POSE: Phase II: CONNECT: Consortium of Open-source plaNNing models for Next-generation Equitable and efficient Communities and Transportation

该项目由开源生态系统之路（POSE）资助，旨在通过高效和有效的多式联运规划解决交通拥堵、空气质量和可持续性问题，以多样化的交通选择促进健康和公平的社会。该项目将通过公平和可持续社区和交通系统开源多模式规划联盟（CONNECT）将通用建模网络规范（GMNS）扩展到GMNS+来实现这一目标。该项目的新特点包括集成多模式网络建模方法，确保不同社区之间的公平性，采用交叉分辨率策略，提供个性化的移动选项，以及利用时间敏感的网络数据。此外，该项目将为各种交通解决方案建立一个广泛的开源生态系统。其影响包括促进高效、可持续、安全、便捷和包容的交通系统，并促进国家、州和城市各级的知情决策。在美国有许多城市规划组织和地区委员会，该项目的简化流程和开源生态系统将支持不同治理级别的明智决策。研究小组采用了一种技术方法，利用现有的数据格式创建无缝集成的多模式网络，促进机构和软件包之间的离线和实时数据共享。这一战略为动态多式联运分析的执行提供了动力，预示着运输科学中标准化数据的新时代的到来，并通过一种通用的、适应性强的工具，使广泛的多式联运数据集的检查更容易实现，这可能会改变运输领域的数据科学。该团队旨在将开放数据规范与系统优化工具相结合，以优化城市交通基础设施社区的效率和公平性。通过实施可持续性治理，该项目将加强实践者和研究界面临的现实挑战之间的联系，确保产品竞争力和整体可持续性。GMNS+生态系统通过使用创新、高度灵活和可扩展的计算和教育工具促进大型多模态数据集的研究，具有显著影响数据科学的潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project, funded by Pathways to Enable Open-Source Ecosystems (POSE), aims to promote a healthy and equitable society with diverse mobility options by addressing traffic congestion, air quality, and sustainability issues through efficient and effective multimodal transportation planning. The project will achieve this by extending the General Modeling Network Specification (GMNS) to GMNS+ through the Consortium of Open-Source Multimodal PlaNNing for Equitable and Sustainable Communities and Transportation Systems (CONNECT). The project's novel features include integrating a multimodal network modeling approach, ensuring equity among diverse communities, employing cross-resolution strategies, offering personalized mobility options, and utilizing time-sensitive network data. Additionally, the project will establish an extensive open-source ecosystem for a wide range of transportation solutions. Its impacts encompass fostering efficient, sustainable, safe, accessible, and inclusive transportation systems and facilitating informed decision-making at national, state, and city levels. With numerous Metropolitan Planning Organizations and Regional Councils in the United States, the project's streamlined process and open-source ecosystem will support informed decision-making at various governance levels. The research team adopts a technical approach that capitalizes on existing data formats to create seamlessly integrated multimodal networks, fostering offline and real-time data sharing among agencies and software packages. This strategy empowers the execution of dynamic multimodal analysis, heralding a new age of standardized data in transportation science, and making the examination of extensive multimodal datasets more approachable through a versatile and adaptable tool—potentially transforming data science within transportation. The team aims to integrate open data specifications with system optimization tools to optimize efficiency and equity within the urban transportation infrastructure community. By implementing sustainability governance, the project will strengthen the connection between real-world challenges faced by practitioners and the research community, ensuring product competitiveness and overall sustainability. The GMNS+ ecosystem holds the potential to significantly impact data science by facilitating the study of large multimodal datasets using innovative, highly flexible, and scalable computational and educational tools.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.