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Mobile Sensing for Multipurpose Applications in Transportation

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Abstract

Routine and consistent data collection is required to address contemporary transportation issues. The cost of data collection increases significantly when sophisticated machines are used to collect data. Due to this constraint, State Departments of Transportation struggle to collect consistent data for analyzing and resolving transportation problems in a timely manner. Recent advancements in sensors integrated into smartphones have resulted in a more affordable method of data collection. The primary objective of this study is to develop and implement a smartphone-based application for transportation-related data collection. The app consists of three major modules: a frontend graphical user interface (GUI), a sensor module, and a backend module. While the frontend GUI enables interaction with the app, the sensor modules collect relevant data such as video, gyroscope, motion and accelerometer readings while the app is in use. The backend leverages a real-time database to stream and store data from sensors, together with providing the computational resources needed to support the application. In comparison to other developed apps for transportation data collection, this app is not overly reliant on the internet enabling the app to be used in internet-restricted areas. Additionally, the app is designed for multipurpose applications in transportation. The collected data were analyzed for a variety of purposes, including calculating the International Roughness Index (IRI), identifying pavement distresses, and understanding driver’s behaviors and environment. From the sensor data, we detected turning movements, lane changes and estimated IRI values. In addition, several pavement distresses were identified from the video data with machine learning.

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Data Availability

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Civil and Environmental Engineering Department, University of Missouri-Columbia, Columbia, USA

    Armstrong Aboah & Yaw Adu-Gyamfi

  2. Computer Science Department, University of Missouri-Columbia, Columbia, USA

    Michael Boeding

Authors
  1. Armstrong Aboah
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  2. Michael Boeding
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  3. Yaw Adu-Gyamfi
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Contributions

Each of the three authors made equal contribution to: conceptualization, conducting experiments, analysis of the experimental data, writing, and reviewing the manuscript.

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Correspondence to Armstrong Aboah.

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Aboah, A., Boeding, M. & Adu-Gyamfi, Y. Mobile Sensing for Multipurpose Applications in Transportation. J. Big Data Anal. Transp. 4, 171–183 (2022). https://doi.org/10.1007/s42421-022-00061-8

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  • DOI: https://doi.org/10.1007/s42421-022-00061-8

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