scholarly journals Hierarchical Agent-Based Modeling for Improved Traffic Routing

2019 ◽  
Vol 9 (20) ◽  
pp. 4376 ◽  
Author(s):  
Raghda Alqurashi ◽  
Tom Altman
Keyword(s):  
Decision Making ◽  
Traffic Congestion ◽  
Agent Based Modeling ◽  
Appropriate Technology ◽  
High Density ◽  
Vehicular Traffic ◽  
Modeling Framework ◽  
Agent Based ◽  
Proposed Model ◽  
Alternative Routes

Agent-based model (ABM) simulation is a bottom–up approach that can describe the phenomena generated from actions and interactions within a multiagent system. An ABM is an improvement over model simulations which only describe the global behavior of a system. Therefore, it is an appropriate technology to analyze emergent phenomena in social sciences and complex adaptive systems such as vehicular traffic and pedestrian crowds. In this paper, a hybrid agent-based modeling framework designed to automate decision-making processes during traffic congestion is proposed. The model provides drivers with real-time alternative routes, computed via a decentralized multi-agent model, that tries to achieve a system-optimal traffic distribution within an entire system, thus reducing the total travel time of all the drivers. The presented work explores a decentralized ABM technique on an autonomous microgrid that is represented through cellular automata (CA). The proposed model was applied to high-density traffic congestion events such as car accidents or lane closures, and its effectiveness was analyzed. The experimental results confirm the efficiency of the proposed model in not only accurately simulating the driver behaviors and improving vehicular traffic flows during congestion but also by suggesting changes to traffic dynamics during the simulations, such as avoiding obstacles and high-density areas and then selecting the best alternative routes. The simulation results validate the ability of the proposed model and the included decision-making sub-models to both predict and improve the behaviors and intended actions of the agents.

Agent-Based Modelnig with Boundedly Rational Agents

2007 ◽  
pp. 225-245 ◽  
Author(s):  
E. Ebenhoh
Keyword(s):  
Experimental Data ◽  
Decision Making ◽  
Model Building ◽  
Theoretical Concept ◽  
Agent Based Modeling ◽  
Modeling Framework ◽  
Agent Based ◽  
Rational Agents ◽  
Decision Making Processes ◽  
Participatory Model

This chapter introduces an agent-based modeling framework for reproducing micro behavior in economic experiments. It gives an overview of the theoretical concept which forms the foundation of the framework as well as short descriptions of two exemplary models based on experimental data. The heterogeneous agents are endowed with a number of attributes like cooperativeness and employ more or less complex heuristics during their decision-making processes. The attributes help to distinguish between agents, and the heuristics distinguish between behavioral classes. Through this design, agents can be modeled to behave like real humans and their decision making is observable and traceable, features that are important when agent-based models are to be used in collaborative planning or participatory model-building processes.

scholarly journals Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3860
Author(s):  
Priyanka Shinde ◽  
Ioannis Boukas ◽  
David Radu ◽  
Miguel Manuel de Manuel de Villena ◽  
Mikael Amelin
Keyword(s):  
Renewable Energy ◽  
Electricity Market ◽  
Power Plants ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Agent Based Modeling ◽  
Market Liquidity ◽  
Energy Sources ◽  
Modeling Framework ◽  
Agent Based

In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.

scholarly journals Size matters: Sample size assessments for chronic wasting disease surveillance using an agent-based modeling framework

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100953
Author(s):  
Aniruddha Belsare ◽  
Matthew Gompper ◽  
Barbara Keller ◽  
Jason Sumners ◽  
Lonnie Hansen ◽  
...  
Keyword(s):  
Sample Size ◽  
Disease Surveillance ◽  
Chronic Wasting Disease ◽  
Agent Based Modeling ◽  
Modeling Framework ◽  
Wasting Disease ◽  
Agent Based

scholarly journals Agent-Based Modeling Framework for Electric Vehicle Adoption Transition in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 73
Author(s):  
Dita Novizayanti ◽  
Eko Agus Prasetio ◽  
Manahan Siallagan ◽  
Sigit Puji Santosa
Keyword(s):  
Electric Vehicles ◽  
Data Analytics ◽  
Agent Based Modeling ◽  
Analysis Framework ◽  
Modeling Framework ◽  
Agent Based ◽  
Twitter Data ◽  
Decision Making Processes ◽  
Electric Vehicle Adoption ◽  
Technical Features

Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

scholarly journals GutLogo: Agent-based modeling framework to investigate spatial and temporal dynamics in the gut microbiome

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207072 ◽  
Author(s):  
Charlie Lin ◽  
Joshua Culver ◽  
Bronson Weston ◽  
Evan Underhill ◽  
Jonathan Gorky ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Temporal Dynamics ◽  
Agent Based Modeling ◽  
Modeling Framework ◽  
Agent Based ◽  
Spatial And Temporal Dynamics
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