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

新的信息和传感技术的迅速出现正在推动智能和互联社区的形成(S和CC)。该项目旨在推动智能和互联技术的使用，以增强基于社区的决策的新模式，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，这些解决方案提供更多就业、教育和医疗保健机会。该项目部署了传感技术来收集创建居民移动性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基于社区的决策框架；在该框架中，为利益相关者提供工具，以可视化的方式探索移动解决方案的预测结果。该团队包括双城地区交通管理局(TCATA)，该机构将反复实施源自情景规划流程的移动解决方案，并对解决方案进行量化评估。与密歇根湖学院的合作进一步增强了该项目的广泛影响，让社区大学生参与研究，并为他们提供智能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简的解决方案来应对移动性挑战这一根本问题，该项目将定义一种经济高效的数据收集战略，该战略可以评估现有解决方案的性能，跟踪居民的移动性模式，并获取居民对其移动性的看法。在城市公交车上使用手机应用程序和计算机视觉进行GPS跟踪将被用来生成对当前移动配置的性能进行建模所需的数据。对居民的调查将补充这些数据来源。该项目将把移动数据映射到一个分析框架，该框架可以根据用户需求的变化预测居民对移动服务的需求和这些服务的性能。将建立以活动为基础的模型，特别强调对小社区旅行需求的细粒度估计。将开发预测模型，以预测移动网络配置提供的运输服务质量。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穿梭的组合。该项目将使参与式规划过程中的流动数据和预测性产出可视化，从而使基于社区的决策成为可能。该团队还将为TCATA提供跟踪和迭代塑造公共交通的能力，以提高就业、医疗保健和教育成果的可及性。这一奖项反映了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。

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.