网约车需求估计的数据融合方法：带抽样和内生性修正的离散选择模型
A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
摘要：
Uber和Didi等公司提供的打车服务在过去十年中增长迅速。了解对这些服务的需求对于规划和管理现代运输系统至关重要。由于数据有限，现有的研究开发了总体水平上的网约车需求估计的统计模型。这些模型缺乏微观经济学理论基础，忽视了网约车与其他出行方式的竞争，并且无法无缝集成到现有的基于个体水平（分解）活动的模型中，以评估网约车服务的系统水平影响。在本文中，我们提出并应用了一种方法来估计乘坐外包需求在一个不聚集的水平，使用离散选择模型和多个数据源。我们首先构建了一个基于出行方式选择的样本，在美国芝加哥，通过丰富的家庭出行调查与公开可用的乘车来源和出租车出行记录。然后，我们制定了一个多变量的极端值为基础的离散选择与抽样和endo-weighting校正，以解释从多个数据源的估计样本的建设和endo-weighting偏差所产生的供应侧约束和激增价格形成机制在乘车外包系统。我们对构建数据集的分析揭示了各种社会经济，土地利用和建筑环境特征对乘车需求的影响。我们还得到了弹性的乘车外包需求相对于旅行成本和时间。最后，我们说明了如何开发的模型可以用来量化的福利影响，如终止某些类型的服务，并引入搭车外包税的政策和法规。
英文摘要：
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.
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