标题： 一种用于网约车需求估计的数据融合方法：具有抽样和内生性修正的离散选择模型 显示英文标题

标题： A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections

摘要： 基于Uber和滴滴等公司提供的拼车服务在过去十年中迅速增长。 了解这些服务的需求对于规划和管理现代交通系统至关重要。 由于数据可用性的限制，现有研究在汇总层面上开发了统计模型来估计拼车需求。 这些模型缺乏微观经济学理论的基础，忽视了拼车与其他出行方式的竞争，并且无法无缝集成到现有的个体层面（非汇总）活动基础模型中，以评估拼车服务的系统级影响。 在本文中，我们提出并应用了一种方法，使用离散选择模型和多种数据来源，在非汇总层面上估计拼车需求。 我们首先通过将家庭出行调查与公开可用的拼车和出租车出行记录相结合，构建了美国芝加哥市基于行程的出行方式选择样本。 然后，我们制定了一个基于多元极端值的离散选择模型，并考虑了抽样和内生性修正，以应对从多个数据源构建估计样本以及拼车系统中的供给侧约束和动态定价机制所产生的内生性偏差。 我们对构建的数据集进行分析，揭示了各种社会经济、土地利用和建成环境特征对拼车需求的影响。 我们还推导了拼车需求相对于出行成本和时间的弹性。 最后，我们说明了所开发的模型如何用于量化终止某些类型服务和引入拼车税费等拼车政策和法规的福利影响。 显示英文摘要

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.