Artificial Intelligence for Truck Fleet Management**

卡车车队管理人工智能**

• 批准号: 533550-2018
• 负责人: Glatard, Tristan
• 金额: $ 1.82万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 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=651998
• 关键词: Artificial; Intelligence; Truck; Fleet; Management

Today's transportation services are moving faster than ever, and companies are challenged to distribute their products more quickly and with increasingly competitive costs while maintaining safety on the road. With the emergence of data collection systems in trucks, tremendous opportunities for data intelligence are created, related to the creation of explanatory and predictive models for various events. The goal of this project is (1) to develop data-driven models to predict road accidents and infractions occurring in the truck fleet, and (2) to identify potential counter-measures to reduce the incidence of detrimental events. To build the predictive models, we pan to rely on Random Forests, a state- of-the-art classification and regression technique that has been successfully applied to many problems including in supply-chain modeling. To interpret the results and identify counter measures, we will use data mining techniques, for instance Association Rules. We expect this project to provide Groupe Robert with predictive models and possible actions to improve the **safety of its employees on the road, which will improve its competitiveness inside Canada.

今天的运输服务比以往任何时候都发展得更快，公司面临的挑战是在保持道路安全的同时，更快地配送产品，成本越来越有竞争力。随着卡车上数据收集系统的出现，为数据智能创造了巨大的机会，这与为各种事件创建解释和预测模型有关。该项目的目标是(1)开发数据驱动模型来预测卡车车队中发生的道路事故和违规行为，以及(2)确定潜在的应对措施，以减少有害事件的发生。为了建立预测模型，我们倾向于依赖随机森林，这是一种最先进的分类和回归技术，已经成功地应用于许多问题，包括供应链建模。为了解释结果并确定对策，我们将使用数据挖掘技术，例如关联规则。我们希望这个项目为Groupe Robert提供预测模型和可能的行动，以提高其员工在道路上的**安全，这将提高其在加拿大的竞争力。