SBIR Phase I: Applying Machine Learning to Mitigate Disruptions in Novel Relay Trucking Model

SBIR 第一阶段：应用机器学习来减轻新型中继货运模型中的干扰

• 批准号: 2213149
• 负责人: Aayush Thakur
• 金额: $ 25.6万
• 依托单位: CONNECT DYNAMICS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213149&HistoricalAwards=false
• 关键词: SBIR; Phase; Applying; Machine; Learning

The broader impacts of this SBIR Phase I project include a more sustainable and profitable trucking industry, enhanced conditions for the trucking workforce, improved public road safety, reduced emissions, and a path to future advancements using alternative energy and autonomous technologies. This project centers on a patented technology that pools shipments and matches tractors and drivers with trailers and cargo in a relay fashion that ensures equipment and cargo keep moving while drivers return home daily. Research has established the theoretical viability of applying relay models in long-haul trucking to solve equipment and human capacity issues. Using relay trucking could potentially double asset utilization, cut delivery times in half, and decrease the high cost of truck driver turnover. To date, no company has created a technology to implement scalable relays in American trucking. Customer discovery and research suggests this is due to the complexity of predicting and mitigating real-world trucking disruption events (e.g. traffic accidents, equipment breakdown etc.). The project aims to assess the feasibility of developing a machine learning (ML) based predictive analytics tool to make our relay technology resilient to disruptions in the driver scheduling and truck route optimization problem. A successful project would extend a relay trucking model beyond largely theoretical studies to build the first real-world application in the U.S. It aligns with NSF’s mission by transforming the trucking industry while creating better jobs for new and existing truckers and reducing carbon emissions. We estimate that resolving the inefficiencies resulting in the loss of $110 billion per year.This project aims to prove the feasibility of using machine learning (ML) techniques to build a predictive tool that integrates with our relay scheduling algorithms to effectively estimate the likelihood of occurrence of disruption events and provide actionable intelligence for deployment of mitigation strategies. A successful outcome will create resilient relay algorithms necessary to commercialize the first scalable relay trucking operations in the U.S. While the use of ML has been explored in predicting weather and traffic events in the recent past, no off-the-shelf ML tools are known to exist that can be utilized for this patented relay technology. This project leverages real-world proprietary operational data from large trucking companies, combined with public data sources and artificially populated datasets representing drivers and equipment informed by work with local partners at relay nodes.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.

SBIR第一阶段项目的更广泛影响包括更可持续和盈利的卡车运输行业，改善卡车运输劳动力的条件，改善公共道路安全，减少排放，以及使用替代能源和自主技术实现未来发展的途径。该项目的核心是一项专利技术，该技术将货物集中在一起，并以接力方式将拖拉机和司机与拖车和货物相匹配，以确保设备和货物在司机每天回家时保持移动。研究已经建立了在长途卡车运输中应用中继模型来解决设备和人力问题的理论可行性。使用中继卡车运输可能会使资产利用率提高一倍，将交货时间缩短一半，并降低卡车司机周转的高成本。到目前为止，还没有一家公司创造出一种技术来在美国卡车运输中实施可扩展的继电器。客户发现和研究表明，这是由于预测和缓解现实世界的卡车运输中断事件（例如交通事故，设备故障等）的复杂性。该项目旨在评估开发基于机器学习（ML）的预测分析工具的可行性，以使我们的中继技术能够适应驾驶员调度和卡车路线优化问题的中断。一个成功的项目将扩展中继卡车运输模型，超越主要是理论研究，建立在美国的第一个现实世界的应用程序，它与NSF的使命一致，通过改变卡车运输行业，同时为新的和现有的卡车司机创造更好的就业机会，减少碳排放。我们估计，解决这些效率低下的问题每年将造成1100亿美元的损失。该项目旨在证明使用机器学习（ML）技术构建预测工具的可行性，该工具与我们的中继调度算法相集成，以有效估计中断事件发生的可能性，并为部署缓解策略提供可操作的情报。一个成功的结果将创建必要的弹性中继算法，以商业化第一个可扩展的中继卡车运输业务在美国虽然ML的使用已经探索了预测天气和交通事件在最近的过去，没有现成的ML工具是已知的存在，可用于此专利中继技术。该项目利用了来自大型卡车运输公司的真实专有运营数据，结合公共数据源和人工填充的数据集，这些数据集代表了通过与当地合作伙伴在中继节点的合作而获得的驾驶员和设备信息。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。