A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns

Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the environment, habits, and activity-travel patterns. Agents learn through their experiences with the transport systems, changes in the environments and from their social network. Conceptually, agents are assumed to have an aspiration level associated with choice sets that in combination with evaluation results determine whether the agent will start exploring or persist in habitual behavior; an activation level of each (location) alternative that determines whether or not the alternative is included in the choice set in the next time step, and an expected (utility) function to evaluate each (location) alternative given current beliefs. Each of these elements is dynamic. Based on principles of reinforcement learning, Bayesian learning, and social comparison theories, the framework specifies functions for experience-based learning, extended and integrated with social learning.

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Optimal patent length and breadth in an R&D driven market with evolving consumer preferences: An evolutionary multi-agent based modeling approach

Technological Forecasting and Social Change ◽

10.1016/j.techfore.2017.02.005 ◽

2017 ◽

Vol 118 ◽

pp. 94-106 ◽

Cited By ~ 1

Author(s):

Salih Çevikarslan

Keyword(s):

Consumer Preferences ◽

Agent Based Modeling ◽

Agent Based ◽

Modeling Approach ◽

Multi Agent ◽

Patent Length

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A Study Of Multi-Agent Based Model For Urban Intelligent Transport Systems

International Journal of Advancements in Computing Technology ◽

10.4156/ijact.vol4.issue6.15 ◽

2012 ◽

Vol 4 (6) ◽

pp. 126-134 ◽

Cited By ~ 3

Author(s):

Xu Jin ◽

Luo Jie

Keyword(s):

Transport Systems ◽

Agent Based Model ◽

Intelligent Transport Systems ◽

Agent Based ◽

Intelligent Transport ◽

Multi Agent

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Proposed Traffic Light Control Mechanism Based on Multi-Agent Coordination

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2016.p0803 ◽

2016 ◽

Vol 20 (5) ◽

pp. 803-812

Author(s):

Satoshi Kurihara ◽

◽

Ryo Ogawa ◽

Kosuke Shinoda ◽

Hirohiko Suwa ◽

...

Keyword(s):

Traffic Flow ◽

Urban Areas ◽

Transport Systems ◽

Light Control ◽

Traffic Light ◽

Agent Based ◽

Traffic Lights ◽

Control Framework ◽

Traffic Light Control ◽

Multi Agent

Traffic congestion is a serious problem for people living in urban areas, causing social problems such as time loss, economical loss, and environmental pollution. Therefore, we propose a multi-agent-based traffic light control framework for intelligent transport systems. Achieving consistent traffic flow necessitates the real-time adaptive coordination of traffic lights; however, many conventional approaches are of the centralized control type and do not have this feature. Our multi-agent-based control framework combines both indirect and direct coordination. Reaction to dynamic traffic flow is attained by indirect coordination, whereas green-wave formation, which is a systematic traffic flow control strategy involving several traffic lights, is attained by direct coordination. We present the detailed mechanism of our framework and verify its effectiveness using simulation to carry out a comparative evaluation.

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The Multi-Agent Based Reliability Modeling Approach for Networked Software System

International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011) ◽

10.1115/1.859933.paper160 ◽

2011 ◽

pp. 707-710

Keyword(s):

Software System ◽

Reliability Modeling ◽

Agent Based ◽

Modeling Approach ◽

Networked Software ◽

Multi Agent

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A Study of Multi-agent Based Metropolitan Demand Responsive Transport Systems

Advances in Soft Computing - The Sixth International Symposium on Neural Networks (ISNN 2009) ◽

10.1007/978-3-642-01216-7_76 ◽

2009 ◽

pp. 711-720 ◽

Cited By ~ 1

Author(s):

Jin Xu ◽

Weiming Yin ◽

Zhe Huang

Keyword(s):

Transport Systems ◽

Agent Based ◽

Multi Agent

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Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

Journal of Advanced Transportation ◽

10.1155/2021/6654254 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Sung-Jung Wang ◽

S. K. Jason Chang

Keyword(s):

Reinforcement Learning ◽

Intelligent Agents ◽

Large Scale ◽

Gradient Algorithm ◽

Transport Systems ◽

Efficient Operation ◽

Fleet Size ◽

Agent Based ◽

Policy Gradient ◽

Multi Agent

Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.

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