Automatic Driver Background Checker

自动驾驶员背景检查器

• 批准号: 531180-2018
• 负责人: England, Andrea
• 金额: $ 1.82万
• 依托单位: Sheridan Institute of Technology and Advanced Learning
• 依托单位国家: 加拿大
• 项目类别: Applied Research and Development Grants - Level 1
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650220
• 关键词: Automatic; Driver; Background; Checker

The Uber business model facilitates providing various services online. However, conducting checking driver background checks is a time consuming and expensive process in the existing model. The proposed research aims to automate this process to improve reliability while reducing costs for industry partner Danby Ltd. (ShipperBee), which seeks to use the Uber model for solving package distribution problems that most delivery service companies face. The proposed model will provide an affordable distribution service for packages while addressing issues such as return packages, which represent a significant percentage of the shipment market. ****Through this proposed Engage grant, several algorithms to check the candidate drivers' background automatically will be developed and tested. By collecting and analyzing driver candidates' information from reliable online sources, the algorithms will improve the efficiency and robustness of selecting appropriate drivers based on editable criteria. This research project will: 1) investigate the usage of the available data analytics platforms, user generated data, and public information on the internet to check the background of potential drivers; and 2) provide guidelines on how to select the near optimal combination of data analytics techniques and data sources for checking drivers' backgrounds. ****The applied research to be undertaken during this project will add a core component to the Uber-based shipment system that will help in hiring better drivers which, in turn, will improve client trust in the shipment service and hence attract new customers. In addition, it will engage students in addressing real-world problems, which will increase their potential for contributing to future innovation.

Uber的商业模式有助于在线提供各种服务。然而，在现有模式中，进行检查驾驶员背景调查是一个耗时且昂贵的过程。这项拟议中的研究旨在自动化这一过程，以提高可靠性，同时降低行业合作伙伴Danby Ltd.（ShipperBee）的成本，该公司寻求使用Uber模式来解决大多数交付服务公司面临的包裹配送问题。拟议的模式将提供负担得起的包裹配送服务，同时解决退货包裹等问题，这在货运市场中占很大比例。* 通过这项拟议的参与赠款，将开发和测试几种自动检查候选司机背景的算法。通过从可靠的在线来源收集和分析驾驶员候选人的信息，该算法将提高基于可编辑标准选择合适驾驶员的效率和鲁棒性。该研究项目将：1）调查可用的数据分析平台、用户生成的数据和互联网上的公共信息的使用情况，以检查潜在驾驶员的背景;以及2）提供关于如何选择数据分析技术和数据源的接近最佳组合以检查驾驶员背景的指导方针。* 本项目期间进行的应用研究将为基于Uber的运输系统增加一个核心组件，这将有助于雇用更好的司机，从而提高客户对运输服务的信任，从而吸引新客户。此外，它将让学生参与解决现实世界的问题，这将增加他们为未来创新做出贡献的潜力。