SCC: Data-Informed Scenario Planning for Mobility Decision Making in Resource Constrained Communities

SCC：基于数据的场景规划，用于资源受限社区的出行决策

• 批准号: 1831347
• 负责人: Robert Goodspeed
• 金额: $ 139.99万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831347&HistoricalAwards=false
• 关键词: SCC; Data; Informed; Scenario; Planning

The rapid emergence of new information and sensing technologies is empowering the formation of smart and connected communities (S&CC). This project aims to advance the use of smart and connected technologies to empower new modes of community-based decision making to identify and implement transformative solutions to community challenges. The project focuses on resource-constrained communities. The team will offer the community of Benton Harbor, Michigan, tools needed to explore new mobility solutions that provide greater access to employment, education, and healthcare. The project deploys sensing technologies to collect data needed to create analytical models of resident mobility preferences and mobility service performance. A community-based decision making framework will be created using scenario planning methods; in this framework, stakeholders are provided tools to explore mobility solutions with predicted outcomes visualized. Included in the team is the Twin Cities Area Transportation Authority (TCATA), which will iteratively implement mobility solutions originating from the scenario planning process with solutions quantitatively assessed. A partnership with Lake Michigan College further enhances the project's broader impacts by engaging community college students in the research and offering them experiences in the smart city field of study.To explore the fundamental question of how resourced-constrained communities can utilize smart and connected technologies to implement novel but lean solutions to mobility challenges, the project will define a cost-effective data collection strategy that can assess the performance of existing solutions, track the mobility patterns of residents, and acquire resident perceptions of their mobility. GPS tracking using cell phones apps and computer vision on city buses will be used to generate the data needed to model the performance of current mobility configurations. Surveys of residents will augment these data sources. The project will map mobility data to an analytical framework that can predict both resident demand for mobility services and the performance of these services given changes in user demand. Activity-based models will be created with special emphasis on fine-grain estimation of travel demand in small communities. Predictive models will be developed to predict the quality of transit services provided by configurations of the mobility network. A key advancement will be the creation of scalable computational methods that optimize the mix of fixed route service with on-demand shuttling. This project will enable community-based decision making by visualizing mobility data and predictive outputs during a participatory planning process. The team will also provide TCATA with the ability to track and iteratively shape public transportation in order to enhance access to employment, healthcare, and education outcomes.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.

新的信息和传感技术的快速出现正在推动智能互联社区（S CC）的形成。该项目旨在促进智能和互联技术的使用，以增强基于社区的决策新模式，以确定和实施应对社区挑战的变革性解决方案。该项目的重点是资源有限的社区。该团队将为密歇根州本顿港的社区提供探索新的移动解决方案所需的工具，以提供更多的就业，教育和医疗保健机会。该项目部署传感技术来收集创建居民移动偏好和移动服务性能分析模型所需的数据。将使用情景规划方法创建一个基于社区的决策框架;在这个框架中，利益相关者将获得工具来探索移动解决方案，并将预测结果可视化。该团队包括双城地区交通管理局（TCATA），该机构将迭代实施源自场景规划流程的移动解决方案，并对解决方案进行定量评估。与密歇根湖学院的合作进一步增强了该项目的广泛影响，让社区大学的学生参与研究，并为他们提供智慧城市研究领域的经验。为了探索资源有限的社区如何利用智能和互联技术来实施新颖但精益的解决方案以应对移动挑战的根本问题，该项目将确定一个具有成本效益的数据收集战略，以评估现有解决方案的绩效，跟踪居民的流动模式，并了解居民对其流动性的看法。在城市公交车上使用手机应用程序和计算机视觉的GPS跟踪将用于生成模拟当前移动配置性能所需的数据。对居民的调查将增加这些数据来源。该项目将把移动数据映射到一个分析框架，该框架可以预测居民对移动服务的需求，以及在用户需求发生变化的情况下这些服务的表现。将建立基于活动的模型，特别强调对小社区旅行需求的精细估计。将开发预测模型，以预测移动网络配置提供的交通服务质量。一个关键的进步将是创建可扩展的计算方法，以优化固定路线服务与按需班车的组合。该项目将通过在参与性规划过程中可视化流动数据和预测产出，实现基于社区的决策。该团队还将为TCATA提供跟踪和迭代塑造公共交通的能力，以提高获得就业、医疗保健和教育成果的机会。该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持优点和更广泛的影响审查标准。

项目成果

Proactive shuttle dispatching in large-scale dynamic dial-a-ride systems

大型动态叫车系统中的主动班车调度

• DOI: 10.1016/j.trb.2021.06.002
• 发表时间: 2021
• 期刊: Transportation Research Part B: Methodological
• 作者: Tafreshian, Amirmahdi;Abdolmaleki, Mojtaba;Masoud, Neda;Wang, Huizhu
• 通讯作者: Wang, Huizhu

A Computer Vision Framework for Human User Sensing in Public Open Spaces

• DOI: 10.1145/3360773.3360880
• 发表时间: 2019-11
• 期刊: Proceedings of the 1st ACM International Workshop on Device-Free Human Sensing
• 作者: Peng Sun;R. Hou;J. Lynch

Improving transit in small cities through collaborative and data-driven scenario planning

通过协作和数据驱动的场景规划改善小城市的交通

• DOI: 10.1016/j.cstp.2023.100957
• 发表时间: 2023
• 期刊: Case Studies on Transport Policy
• 影响因子: 2.5
• 作者: Goodspeed, Robert;Admassu, Kidus;Bahrami, Vahid;Bills, Tierra;Egelhaaf, John;Gallagher, Kim;Lynch, Jerome;Masoud, Neda;Shurn, Todd;Sun, Peng
• 通讯作者: Sun, Peng