Data in Brief (Aug 2024)
High-fidelity tracking data gathered on minibus taxis in Stellenbosch, South Africa
Abstract
Minibus taxis, a form of informal shared mobility that carries up to 16 passengers, is the main mode of public transport in sub-Saharan Africa, and given global trends, a large-scale shift to electric paratransit is imminent in the coming decades. Modeling the energy consumption (kWh/km) of electric vehicle (EV) fleets is a pre-requisite for planning for fleet deployment, especially in energy-constrained contexts. Given the paucity of EVs in sub-Saharan Africa, ground-truth data on the energy consumption of electric paratransit does not exist for many developing contexts. Consequently, GPS tracking data on internal combustion engine (ICE) versions of these vehicles is often used to estimate the energy consumption of an electric equivalent. To date, only per-minute GPS tracking data has been captured on these vehicles and used for energy consumption estimates. But this sampling frequency is insufficient for accurate energy consumption estimates, especially given the unique micro-mobility patterns of minibus taxis that are characterized by many rapid acceleration/deceleration events in quick succession. Although simulators can be used to interpolate between the dataset, they have been shown to be inaccurate in the regional context. This article presents a dataset of high-fidelity micromobility data captured on minibus taxis in transit on four typical route types: inter-city, intra-city, uphill, and downhill. The main objective was to estimate energy requirements for the eventual electrification of these vehicles, the data was thus processed according to an electro-kinetic model. This high-fidelity mobility data was captured by “standardised passengers” with bespoke GPS-location logging devices sampling at 1 Hz. Trips on the four route types were recorded and saved in six folders – three routes, each in two directions, with one route being uphill in one direction and downhill in another. Each of the six folders have subfolders for time of day – morning, afternoon, and evening. In total 62 trips were recorded with varying durations, depending on the traffic and route length.
