arrow_back Smartphone and Inertial Measurement Units data for vehicular transportation analysis

Dataset usage instructions

 Quantitative data for various vehicular transportation mode analysis 

          PercepTrans data

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Authors:

Alain KIBANGOU, Walter Musakwa, Nomfundo CELE, Thembani MOYO, Hassen FOURATI

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Contact:

alain.kibangou@univ-grenoble-alpes.fr; wmusakwa@uj.ac.za

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Acknowledgements:

This work was partly supported by Campus France, the CNRS, then French embassy in South Africa, and the National Research Foundation South Africa, Grant Number 129925, South Africa/France (PHC Protea) Joint Research Programme-PercepTrans project.

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Data description:

PercepTrans Data Database

Introduction

The "PercepTrans Data" database comprises raw sensor data collected from smartphones and inertial measurement units (IMUs) during 113 transportation trips in Johannesburg and Durban, South Africa. This dataset aims to characterize the movement of vehicles across various transportation modes. Passengers carrying the sensors had restricted movement, effectively linking the sensor data to the vehicle's motion. The data is intended for analysis of vehicle movement, including assessing driving quality and identifying mismatches between perceived and actual driving performance.

Data Overview

The database includes the following sensor data:

  • Location: GPS coordinates.
  • Velocity: Speed.
  • Acceleration: Linear acceleration in three axes.
  • Angular Rate (Gyroscope): Rate of rotation in three axes.
  • Magnetic Field: Magnetic field strength in three axes.
  • Atmospheric Pressure: Ambient air pressure.

Sampling Rates:

  • Smartphone sensor data: Varies based on the SensLog app settings and phone capabilities.
  • IMU (Xsens MTw Awinda): 100 Hz.

Database Structure

The main directory contains:

  • README.txt (This file): Provides an overview of the database.
  • files_inventory.xlsx: A detailed inventory of all data files.

  • [Transportation Mode]_[Location] Subfolders (6 total): Raw data organized by transportation mode and location.

    • Transportation Modes:
      • Minibus Taxi
      • Bus
      • Ride Hailing (RH)
      • Private Car
    • Locations: Johannesburg and Durban (including surrounding suburbs).

files_inventory.xlsx Details

The files_inventory.xlsx file provides the following information for each data file:

  • File Size: The size of the data file.
  • Transportation Mode: The mode of transportation used during the trip.
  • Location: The city or region where the data was collected.
  • Device Type: Whether the data was collected using a smartphone or an IMU.
  • Sensor Count: The number of sensors used (1 for smartphones, 1-3 for IMUs). IMUs were placed in trouser or shirt pockets.
  • Data Types: The specific sensor data recorded (GPS location, velocity, acceleration, angular rate, magnetic field, atmospheric pressure).

Data Acquisition

  • Smartphones: Data was collected using the SensLog application.
  • IMUs: Xsens MTw Awinda IMUs were used for capturing inertial data.

Notes

  • "Raw data" indicates that minimal preprocessing has been performed on the sensor readings.
  • The files_inventory.xlsx file is crucial for navigating and understanding the dataset.

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Publications:

If you use this dataset for publication purposes, please cite us as follows:

Alain KIBANGOU, Walter Musakwa, Nomfundo CELE, Thembani MOYO, Hassen FOURATI, PercepTrans Data, 2025.

This dataset was partly used in the Following publications: Cele, N., Kibangou, A., & Musakwa, W. (2025) Qualitative and Quantitative Assessment of Public Minibus Taxi Driving. International Journal of Intelligent Transportation Systems Research. https://doi.org/10.1007/s13177-025-00465-0

Nomfundo P. Cele, Alain Kibangou, Walter Musakwa. Machine Learning Analysis of Informal Minibus Taxi Driving. MAIH 2024 - International Conference on Mobility, Artificial Intelligence and Health, Nov 2024, Marraksh, Morocco. https://hal.science/hal-04818563

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