Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents
Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents
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DOI：
10.1177/0361198105193500116
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发表时间：
2005
影响因子：
1.7
通讯作者：
F. Marchal;K. Nagel
中科院分区：
文献类型：
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作者：
F. Marchal;K. Nagel
交通科学中的基于活动的模型侧重于对人类出行和活动的描述。在本文中，所谓的次要活动的空间决策建模。考虑到家庭和工作地点，人们在哪里进行购物和休闲等活动？使用随机效用模型来模拟这些决策，需要对可能的结果进行全面枚举。对于大型数据集，由于组合的复杂性，它在计算上变得不可行。为了克服这一局限性，提出了一种模型，其中代理人有有限的，准确的信息的一个小的子集的整体空间环境。代理人通过一个社交网络相互连接，通过这个网络他们可以交换信息。与标准随机效用模型的显式模拟相比，这种方法具有以下几个优点：（a）它在合理的计算时间内计算出合理的选择集，（B）它可以很容易地扩展到整合关于旅行行为的进一步经验证据，以及（c）它提供了一个有用的框架来研究任何新可用信息的传播。本文强调了现实世界的例子的方法的计算效率。
Activity-based models in transportation science focus on the description of human trips and activities. Modeling the spatial decision for so-called secondary activities is addressed in this paper. Given both home and work locations, where do individuals perform activities such as shopping and leisure? Simulation of these decisions using random utility models requires a full enumeration of possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome that limitation, a model is proposed in which agents have limited, accurate information about a small subset of the overall spatial environment. Agents are interconnected by a social network through which they can exchange information. This approach has several advantages compared with the explicit simulation of a standard random utility model: (a) it computes plausible choice sets in reasonable computing times, (b) it can be extended easily to integrate further empirical evidence about travel behavior, and (c) it provides a useful framework to study the propagation of any newly available information. This paper emphasizes the computational efficiency of the approach for real-world examples.