Abstract
This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.
Highlights
An appropriate transport model features prominently in characterizing travel demand, related traffic/passenger flows, and their dynamic performance
The agent in the systems does not “play by the script” but performs in realtime. It starts from the local rules of agents to the more complex adaptive and emerging behaviors formed by the interaction of the neighborhood agents, yielding the system dynamics in the environment
In transport systems, such as transportation management systems and transportation control systems, there exist a large number of independent entities that behave in heterogeneous and inherently complex manners. is usually requires the structural framework of the combination of the distributed subsystems representing the agents with local plans or strategies based on knowledge and rule. e overall system performance is achieved by the cooperative and even learning the logic of agents, which are the vital components to perform a global improvement [2,3,4,5,6,7]
Summary
An appropriate transport model features prominently in characterizing travel demand, related traffic/passenger flows, and their dynamic performance. The agent in the systems does not “play by the script” but performs in realtime It starts from the local rules of agents to the more complex adaptive and emerging behaviors formed by the interaction of the neighborhood agents, yielding the system dynamics in the environment. In transport systems, such as transportation management systems and transportation control systems, there exist a large number of independent entities that behave in heterogeneous and inherently complex manners.
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