Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning
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-...
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/138858 |
| _version_ | 1826205622728654848 |
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| author | Bustamante-Bello, Rogelio García-Barba, Alec Arce-Saenz, Luis A. Curiel-Ramirez, Luis A. Izquierdo-Reyes, Javier Ramirez-Mendoza, Ricardo A. |
| author2 | Massachusetts Institute of Technology. Microsystems Technology Laboratories |
| author_facet | Massachusetts Institute of Technology. Microsystems Technology Laboratories Bustamante-Bello, Rogelio García-Barba, Alec Arce-Saenz, Luis A. Curiel-Ramirez, Luis A. Izquierdo-Reyes, Javier Ramirez-Mendoza, Ricardo A. |
| author_sort | Bustamante-Bello, Rogelio |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:16:01Z |
| format | Article |
| id | mit-1721.1/138858 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:16:01Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1388582024-06-07T20:22:47Z Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning Bustamante-Bello, Rogelio García-Barba, Alec Arce-Saenz, Luis A. Curiel-Ramirez, Luis A. Izquierdo-Reyes, Javier Ramirez-Mendoza, Ricardo A. Massachusetts Institute of Technology. Microsystems Technology Laboratories 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. 2022-01-10T16:28:06Z 2022-01-10T16:28:06Z 2022-01-08 2021-11-11 2022-01-10T14:38:36Z Article http://purl.org/eprint/type/JournalArticle 1424-8220 https://hdl.handle.net/1721.1/138858 Sensors 22 (2): 456 (2022) http://dx.doi.org/10.3390/s22020456 Sensors Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Bustamante-Bello, Rogelio García-Barba, Alec Arce-Saenz, Luis A. Curiel-Ramirez, Luis A. Izquierdo-Reyes, Javier Ramirez-Mendoza, Ricardo A. Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning |
| title | Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning |
| title_full | Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning |
| title_fullStr | Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning |
| title_full_unstemmed | Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning |
| title_short | Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning |
| title_sort | visualizing street pavement anomalies through fog computing v2i networks and machine learning |
| url | https://hdl.handle.net/1721.1/138858 |
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