Mining, Fusion and Modeling of Truck Big Data for the development of Agent-Based Microsimulation Models

卡车大数据的挖掘、融合和建模，用于开发基于代理的微观仿真模型

• 批准号: RGPIN-2017-05843
• 负责人: Maoh, Hanna
• 金额: $ 1.53万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636278
• 关键词: Mining; Fusion; Modeling; Truck; Big

Modern supply chain processes rely heavily on goods transferred between different manufacturers, distributors and retailers. Trucks are by far the dominant mode of transportation for moving goods in Canada. Increased freight transportation activities in recent years underline the importance for understanding freight movements by trucks. Previous efforts to study freight movements have relied on data coming from surveys. However, these surveys are usually expensive for the analyst and time consuming for the respondent. An emerging alternative to surveys over the past few years has been the analysis of Global Positioning Systems (GPS) Data. Carriers have been using GPS transponders for over 2 decades to identify the location of their trucks in real-time and to determine when their goods get delivered. While GPS data provide remarkable opportunities to researchers, their usage in transportation modeling is challenging for two reasons: 1) these GPS records were not originally intended as an input for transportation models and analysis, and 2) the volume, velocity and variety of the obtained GPS data translates into billions of records (i.e. big-data) that have to be analyzed. Therefore, a need exists for novel methods and techniques to mine big-data from passive GPS records and fuse these data with other sources in order to understand and model truck movements and supply chain processes over space.

现代供应链流程在很大程度上依赖于不同制造商、分销商和零售商之间的货物转移。到目前为止，卡车是加拿大运输货物的主要方式。近年来货运活动的增加突出了了解卡车货运的重要性。以前研究货运流动的努力依赖于来自调查的数据。然而，这些调查通常是昂贵的分析师和时间消耗的受访者。过去几年来，一种新兴的调查替代方案是全球定位系统（GPS）数据分析。20多年来，承运人一直在使用GPS应答器来实时识别卡车的位置，并确定货物何时交付。虽然GPS数据为研究人员提供了巨大的机会，但由于两个原因，它们在交通建模中的使用具有挑战性：1）这些GPS记录最初并不打算作为交通模型和分析的输入，以及2）所获得的GPS数据的体积，速度和种类转化为数十亿条必须分析的记录（即大数据）。因此，需要新的方法和技术来从被动GPS记录中挖掘大数据，并将这些数据与其他来源融合，以便了解和模拟空间上的卡车移动和供应链过程。