@article{ref1,
title="Using trajectory data to explore roadway characterization for bikeshare network",
journal="Journal of intelligent transportation systems: technology, planning, and operations",
year="2018",
author="Liu, Xiaoyue Cathy and Taylor, Jeffrey and Porter, Richard J. and Wei, Ran",
volume="22",
number="6",
pages="530-546",
abstract="The rapid expansion of bikeshare programs nationwide provides opportunities to gain insights on the optimal development of multimodal networks and bike-friendly environments. The profusion of trajectory-level data produced by bikeshare systems allows for information extraction on users' route preferences and, if modeled properly, will lead to a greater understanding of road characteristics that are appealing to bikeshare users. Leveraging Global Positioning System (GPS) data obtained from the GREENbike program, this study proposes a method to characterize roadways (e.g. collector, peripheral road, attractive road, and local road) on the basis of a variety of network centrality functions. The methodology is able to uncover the structure of the underlying transportation network and identify locations of critical bicycle infrastructures. A series of centrality measures, including degree, shortest-path betweenness, and random-walk betweenness centrality are implemented to determine the roadway classifications. Their suitability and usability for this purpose is then explored and discussed at length through a sensitivity analysis. The method can be applied to any bikeshare system that has access to trajectory-level (i.e. GPS, crowdsourcing) data for identifying road attributes that are appealing to bike users. <br><br>RESULTS can effectively guide future investment choices.<p /> <p>Language: en</p>",
language="en",
issn="1547-2450",
doi="10.1080/15472450.2018.1444484",
url="http://dx.doi.org/10.1080/15472450.2018.1444484"
}