Title: A big data analytics framework for border crossing transportation
Authors: Haibo Wang; Da Huo; Yaquan Xu
Sanchez School of Business, Texas A&M International University Laredo, USA
School of International Trade and Economics, Central University of Finance and Economics, Beijing, China
School of Science and Technology, Georgia Gwinnett College, Lawrenceville, USA
Abstract: In this paper, the authors present a framework on developing a comprehensive system to analyse border crossing transportation using an open-source meta-data acquisition and aggregation tool. It is a platform integration approach based on Hadoop, MapReduce and MongoDB to consolidate databases from both the USA and Mexico. We design data-driven XML schema for tagging the data entries from different sources with different formats, and implement a package using open-source software R to aggregate XML-transformed data into time and space dimensions. Then the transformed data is analysed by a difference-in-difference (DiD) estimation model to understand the behaviour of border crossing transportation.
Keywords: big data analytics; border crossing transportation; difference-in-difference estimation.
Int. J. of Big Data Intelligence, 2017 Vol.4, No.4, pp.227 - 236
Submission date: 01 Mar 2016
Date of acceptance: 18 Aug 2016
Available online: 04 Aug 2017