Rethink Ride-Hail: From Understanding Limits to Reaching Full Potential

Uber和Lyft等交通网络公司（TNC）重塑了传统出租车服务。与传统出租车乘客在路边叫车不同，跨国公司允许乘客通过智能手机或电子叫车进行数字化叫车。然而，目前还不清楚这两种服务（街道招呼与电子招呼）的性能如何相互比较，它们能够在多大程度上匹配乘客与司机，以及乘客必须等待多长时间车辆到达。该项目将制定一个总体框架，描述两种服务类型的乘客和司机之间的匹配，并在此基础上比较他们各自的表现。还将制定政策和战略，以改善这些服务的业绩。 该项目将编制教育材料，并让本科生和研究生参与。本项目的研究成果将通过学术期刊和国际会议广泛传播。本项目研究了一个新颖的和通用的乘客和司机之间的匹配过程模型，并使用经验数据进行验证。 该模型描述了在一个提供电子招呼和街道招呼服务的混合市场中供求之间的空间平衡，并试图解释这两种服务之间的根本差异。 模型中的参数将根据经验数据进行校准，从而使一般模型框架与在真实的世界中实施的各种匹配策略之间具有现实性。该项目还将分析服务运营商和监管机构的实际决策问题。运营商或许能够设计出一种混合服务，将e-hail更高的生产率与street-hail更高的规模回报结合起来。 项目结果还将为目前正在考虑各种政策的监管机构提供见解，如入门上限和最低工资。这些决策问题，通常制定为数学规划约束的平衡条件或马尔可夫决策过程，是大的维度和规模，高度非线性，非凸的。 因此，该项目也有助于分析和解决这些具有挑战性的优化问题。 最后，该项目开发了一个综合评估框架，包括基于模型，基于模拟和基于应用的案例研究。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Transportation Network Companies (TNC) such as Uber and Lyft has reshaped traditional taxi services. Unlike traditional taxis where riders hail vehicles from street curbs, TNCs allow riders hail vehicles digitally through smart phones, or e-hail. It is however unclear how the performance of these two types of services (street hail vs. e-hail) compare with each other in terms of how well they are able to match riders with drivers and how long riders must wait for vehicles to arrive. This project will develop a general framework describing the matching between riders and drivers for both types of services, based on which, their respective performance will be compared. Policies and strategies will also be developed in order to improve the performance of these services. The project will develop educational materials and involve undergraduate and graduate students. Findings from this project will be widely disseminated through academic journals and international meetings.The project researches a novel and general model of the matching process between riders and drivers and validates it with empirical data. This model describes spatial equilibrium between supply and demand in a mixed market that offers both e-hail and street-hail services and seeks to explain fundamental differences between the two types of services. Parameters in the model will be calibrated from empirical data, allowing realism between the general model framework and the various matching strategies implemented in the real world. The project will also analyze practical decision-making problems for service operators and regulators. Operators may be able to design a hybrid service by combining e-hail's greater productivity with street-hail's higher returns to scale. The project results will also provide insights to regulators who are currently contemplating various policies such as entry cap and minimum wage. These decision problems, typically formulated as either Mathematical Programs constrained by Equilibrium Conditions or Markov Decision Processes, are large in both dimension and scale, highly nonlinear, and nonconvex. Accordingly, the project also contributes to the analysis and solution of these challenging optimization problems. Finally, the project develops a comprehensive evaluation framework that consists of model-based, simulation-based and application-based case studies. These case studies can be used by other researchers and educators working in this field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.