Patterns of origin destination distributions: Rules mining using massive GPS trajectory data

Abstract

Understanding the spatio-temporal distribution of road trafic conditions is imperative to the design of suitable countermeasures for congestion reduction. The amount of crowd sourced data from private mobility companies is rapidly expanding to include data sources that were previously not available; this data can assist in developing new approaches and algorithms to understand travel patterns and origin-destination (O-D) distributions. We acquired four months (February, June, July, and October of 2015) of INRIXWaypoint (global positioning system or GPS trajectories) data that includes vehicle trips from various sources. To determine the association between demographic, economic, and land use information and O-D patterns, we acquired Census block group level data from American Community Survey (ACS), and block level economic data from Longitudinal Employer-Household Dynamics (LEHD). We provided insights on the relationship between demographic information and O-D patterns by Census spatial unit 'block group'. Based on multi-source data, we used classification-based association rules mining to determine key association patterns. © 2020 CEUR-WS. All rights reserved.