Capturing positive network attributes during the estimation of recursive logit models: A prism-based approach
递归 Logit 模型中正向网络属性的估计：棱柱约束方法AI 解读 ↓
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递归 Logit 模型中正向网络属性的估计：棱柱约束方法
- 研究问题
- 递归 Logit 参数更新可能使价值函数不可计算时，如何稳定估计模型，并容纳街道绿化等具有正向效用的路径属性？
- 方法与思路
- 作者分析棱柱约束递归 Logit 模型，在状态扩展网络上定义受限路径集合，并通过数值实验与行人路线应用考察估计表现。
- 材料陈述的贡献
- 摘要称棱柱约束在所考察实验中缓解估计可行性问题，应用捕捉到绿化的正向效应，且拟合与预测优于原递归 Logit。
- 阅读关注点
- 应核对棱柱路径集如何定义、价值函数可行性保证的范围，以及限制绕行和循环路径是否影响参数解释与样本外评价。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验。摘要中的数值表现不能代替普遍保证，绿化系数也不能直接解释为因果效应。
原文摘要
Although the recursive logit (RL) model has been recently popular and has led to many applications and extensions, an important numerical issue with respect to the computation of value functions remains unsolved. This issue is particularly significant for model estimation, during which the parameters are updated every iteration and may violate the feasibility condition of the value function. To solve this numerical issue of the value function in the model estimation, this study performs an extensive analysis of a prism-constrained RL (Prism-RL) model proposed by Oyama and Hato (2019), which has a path set constrained by the prism defined based upon a state-extended network representation. The numerical experiments have shown two important properties of the Prism-RL model for parameter estimation. First, the prism-based approach enables estimation regardless of the initial and true parameter values, even in cases where the original RL model cannot be estimated due to the numerical problem. We also successfully captured a positive effect of the presence of street green on pedestrian route choice in a real application. Second, the Prism-RL model achieved better fit and prediction performance than the RL model, by implicitly restricting paths with large detour or many loops. Defining the prism-based path set in a data-oriented manner, we demonstrated the possibility of the Prism-RL model describing more realistic route choice behavior. The capture of positive network attributes while retaining the diversity of path alternatives is important in many applications such as pedestrian route choice and sequential destination choice behavior, and thus the prism-based approach significantly extends the practical applicability of the RL model.