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首页> 外文期刊>Journal of Transport and Land Use >An Agent-Based Model of Origin Destination Estimation (ABODE)
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An Agent-Based Model of Origin Destination Estimation (ABODE)

机译：基于代理的出发地目的地估计模型（ABODE）

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This paper proposes and tests an agent-based model of worker and job matching. The model takes residential locations of workers and the locations of employers as exogenous and deals specifically with the interactions between firms and workers in creating a job-worker match and the commute outcomes. It is meant to illustrate that by explicitly modeling the search and hiring process, origins and destinations (ODs) can be linked at a disaggregate level. The model is tested on a toy-city as well as using data from the Twin Cities area. The toy-city model illustrates that the model predicts reasonable commute outcomes, with agents selecting the closest work place when wage and skill differentiation is absent in the labor market. The introduction of wage dispersion and skill differentiation in the model increases the the average home to work distances considerably. Using data from Twin Cities area of Minneapolis-St. Paul, aggregate commute and wage outcomes from the model are shown to capture the trends in the observed data. Overall, the results suggest that the behavior rules as implemented lead to reasonable patterns. Future directions are also discussed.
机译：本文提出并测试了基于Agent的工人和工作匹配模型。该模型将工人的住所位置和雇主的位置作为外生的，并专门处理公司与工人之间的互动，以创建工作-工人匹配和通勤结果。它旨在说明，通过对搜索和雇用过程进行显式建模，可以在分解级别上链接起点和目的地（OD）。该模型已在玩具城市以及双子城市区域的数据中进行了测试。玩具城模型说明，该模型可预测合理的通勤结果，当劳动力市场上缺乏工资和技能差异时，代理商会选择最接近的工作地点。在模型中引入工资分散和技能差异会大大增加平均家庭到工作的距离。使用明尼阿波利斯-圣保罗双城地区的数据。 Paul展示了该模型的通勤和工资结果汇总，以捕获观察数据中的趋势。总体而言，结果表明所实施的行为规则导致合理的模式。还讨论了未来的方向。

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