A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
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网约车需求估计的数据融合方法：带抽样和内生性校正的离散选择模型
- 研究问题
- 如何融合出行调查与平台行程记录，在个体层面估计网约车及其他交通方式之间的选择，并处理抽样和价格内生性问题？
- 方法与思路
- 作者融合芝加哥家庭出行调查、网约车及出租车记录，建立基于多元极值分布的离散选择模型，加入抽样和内生性校正。
- 材料陈述的贡献
- 摘要称模型揭示社会经济和建成环境特征与网约车需求的关系，推导成本与时间弹性，并展示服务终止和征税政策的福利分析。
- 阅读关注点
- 应核对多源样本的抽样概率和权重、内生性校正的识别依据、交通方式替代结构，以及需求弹性和福利反事实的计算条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明内生性校正的具体识别条件及估计结果，政策福利结论仍需核对模型假设。
原文摘要
Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes.