CRII: CPS: Emerging Markets and Myopic Decision-Making in Multi-Modal Transportation Systems: Modeling and Validation

该项目旨在创建高保真模型，并通过实际数据验证混合模式出行决策和新兴移动市场。越来越多的旅行者根据用户自定义偏好优化（混合模式）路线的应用程序做出决策。局部优化的解决方案往往会导致效率低下，而旅行者的风险敏感性（由内生和外生不确定性引起）会加剧效率低下。传统的理性、效用最大化模型往往不能捕捉到这些影响，特别是在短期决策中，很少有时间进行思考，参考点在选择中起主要作用。该项目旨在(a)学习考虑风险敏感性的旅行者决策模型，(b)开发捕捉旅行者对移动模式估值的市场结构模型（例如，拼车平台）。本研究通过利用前景理论捕捉风险敏感性和有限理性捕捉信息不对称和短视来扩展理性的旅行决策模型。这些概念将被整合到一个反向强化学习框架中，我们为此寻求在线算法来支持控制/激励设计。此外，我们将开发技术方法来建模移动市场结构，包括捕捉风险敏感性的新的排队博弈论模型。拟议的工作将受益于市政和行业合作伙伴提供的多式联运数据，以便在一个完善的试验台进行测试和验证。该项目将强调市政当局可以调整其管理战略的领域，以补充共享经济，以公平的方式解决服务差距。通过与西雅图运输部和行业合作伙伴迅速和IDAX的合作，有可能将结果转化为实践。研究生和本科生将大量参与该项目，并将有充分的机会通过我们的市政合作伙伴与社区互动，并接触到未来智能城市的工作。

This project aims to create high-fidelity models, validated with real-world data, of mixed-mode travel decisions and emerging mobility markets. A growing subset of travelers make decisions informed by apps that optimize (mixed-mode) routes based on user-defined preferences. Locally optimized solutions tend to cause inefficiencies that are exacerbated by risk-sensitivity (arising from endogenous and exogenous uncertainties) in travelers. Traditional rational, utility maximization models tend not to capture these effects, particularly in short-horizon decisions that leave little time for cogitation and points of reference play a primary role in choice. The project aims to (a) learn models of traveler decision-making that account for risk-sensitivity and (b) develop models of market structures (e.g., ride-sharing platforms) that capture traveler valuations of mobility modes. The proposed research extends rational models of travel decisions by leveraging prospect theory for capturing risk-sensitivity and bounded rationality for capturing information asymmetries and myopia. These concepts will be integrated into an inverse reinforcement learning framework for which we seek online algorithms in support of control/incentive design. Moreover, we will develop technical approaches to modeling mobility market structures that include new queuing game-theoretic models that capture risk-sensitivity. The proposed work will benefit from multi-modal transit data available via municipal and industry partners for testing and validation within a well-developed testbed.This project will highlight areas where municipalities can adjust their management strategies to supplement the sharing economy in addressing the service gap in an equitable way. Through collaborations with the Seattle Department of Transportation and industry partners Swiftly and IDAX, there is potential to translate results to practice. Graduate and undergraduate students will be heavily involved in the project and will have ample opportunity to engage with the community through our municipal partners as well as be exposed to the jobs of tomorrow's smart cities.