban areas.


2013 ◽  
Vol 791-793 ◽  
pp. 1476-1479
Author(s):  
Shou Yu Zhang ◽  
Shi Zhen Guo

Research of wartime equipment support simulation faces complex and great challenges. It is very difficult to describe, design and finish the complex giant equipment support simulation system with the traditional simulation and model methods. Proposing a new framework structure based on ACP (artificial systems and computational experiments and parallel execution) approach to solve the complexity giant simulation of RESS (real world equipment support system). Including agent-based model analysis, computational experiments and decision-making problems and etc and discuss an ESASS (equipment support artificial simulation system) platform framework. The work can provide an actionable guidance to equipment support practice simulation research.


2018 ◽  
Vol 138 ◽  
pp. 119-135 ◽  
Author(s):  
Iván García-Magariño ◽  
Guillermo Palacios-Navarro ◽  
Raquel Lacuesta ◽  
Jaime Lloret

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