Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems

Multi-Agent Systems for Traffic and Transportation Engineering - Advances in Mechatronics and Mechanical Engineering ◽

10.4018/978-1-60566-226-8.ch001 ◽

2009 ◽

pp. 1-35

Author(s):

Takeshi Takama

Keyword(s):

Simulation Model ◽

Simulation Analysis ◽

Choice Model ◽

Stated Preference ◽

Road User ◽

Multi Agent System ◽

Minority Game ◽

Agent System ◽

Multi Agent ◽

Adaptation Effects

This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both methodological discussion and an empirical case study are presented in this chapter. The main focus is on the comparison of an analysis of a MAS simulation analysis and an analysis that solely uses discrete choice modelling. This study explains and discusses some important concepts in design empirical MAS in traffic and transportation, including validation Minority Game and adaptation effects. This study develops an empirical MAS simulation model based on real stated-preference data to analyse the effect of a real road-user charge policy and a complimentary park and ride scheme at the Upper Derwent Valley in the Peak District National Park, England. The simulation model integrates a transport mode choice model, Markov queue model, and Minority Game to overcome the disadvantages of a conventional approach. The results of the simulation model show that the conventional analysis overestimates the effect of the transportation and environment policy due to the lack of adaptation affects of agents and congestion. The MAS comprehensively analysed the mode choices, congestion levels, and the user utility of visitors while including the adaptability of agents. The MAS also called as agent-based simulation successfully integrates models from different disciplinary backgrounds, and shows interesting effects of adaptation and congestion at the level of an individual agent.

Download Full-text

Multi-agent system simulation for urban policy design: open space land use change problem

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962316420022 ◽

2016 ◽

Vol 07 (02) ◽

pp. 1642002 ◽

Cited By ~ 2

Author(s):

Roman Buil ◽

Miquel Angel Piera ◽

Egils Ginters

Keyword(s):

Land Use ◽

Simulation Model ◽

Urban Space ◽

Urban Policy ◽

Open Space ◽

New Technologies ◽

Policy Design ◽

Multi Agent System ◽

Agent System ◽

Multi Agent

Multi-agent system (MAS) models have been increasingly applied to the simulation of complex phenomena in different areas, providing successful and credible results. Citizens behavior related to a specific urban activity (i.e., recreation activities in a park, using bicycle for mobility purposes) can be modeled as an agent (actor) with several affinities and preferences which are dependent on aspects that affect the activity. A particular application of a MAS approach is in area of urban policy design, in which policies should be designed considering citizens needs, preferences and behavior. Once an open space in a city is available (i.e., an industry is moved to an industrial area), a land use policy should contribute to identify the new use for the urban space. There are different land use policies that can be applied depending on which services or facilities must be empowered in the city. It is important to identify the correct policy in order to satisfy present citizens needs but considering also the future needs in a social changing context. A socio-technological simulation model has been developed to allow citizens to get a better understanding of the urban problem, its dynamics and explore the sustainability of the different solutions., enhancing citizens to participate in the urban decisions through new technologies (i.e., e-participation). This paper illustrates an open space MAS simulation model for land use design policies in which citizens can check their opinion and get a better understanding of the different choices and its acceptability by the community considering not only present neighborhood profiles, but also future neighborhood configurations. It is the first step before the development of the final software including a user friendly interface to let citizens with different cultural profiles to perform simulations as an essential and neutral tool to reach consensus during the decision-making process in urban policy design.

Download Full-text

Simulation model of pedestrian flow based on multi-agent system and Bayesian Nash equilibrium

AGILE: GIScience Series ◽

10.5194/agile-giss-2-42-2021 ◽

2021 ◽

Vol 2 ◽

pp. 1-7

Author(s):

Yiyu Wang ◽

Jiaqi Ge ◽

Alexis Comber

Keyword(s):

Decision Making ◽

Nash Equilibrium ◽

Simulation Model ◽

Decision Making Process ◽

Multi Agent System ◽

Pedestrian Flow ◽

Time Step ◽

Agent System ◽

Bayesian Nash Equilibrium ◽

Multi Agent

Abstract. Computer-based simulation is a means of exploring complex systems and has become the mainstream method of pedestrian research. In this research, a multi-agent simulation model of pedestrian flow will be established using a multi-agent system (MAS) and Bayesian Nash equilibrium. MAS is used to simulate the crowd movement and the interaction between pedestrians, and Bayesian Nash equilibrium is adopted to analyze the decision-making process of pedestrians. In contrast to previous pedestrian flow simulation modeling methods, this study adopts multi-agent modeling to realize the complete heterogeneity of pedestrians, so as to achieve more accurate simulation and make the research conclusions closer to reality. To be specific, we attempt to determine the cell side length and simulation time step of an initial model parameterized using a dataset of actual pedestrian movements. It allows more than one pedestrian to be in the same cell and stipulates that the utility of pedestrians decreases with the growing number of pedestrians in the cell. The Bayesian Nash equilibrium is applied to analyze the decision-making process of pedestrians and collision avoidance rules and interaction rules of agents are also formulated. A number of areas of further research are discussed.

Download Full-text