Understanding and Optimizing Ride-Sourcing Drivers' Learning Dynamics

该项目将为出租车司机提供行为洞察和算法工具，以实现有效的司机组织和最终的市场效率，并提高社会福利。它将与驾驶员合作，获取位置和运营数据，并就战术和运营决策提供优化和协调的指导，以最大限度地提高他们的福利。运输机构和当地社区可以与司机合作，深入了解市场，优化政策，以实现社会目标。网约车司机的独立性是以社会孤立和焦虑为代价的。该项目将绘制一条技术启发的途径，以更好地连接和组织分散的劳动力，并提高其凝聚力，活力和社会贡献。乘车外包司机更有可能来自低收入和其他弱势群体，项目原型除了作为研究计划的试验台外，还作为改善他们福祉的推广工具。PI将把研究成果纳入她的研究生/高年级本科课程的运输系统分析和经济学，传统上运输市场的供应商方面没有像消费者方面一样深入对待。 主要研究目标有三个：1）基于心理学的学习理论，利用动态离散选择模型理解驾驶员的学习和选择行为; 2）开发基于模型和无模型的算法，以优化何时开始和结束工作、在哪里寻找乘客以及是否接受乘车请求的决策，并可扩展到驾驶员参与的水平; 3）综合来自前两个目标的结果，生成行为上知情的驾驶员指导。该项目在三个主要方面具有创新性。首先，它提供了一种替代方法，通过直接与司机而不是文献和实践中常见的平台合作来增强乘车外包市场。以司机为中心的方法可能更有社会效率，因为它避免了平台的过度供应倾向，并且由于其协作性质而更公平。其次，它进步的理解，司机的学习和选择动态下的不确定性，在不同的时间尺度：路由和订单接受分钟到分钟的水平，和调度在小时到小时的水平。这一综合方法将填补网约车司机留存率低背后的原因和动态方面的知识空白。第三，该奖项有助于为网约车运营开发高性能的优化算法，重点是可扩展性，以达到驾驶员参与和数据可用性的水平。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。

This project will generate behavioral insights and algorithmic tools for ride-sourcing drivers to enable effective driver organization and eventual market efficiency and enhanced societal welfare. It will work with drivers to obtain location and operational data and generate optimized and coordinated guidance on tactical and operational decisions to maximize their welfare. Transportation agencies and local communities could partner with drivers to gain insights into the market and optimize policies to meet societal goals. The independence of ride-sourcing drivers comes at the price of social isolation and anxiety. This project will chart a technology-inspired pathway to better connection and organization of the diffusive workforce, and increase its cohesion, vitality, and social contribution. Ride-sourcing drivers are more likely from the lower income and other vulnerable parts of the population, and the project prototype serves as an outreach tool to improve their well-being, in addition to being a test bed for the research program. The PI will incorporate the research results in her graduate/upper-class undergraduate courses on transportation systems analysis and economics, where traditionally the supplier side of the transportation market is not treated in the same depth as the consumer side. There are three major research objectives: 1) Understand drivers' learning and choice behaviors using dynamic discrete choice models grounded on psychologically sound learning theories; 2) Develop model-based and model-free algorithms to optimize decisions on when to start and end working, where to search for passengers and whether to accept a ride request, scalable to the level of driver participation; 3) Generate behaviorally informed driver guidance synthesizing results from previous two objectives. The project is innovative in three major aspects. First, it provides an alternative approach to enhancing the ride-sourcing market by directly working with drivers instead of platforms as commonly done in the literature and practice. The driver-centered approach is potentially more socially efficient due to the avoidance of a platform's tendency for over-supply and fairer due to its collaborative nature. Secondly, it advances understandings of drivers' learning and choice making dynamics under uncertainty at various temporal scales: routing and order acceptance at the minute-to-minute level, and scheduling at the hour-to-hour level. This integrated approach will fill the knowledge gap of reasons and dynamics behind the low retention rate of ride-sourcing drivers. Thirdly, it contributes to the development of high-performance optimization algorithms for ride-sourcing operations with an emphasis on the scalability to the level of driver participation and data availability.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.