Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning
Name
sensors-22-00456.pdf
Size
8.65 MB
Format
Adobe PDF
Checksum (MD5)
c9a5e19ed9be01f3003ecd0f8ff5a89f
Author(s)
Bustamante-Bello, Rogelio
García-Barba, Alec
Arce-Saenz, Luis A.
Curiel-Ramirez, Luis A.
Izquierdo-Reyes, Javier
Ramirez-Mendoza, Ricardo A.
Date Issued
January 8, 2022
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 22 (2): 456 (2022)
Version
Final published version
Abstract
Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies.
MIT Department
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/s22020456
