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摘要翻译：
我们建立了一个城市交通的多智能体仿真模型，对普通交通和紧急或危机模式交通进行建模。该仿真首先基于详细的地理信息建立了模型化的道路网络。在这个网络上，模拟创建了两个代理群体：运输者和移动者。运输者体现了道路本身；它们是功利主义的，旨在处理模拟的低水平真实感。移动代理体现了在网络上流通的交通工具。他们有一个或几个目的地，他们试图通过最初他们对网络结构的信念（边缘长度、速度限制、车道数等）到达。然而，当面对一个动态的、易突发的环境（其他车辆、意外封闭的道路或车道、交通堵塞等）时，相当被动的主体会激活更多的认知模块来适应其信念、愿望和意图。它可能会改变它的目的地，改变到达目的地所使用的策略（支持较少使用的道路，跟随其他代理，使用通用标题）等。我们描述了我们的模型当前的验证和下一步计划的改进，包括验证和功能。
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英文标题：
《A multiagent urban traffic simulation》
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作者：
Pierrick Tranouez (LITIS), Eric Daud\'e (IDEES), Patrice Langlois
(IDEES)
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
We built a multiagent simulation of urban traffic to model both ordinary traffic and emergency or crisis mode traffic. This simulation first builds a modeled road network based on detailed geographical information. On this network, the simulation creates two populations of agents: the Transporters and the Mobiles. Transporters embody the roads themselves; they are utilitarian and meant to handle the low level realism of the simulation. Mobile agents embody the vehicles that circulate on the network. They have one or several destinations they try to reach using initially their beliefs of the structure of the network (length of the edges, speed limits, number of lanes etc.). Nonetheless, when confronted to a dynamic, emergent prone environment (other vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather reactive agent will activate more cognitive modules to adapt its beliefs, desires and intentions. It may change its destination(s), change the tactics used to reach the destination (favoring less used roads, following other agents, using general headings), etc. We describe our current validation of our model and the next planned improvements, both in validation and in functionalities.
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PDF链接：
https://arxiv.org/pdf/1201.5472
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