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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749088
• 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. To this end, the specific objectives of this research program are as follows: 1) Advance the current state of knowledge on freight movement and supply chain processes in Canada using a big-data that depict the movement of 60,000 Canadian trucks over a period of 7 months (September 2015 - March 2016); 2) Develop, implement and test new data mining methods that can be used to extract meaningful patterns and knowledge on truck movement and trade flows; 3) Develop a new software platform that embeds the devised methods from objectives #2 to visualize and characterize the nature of truck movements in Canada and between Canada and the US; 4) Model freight transportation movement to understand the travel behavior of shippers, and the underlying factors affecting the movement of cargo through transportation facilities; 5) Develop integrated simulation and microsimulation predictive models for freight transportation, trade flow and supply chain movements, to examine policies that could improve the performance of traffic on major congested corridors and cross-border points. The novelty of the proposed research program is twofold: 1) this is the first research program in Canada to analyze a big-data that depicts the movement of a large number of Canadian trucks for the years 2015 and 2016; and 2) to our knowledge, this is the only research program in Canada that seeks to develop and apply a suite of new data mining and fusion methods on big-data coming from GPS and use the outcomes to devise freight microsimulation models.

现代供应链流程严重依赖于不同制造商、分销商和零售商之间转移的货物。迄今为止，卡车是加拿大货物运输的主要运输方式。近年来货运活动的增加凸显了了解卡车货运的重要性。以前研究货运运动的努力依赖于调查数据。然而，这些调查对于分析师来说通常很昂贵，对于受访者来说也很耗时。过去几年中新兴的调查替代方法是对全球定位系统 (GPS) 数据的分析。二十多年来，承运商一直在使用 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绝佳的机会，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 记录最初并不是作为交通模型和分析的输入，2）所获得的 GPS 数据的数量、速度和种类转化为必须分析的数十亿条记录（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，以便理解和模拟卡车在太空中的移动和供应链流程。 为此，该研究计划的具体目标如下： 1）利用描述 7 个月期间（2015 年 9 月至 2016 年 3 月）60,000 辆加拿大卡车运输情况的大数据，推进加拿大货运和供应链流程的现有知识水平； 2) 开发、实施和测试新的数据挖掘方法，可用于提取有关卡车运输和贸易流的有意义的模式和知识； 3) 开发一个新的软件平台，嵌入目标#2 中设计的方法，以可视化和描述加拿大以及加拿大和美国之间卡车运输的性质； 4）对货运运动进行建模，以了解托运人的出行行为，以及影响货物通过运输设施运动的潜在因素； 5) 开发货运、贸易流和供应链运动的综合模拟和微观模拟预测模型，以研究可改善主要拥堵走廊和跨境点交通绩效的政策。拟议研究计划的新颖性有两个：1）这是加拿大第一个分析大数据的研究计划，该数据​​描述了 2015 年和 2016 年大量加拿大卡车的移动情况； 2) 据我们所知，这是加拿大唯一的研究项目，旨在开发和应用一套新的数据挖掘和融合方法来处理来自 GPS 的大数据，并使用结果来设计货运微观模拟模型。