Citation

Keler A, Krisp JM. J. Locat. Based Serv. 2019; 13(3): 159-177.

Copyright

(Copyright © 2019, Informa - Taylor and Francis Group)

DOI

10.1080/17489725.2019.1588406

PMID

unavailable

Abstract

Urban mobility has complex patterns and principles. Data of moving entities on the underlying transportation infrastructure can help understanding those complex patterns and principles. Therefore, we need static infrastructural information and knowledge on spatio-temporal movement patterns of public transport services and of various vehicle fleets. We focus on inspecting data partitions of individual taxi movement acquisitions in New York City (NYC), together with OpenStreetMap (OSM) data extracts, for gaining more knowledge about the complex daily mobility patterns in NYC. We select trip information of tracked boro taxi drivers, who are restricted to pick up customers at the airports and the southern part of Manhattan. By computing with taxi customer drop-off positions, we define drop-off clusters as the customer destination hotspots of selected Saturdays in June 2015. These hotspots are then related to the OSM road network, in particular to its derivatives: complicated crossings. By comparing with a previous assumption of detecting 'fast leaving' behaviour within the restricted zone, we receive characteristic matching results: only few destination hotspots appear at complicated crossings. Nearly all the matching intersections have nearby situated pedestrian zones and many are associated with previous construction measures. Finally, we reason on the usefulness of the proposed method.

Language: en

Keywords

boro taxis; mobility patterns; spatial analysis; transportation infrastructure; Urban transportation; vehicle fleets