ABSCEV: An agent-based simulation framework about smart transportation for reducing waiting times in charging electric vehicles

2018 ◽  
Vol 138 ◽  
pp. 119-135 ◽  
Author(s):  
Iván García-Magariño ◽  
Guillermo Palacios-Navarro ◽  
Raquel Lacuesta ◽  
Jaime Lloret
Keyword(s):  
Electric Vehicles ◽  
Waiting Times ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Smart Transportation

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

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.

DRESS: A Distributed RMS Evaluation Simulation Software

2020 ◽  
Vol 16 (3) ◽  
pp. 1-18
Author(s):  
Vincenzo Agate ◽  
Alessandra De Paola ◽  
Giuseppe Lo Re ◽  
Marco Morana
Keyword(s):  
Simulation Software ◽  
Reputation Management ◽  
Management Systems ◽  
Distributed Environments ◽  
Simulation Framework ◽  
Security Attacks ◽  
Agent Based Simulation ◽  
Huge Number ◽  
Agent Based ◽  
Simulation Environments

Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agent reputations are required. Reputation management systems (RMS's) allow agents to perform such estimation in a cooperative way. In particular, distributed RMS's exploit feedbacks provided after each interaction and allow prediction of future behaviors of agents. Such systems, in contrast to centralized RMSs, are sensitive to fake information injected by malicious users; thus, predicting the performance of a distributed RMS is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMS's, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks.

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