A Computational Framework for a Multi-Agent Simulation of Freight Transport Activities
A Computational Framework for a Multi-Agent Simulation of Freight Transport Activities
复制标题
货运活动多智能体模拟的计算框架
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
K. Nagel
中科院分区：
文献类型：
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作者：
S. Schroeder;Michael Zilske;G. Liedtke;K. Nagel
人们普遍认为微观模拟和基于主体的方法可以成功地应用于交通政策分析。然而，物流决策和货运参与者之间的复杂关系使这一任务具有挑战性，这也是货运模型的发展仍然落后于客运模型发展的原因。在本文中，我们提出了一个多代理货运模型，其中物流决策被划分为不同的角色：决定运输频率的托运人，创建运输链的运输服务提供商，以及计划行程和调度车辆的承运人。各种类型的代理商可以在各自的水平上整合运费，实现规模经济。模型的最低层包含单个货运车辆，将其集成到MATSim交通模拟中，以创建货运和客运交通的综合模型。乘客需求的变化、交通系统的干扰或政策措施可以被货运司机发现，并向上传播，从而影响车辆调度和运输链建设层面的决策，并进一步影响托运人层面的决策。作为概念验证，我们设置了一个场景，其中虚构的货运参与者为一组客户提供服务。我们论证了在不同交通条件和政策措施下，货运量是可以模拟的。
It is widely recognized that micro-simulation and agent-based approaches can successfully be applied in transport policy analysis. However, logistic decisions and the complex relationships among freight actors make this a challenging task and a reason why the development of freight models is still behind the development of passenger models. In this paper, we present a multiagent freight transport model in which logistics decisions are separated into different roles: the shippers, which decide about shipment frequency, the transport service providers, which create transport chains, and carriers, which plan tours and schedule vehicles. All agent types can consolidate freight on their respective level and realize economies of scale. The lowest tier of the model, which contains individual freight vehicles, is integrated into the MATSim traffic simulation to create an integrated model for freight and passenger traffic. Changes in passenger demand, disturbances in the traffic system or policy measures can be picked up by freight drivers and propagated upwards to influence decisions on the levels of vehicle scheduling and transport chain building, and further on the level of shippers. As proof of concept, we set up a scenario with fictitious freight actors serving a set of customers. We demonstrate that freight traffic can be simulated under different traffic conditions and policy measures.