Nonparametric mixed logit model with market-level parameters estimated from market share data
利用市场份额数据估计市场层级参数的非参数混合 Logit 模型AI 解读 ↓
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利用市场份额数据估计市场层级参数的非参数混合 Logit 模型
- 研究问题
- 仅有市场层面的选择份额时，如何刻画不同市场的偏好异质性，并获得可用于交通需求预测、替代关系分析和供给优化的参数？
- 方法与思路
- 作者将每个市场视为一个主体，通过多主体逆向效用最大化估计市场特定参数，构建非参数混合 Logit，并在模拟与纽约出行数据上比较基准模型。
- 材料陈述的贡献
- 摘要报告应用中的样本外准确率改善、耗时低于 BLP 的十分之一，并展示价格弹性、转移比例及参数与交通供给优化和政策情景分析的衔接。
- 阅读关注点
- 应核对市场特定参数的识别与约束、价格内生性的处理、训练测试划分和计算口径，并检查拥堵收费与票价补贴分析依赖的行为和供给假设。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要中的准确率与耗时属于特定应用比较，未说明完整识别条件，政策情景结果不能直接视为因果证据。
原文摘要
We propose a nonparametric mixed logit model that is estimated using market-level choice share data. The model treats each market as an agent and represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric estimation. A simulation study is conducted to evaluate the performance of our model in terms of estimation time, estimation accuracy, and out-of-sample predictive accuracy. In a real data application, we estimate the travel mode choice of 53.55 million trips made by 19.53 million residents in New York State. These trips are aggregated based on population segments and census block group-level origin-destination (OD) pairs, resulting in 120,740 markets. We benchmark our model against multinomial logit (MNL), nested logit (NL), inverse product differentiation logit (IPDL), and the BLP models. The results show that the proposed model improves the out-of-sample accuracy from 65.30% to 81.78%, with a computation time less than one-tenth of that taken to estimate the BLP model. The price elasticities and diversion ratios retrieved from our model and benchmark models exhibit similar substitution patterns. Moreover, the market-level parameters estimated by our model provide additional insights and facilitate their seamless integration into supply-side optimization models for transportation design. By measuring the compensating variation for the driving mode, we found that a $9 congestion toll would impact roughly 60 % of the total travelers. As an application of supply-demand integration, we showed that a 50% discount of transit fare could bring a maximum ridership increase of 9402 trips per day under a budget of $50,000 per day.