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

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。迄今为止，卡车是加拿大运输货物的主要运输方式。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。在过去几年中，一种新兴的替代调查方法是分析全球定位系统数据。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。 为此，该研究计划的具体目标如下：1）使用描述6万辆加拿大卡车在7个月内移动的大数据，推进加拿大货运和供应链流程的知识现状（2）开发、实施和测试新的数据挖掘方法，可用于提取关于卡车移动和贸易流动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）模拟货物运输运动，以了解托运人的旅行行为，以及影响货物通过运输设施的运动的潜在因素; 5）为货物运输、贸易流动和供应链流动开发综合模拟和微观模拟预测模型，以审查可改善主要拥挤走廊和跨境点交通状况的政策。 该研究计划的新奇有两个方面：1）这是加拿大第一个分析2015年和2016年大量加拿大卡车运动的大数据的研究计划; 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，数据来自GPS和使用的结果来设计货运微观模拟模型。

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.