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，卡车是加拿大主要的货物运输方式。近年来货运活动的增加突出了了解卡车货运活动的重要性。此前研究货运量的努力一直依赖于调查数据。然而，这些调查通常对分析师来说很昂贵，对受访者来说也很耗时。过去几年来，一种新兴的调查替代方法是分析全球定位系统(GPS)数据。20多年来，运营商一直使用GPS应答器来实时识别卡车的位置，并确定货物到达的时间。虽然GPS数据为研究人员提供了极好的机会，但它们在交通建模中的使用具有挑战性，原因有两个：1)这些GPS记录最初不是作为交通模型和分析的输入，2)获得的GPS数据的数量、速度和多样性转化为数十亿条记录(即大数据)，必须进行分析。因此，需要新的方法和技术从被动GPS记录中挖掘大数据，并将这些数据与其他来源融合，以便了解和模拟卡车移动和空间供应链过程。