用深度神经网络预测工作地点选择，比传统模型更准。
Workplace Location Choice Model based on Deep Neural Network
- 用深度神经网络建模工作地点选择行为
- 长距离选择时DNN表现优于传统模型
- 短距离选择时传统模型更贴合实际数据
离散选择模型（DCMs）长期用于分析工作地点选择，但难以准确反映个体决策过程。本文提出基于深度神经网络（DNN）的方法，旨在更好理解复杂决策模式，并在性能上优于传统离散选择模型。研究发现，两种模型均能有效捕捉工作机会对选址的影响，但在某些方面DNN表现更优。尽管如此，当评估个体属性对工作距离的影响时，DCM与数据更一致：在短距离场景中，DCM表现更佳；而在长距离场景中，DNN表现与数据和DCM相当。结果表明，应根据具体应用需求选择合适的模型。
原文摘要 · Abstract (English)
Discrete choice models (DCMs) have long been used to analyze workplace location decisions, but they face challenges in accurately mirroring individual decision-making processes. This paper presents a deep neural network (DNN) method for modeling workplace location choices, which aims to better understand complex decision patterns and provides better results than traditional discrete choice models (DCMs). The study demonstrates that DNNs show significant potential as a robust alternative to DCMs in this domain. While both models effectively replicate the impact of job opportunities on workplace location choices, the DNN outperforms the DCM in certain aspects. However, the DCM better aligns with data when assessing the influence of individual attributes on workplace distance. Notably, DCMs excel at shorter distances, while DNNs perform comparably to both data and DCMs for longer distances. These findings underscore the importance of selecting the appropriate model based on specific application requirements in workplace location choice analysis.
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