School of Mathematics and Statistics - Research Publications

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    An Overview of Agent-Based Models for Transport Simulation and Analysis
    Huang, J ; Cui, Y ; Zhang, L ; Tong, W ; Shi, Y ; Liu, Z ; Jin, PJ (Hindawi Limited, 2022-01-01)
    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.
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    Modeling disruption and recovery of traffic in road networks
    Zhang, L ; Garoni, TM ; Was, J ; Sirakoults, GC ; Bandini, S (Springer International Publishing, 2014-01-01)
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    A Study of Aggregated Speed in Road Networks Using Cellular Automata
    ZHANG, L ; Shiri, S ; Garoni, T ; Was, J ; Sirakoulis, GC ; Bandini, S (Springer, 2014)
    Several recent works have focused on studying the relationship between the aggregated flow and density in arterial road networks. Analogous studies involving aggregated speed appear not to have been yet undertaken, however. Here we study and compare such relations for arterial road networks controlled by different types of adaptive traffic signal systems, under various boundary conditions. To study such systems we simulate stochastic cellular automaton models. Our simulation results suggest that network speed could be used as a surrogate for density, due to a strong anticorrelation between these two network observables. Since speed estimates can be more easily obtained than density estimates, e.g. from probe vehicle data, this suggests that Macroscopic Fundamental Diagrams relating aggregated flow with speed might be a practically useful alternative to those relating flow to density.