Quantifying informal public transport using GPS data

Abstract

Informal public transport modes transport the largest number of passengers in most developing countries. Despite its significance, limited information is available on the extent of its operations, and passenger counts alone do not provide sufficient insight into network coverage or passenger turnover. GPS tracking has emerged as a valuable tool, yet its potential for understanding minibus taxi operations at the road segment level remains underexplored. GPS studies of informal operators have rarely been extrapolated to volume counts per time period, due to statistical problems (non-representative sampling) and small sample sizes. This paper addresses this gap by developing a methodology to determine the minibus taxi vehicle trip count per street segment from GPS data, to map routes, and identify high-traffic corridors, with an illustrative application in the City of Tshwane, South Africa.

The methodology includes data inspection, addressing limitations, and counting trips per street segment using a database and QGIS visualisation. Additionally, the paper outlines detailed steps in QGIS for processing GPS data. We show that the method delivers plausible results at the segment level. The methodology can help to address the global South's need for data-driven interventions in its predominant public transport mode.