Citation

Kashiyama T, Pang Y, Sekimoto Y. Transp. Res. C Emerg. Technol. 2017; 85: 249-267.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.trc.2017.09.016

PMID

unavailable

Abstract

Understanding people flow at a citywide level is critical for urban planning and commercial development. Thanks to the ubiquity of human location tracking devices, many studies on people mass movement with mobility logs have been conducted. However, high cost and severe privacy policy constraints still complicate utilization of these data in practice. There is no dataset that anyone can freely access, use, modify, and share for any purpose. To tackle this problem, we propose a novel dataset creation approach (called Open PFLOW) that continuously reports the spatiotemporal positions of all individual's in urban areas based on open data. With fully consideration of the privacy protection, each entity in our dataset does not match the actual movement of any real person, so that the dataset can be totally open to public as part of data infrastructure. Because the result is shown at a disaggregate level, users can freely modify, process, and visualize the dataset for any purpose. We evaluate the accuracy of the dataset by comparing it with commercial datasets and traffic census indicates that it has a high correlation with mesh population and link-based traffic volume.

Language: en

Keywords

Attitude formation; Data visualization; Open data; Public Participation GIS (PPGIS); Urban simulation