Nairobi Motorcycle Transit Comparison Dataset: Fuel vs. Electric Vehicle Performance Tracking (2023)
Description
This dataset contains GPS tracking data and performance metrics for motorcycle taxis (boda bodas) in Nairobi, Kenya, comparing traditional internal combustion engine (ICE) motorcycles with electric motorcycles. The study was conducted in two phases: Baseline Phase: 118 ICE motorcycles tracked over 14 days (2023-11-13 to 2023-11-26) Transition Phase: 108 ICE motorcycles (control) and 9 electric motorcycles (treatment) tracked over 12 days (2023-12-10 to 2023-12-21) The dataset is organised into two main categories: Trip Data: Individual trip-level records containing timing, distance, duration, location, and speed metrics Daily Data: Daily aggregated summaries containing usage metrics, economic data, and energy consumption This dataset enables comparative analysis of electric vs. ICE motorcycle performance, economic modelling of transportation costs, environmental impact assessment, urban mobility pattern analysis, and energy efficiency studies in emerging markets. Institutions: EED Advisory Clean Air Taskforce Stellenbosch University
Files
Steps to reproduce
Raw Data Collection GPS tracking devices installed on motorcycles, collecting location data at 10-second intervals Rider-reported information on revenue, maintenance costs, and fuel/electricity usage Processing Steps GPS data cleaning: Filtered invalid coordinates, removed duplicates, interpolated missing points Trip identification: Defined by >1 minute stationary periods or ignition cycles Trip metrics calculation: Distance, duration, idle time, average/max speeds Daily data aggregation: Summed by user_id and date with self-reported economic data Validation: Cross-checked with rider logs and known routes Anonymisation: Removed start and end coordinates for first and last trips of each day to protect rider privacy and home locations Technical Information Geographic coverage: Nairobi, Kenya Time period: November-December 2023 Time zone: UTC+3 (East Africa Time) Currency: Kenyan Shillings (KES) Data format: CSV files Software used: Python 3.8 (pandas, numpy, geopy) Notes: Some location data points are intentionally missing to protect rider privacy. Self-reported economic and energy consumption data has some missing values where riders did not report.