CONTACT US: Contact info
|
Citation |
Aboah A, Boeding M, Adu-Gyamfi Y. J. Big Data Anal. Transp. 2022; 4(2): 171-183. |
|
Copyright |
(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group) |
|
DOI |
|
PMID |
unavailable |
|
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. Language: en |
|
Keywords |
Accelerometer; Deep learning; International roughness index (IRI); Mobile sensing application |