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转移的商品。到目前为止，卡车是加拿大搬运货物的主要运输方式。近年来，增加的货运运输活动强调了了解卡车运费的重要性。以前研究货运运动的努力依赖于调查的数据。但是，这些调查通常对于分析师而言是昂贵的，并且对于受访者来说很耗时。在过去几年中，调查的一种新兴替代方法是对全球定位系统（GPS）数据的分析。运营商一直在使用GPS发音器，已有20多年的历史来实时确定其卡车的位置，并确定何时交付货物。尽管GPS数据为研究人员提供了极大的机会，但他们在运输模型中的用法具有挑战性，原因有两个：1）这些GPS记录并非目的是作为运输模型和分析的输入，以及2）所获得的GPS数据的数量，速度和多样性转化为必须分析数十亿个记录（即大数据）。因此，需要从被动GPS记录中挖掘大数据并将这些数据与其他来源融合的新方法和技术的需求，以了解和建模卡车运动和供应链过程。