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

CRII：CPS：多式联运系统中的新兴市场和短视决策：建模和验证

基本信息

批准号：
1656873
负责人：
Lillian Ratliff
金额：
$ 17.49万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-15 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1656873&HistoricalAwards=false
关键词：
CRII CPS Emerging Markets Myopic

项目摘要

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.

该项目旨在创建混合模式出行决策和新兴移动市场的高保真模型，并通过真实世界的数据进行验证。越来越多的旅行者通过应用程序做出决定，这些应用程序根据用户定义的偏好优化(混合模式)路线。局部优化的解决方案往往导致低效，旅行者的风险敏感性(由内源性和外源性不确定性引起)加剧了这种低效。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，这些决策几乎没有时间进行深思熟虑，而参照点在选择中起着主要作用。该项目旨在(A)学习考虑风险敏感性的旅行者决策模型，以及(B)开发市场结构模型(例如，拼车平台)，以获取旅行者对出行方式的估值。该研究通过利用前景理论来捕捉风险敏感性，利用有限理性来捕捉信息不对称和近视，从而扩展了旅行决策的理性模型。这些概念将被整合到一个反向强化学习框架中，我们为该框架寻找支持控制/激励设计的在线算法。此外，我们将开发技术方法来模拟移动市场结构，其中包括捕捉风险敏感性的新排队博弈论模型。拟议的工作将受益于通过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内进行测试和验证。该项目将突出市政当局可以调整其管理战略以补充共享经济的领域，以公平的方式解决服务差距。通过与西雅图交通部、行业合作伙伴斯威特和IDAX的合作，有可能将成果转化为实践。研究生和本科生将大量参与该项目，并将有足够的机会通过我们的市政合作伙伴与社区接触，以及接触到明天的智能城市的工作。