SBIR Phase I: Simulating Demand and Competition for Emerging Transportation Modes

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响是通过开发一个决策支持工具来评估这些新的交通方式将对社会产生的广泛的经济，环境和公平影响，从而推进电动飞机需求建模的最新技术。该项目的动机是电池技术、自动化和分布式推进技术的最新进展，这些技术正在通过开发新型电动垂直起降（eVTOL）飞机来改变航空运输。长途电动飞机的开发也在进行中，可以重振和支持支线机场的新服务。 政府机构、飞机制造商和其他利益相关者可以使用该决策支持工具来指导政策和数百万美元的公共和私人基础设施投资，以实现经济、环境和公平影响之间的平衡。 SBIR第一阶段项目将推动新交通模式需求建模的前沿，同时解决与地面模式的竞争，有竞争力的定价和刺激需求。 该项目的重点是研究如何模拟空中和地面运输之间的竞争，距离在50至350英里之间，空中旅行在过去在经济上不可行。 该项目侧重于三个未经证实的高影响力创新。首先是设计一个可扩展的航空旅行需求模拟平台，包括新飞机预计运行的距离。第二个是开发算法，使用基于位置的数据来识别当前乘客在机场对之间的移动，并预测需求刺激。第三个是利用机器学习来校准航空运输网络，以适应具有数百个输入的竞争市场。该奖项反映了NSF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。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the state-of-the-art for demand modeling for electric aircraft by developing a decision support tool that assesses the broad economic, environmental, and equity impacts that these new modes of transportation will have on society. The project is motivated by recent advancements in battery technologies, autonomy, and distributed propulsion that are transforming air transportation through the development of a new class of electric vertical takeoff and landing (eVTOL) aircraft. The development of electric aircraft for longer distances is also in progress and could revitalize and support new service out of regional airports. The decision support tool can be used by government agencies, aircraft manufacturers, and other stakeholders to guide policy and multi-million dollar public and private infrastructure investments in a way that achieves a balance between economic, environmental, and equity impacts. This SBIR Phase I project will push the frontiers in modeling demand for new transportation modes, simultaneously addressing competition with ground modes, competitive pricing, and stimulated demand. The project focuses on examining how to model competition between air and ground transportation for distances between 50 and 350 miles where air travel has not been economically feasible in the past. This project focuses on three unproven, high-impact innovations. The first is the design of a scalable, demand simulation platform for air travel that includes distances in which the new aircraft are expected to operate. The second is the development of algorithms that use location-based data to identify the movement of current passengers between airport pairs and to predict demand stimulation. The third is the use of machine learning to calibrate air transportation networks for competitive marketplaces that have hundreds of inputs.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.