Land-Use Classification Using Taxi GPS Traces

TLDR

Detailed land use is essential for urban planning but hard to obtain, while readily available vehicle GPS traces capture human mobility that is closely related to regional land use. The study explores using one year of taxi GPS traces from 4000 taxis to classify urban land use and infer social functions. The authors analyze pick‑up and drop‑off patterns from a year of GPS traces of 4000 taxis to classify land‑use types. Pick‑up/drop‑off dynamics extracted from taxi traces matched land‑use classes, and using six engineered features the authors achieved 95 % classification accuracy, revealing both stable land‑use types and transitions that signal real‑world social events.

Abstract

Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.