CAREER: Algorithmic Foundations for Demand-Responsive Transit Systems - Creating More Equitable and Sustainable Cities through Better Transit

职业：需求响应型交通系统的算法基础 - 通过更好的交通创建更加公平和可持续的城市

• 批准号: 2144127
• 负责人: Samitha Samaranayake
• 金额: $ 59.81万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144127&HistoricalAwards=false
• 关键词: CAREER; Algorithmic; Foundations; Demand; Responsive

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Faculty Early Career Development (CAREER) project will address fundamental research questions related to designing and operating transit-centric transportation systems, with the aim of enabling an efficient, sustainable and equitable transportation system for all. While the past decade has seen enormous advances in transportation technologies such as ridesharing and self-driving cars powered by, for example, artificial intelligence, mobile phone adoption and new business models, it remains unclear whether these innovations alone can lead us toward a future that is sustainable and equitable. This project argues that fundamental progress in this regard is best achieved via hybrid transit systems, services that seamlessly integrate the efficiencies of mass transit with agile, demand-responsive modes related to ridesharing. The technical focus will be on algorithms for designing and operating such systems, an area with key research gaps. The research will be conducted through the lens of Algorithm Engineering, which focuses on developing theoretical insights from successful data-driven and heuristic approaches, and heuristics from theory. Collaborations with stakeholders, such as transit agencies, technology providers, community groups, and policy makers will enable an understanding of practical and societal needs, model calibration using real-data, and validation through simulation and deployments. The project aims to broaden the renewed national focus on transit infrastructure to innovations in service modes, and will involve community outreach and education efforts targeting high school students, college students and public agencies. The research will incorporate technical ideas from Civil Engineering, Operations Research and Computer Science to develop new methodologies and train students with cross-disciplinary expertise. The project aims to make fundamental intellectual contributions for enabling the realization of hybrid transit systems, spanning four research thrusts: 1) Quantifying the value of integrating agile, demand-responsive systems with mass transit via formalizing a new metric, the Value of Dynamism, and designing efficient techniques based on two-stage stochastic optimization for computing it; 2) Developing new models and algorithms for hybrid transit network design via approximation algorithms and data-driven heuristics; 3) Integrating pricing and social welfare analysis with hybrid network design, via compact mixed integer linear programming (MILP) formulations, and using these methods to understand the impact of service design and policy decisions (e.g., subsidies) on societal goals (e.g., equity); and 4) Developing fast, scalable, passenger-matching and routing algorithms for the demand-responsive component of hybrid transit systems—by formulating novel relaxations of the classical integer linear programming approach that can efficiently integrate information about future demand (i.e., be non-myopic).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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。这一学院早期职业发展(CAREAR)项目将解决与设计和运营以交通为中心的交通系统相关的基本研究问题，目的是为所有人提供高效、可持续和公平的交通系统。尽管过去十年见证了交通技术的巨大进步，如拼车和自动驾驶汽车，例如人工智能、手机采用和新的商业模式，但尚不清楚仅凭这些创新能否带领我们走向可持续和公平的未来。该项目认为，这方面的根本进展最好是通过混合交通系统实现，这种服务将公共交通的效率与与拼车相关的灵活、需求响应模式无缝结合。技术重点将放在设计和运行这类系统的算法上，这是一个有关键研究空白的领域。这项研究将通过算法工程的视角进行，它专注于从成功的数据驱动和启发式方法中开发理论见解，并从理论中开发启发式方法。与运输机构、技术提供商、社区团体和政策制定者等利益攸关方的合作将使人们能够了解实际和社会需求，使用真实数据进行模型校准，并通过模拟和部署进行验证。该项目旨在将国家对交通基础设施的重新关注扩大到服务模式的创新，并将涉及针对高中生、大学生和公共机构的社区外联和教育努力。这项研究将融合土木工程、运筹学和计算机科学的技术理念，以开发新的方法，并培养具有跨学科专业知识的学生。该项目旨在为混合公交系统的实现做出基础性的智力贡献，跨越四个研究方向：1)通过形式化一个新的指标--动态值，并设计基于两阶段随机优化的有效技术来量化将敏捷、需求响应系统与公共交通相结合的价值；2)通过近似算法和数据驱动启发式算法开发混合公交线网设计的新模型和新算法；3)通过紧凑混合整数线性规划(MILP)公式，将定价和社会福利分析与混合网络设计相结合，并使用这些方法来了解服务设计和政策决策(例如，补贴)对社会目标(例如，公平)的影响；以及4)为混合交通系统的需求响应组件开发快速、可扩展的乘客匹配和路线选择算法-通过制定经典整数线性规划方法的新松弛，可以有效地整合有关未来需求的信息(即，非短视)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

On the Value of Dynamism in Transit Networks

论交通网络动态的价值

• DOI: 10.1287/trsc.2022.1193
• 发表时间: 2023
• 期刊: Transportation Science
• 影响因子: 4.6
• 作者: Martínez Mori, J. Carlos;Speranza, M. Grazia;Samaranayake, Samitha
• 通讯作者: Samaranayake, Samitha