@article{ref1,
title="Scalable framework for enhancing raw GPS trajectory data: application to trip analytics for transportation planning",
journal="Journal of big data analytics in transportation",
year="2021",
author="Vander Laan, Zachary and Franz, Mark and Marković, Nikola",
volume="3",
number="2",
pages="119-139",
abstract="Transportation analysts and planners are beginning to leverage GPS trajectory data to draw additional insight into travel behavior and enhance data-driven decision-making capabilities. However, raw trajectory data cannot be utilized directly; they require extensive processing prior to analysis. This paper presents a scalable approach for enhancing raw GPS trajectory data by snapping and routing waypoints along a user-defined target road network that may have discontinuities and missing links, thus enabling trajectory datasets to be used in conjunction with the types of non-routable road networks often employed by transportation agencies. The proposed approach fuses a well-established map matching solution with a custom waypoint conflation procedure, and provides a framework to execute the trajectory processing in parallel to efficiently leverage available computing resources for large GPS datasets. To demonstrate its capability, four months of 2018 trajectory data from Maryland (2.5 billion waypoints from 46 million trips) are processed in this manner and assigned to a Traffic Message Channel road network. The enhanced trajectory data are then used to demonstrate a real-world use case, identifying key travel patterns along the I-270 spur in Maryland--a key commuting corridor currently being considered by the Maryland Department of Transportation for a congestion mitigation investment.<p /> <p>Language: en</p>",
language="en",
issn="2523-3556",
doi="10.1007/s42421-021-00040-5",
url="http://dx.doi.org/10.1007/s42421-021-00040-5"
}