Road Anomalies Detection System Evaluation
Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/7/1984 |
| _version_ | 1798026060342755328 |
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| author | Nuno Silva Vaibhav Shah João Soares Helena Rodrigues |
| author_facet | Nuno Silva Vaibhav Shah João Soares Helena Rodrigues |
| author_sort | Nuno Silva |
| collection | DOAJ |
| description | Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities. |
| first_indexed | 2024-04-11T18:29:06Z |
| format | Article |
| id | doaj.art-c2e8045353dc42eb90e7674644a40c5f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:29:06Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c2e8045353dc42eb90e7674644a40c5f2022-12-22T04:09:32ZengMDPI AGSensors1424-82202018-06-01187198410.3390/s18071984s18071984Road Anomalies Detection System EvaluationNuno Silva0Vaibhav Shah1João Soares2Helena Rodrigues3Information Systems Department, University of Minho, 4800-058 Guimarães, PortugalInformation Systems Department, University of Minho, 4800-058 Guimarães, PortugalInformation Systems Department, University of Minho, 4800-058 Guimarães, PortugalInformation Systems Department, University of Minho, 4800-058 Guimarães, PortugalAnomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.http://www.mdpi.com/1424-8220/18/7/1984road anomaliesPCAFi-Waredata-miningcollaborative mobile sensing |
| spellingShingle | Nuno Silva Vaibhav Shah João Soares Helena Rodrigues Road Anomalies Detection System Evaluation Sensors road anomalies PCA Fi-Ware data-mining collaborative mobile sensing |
| title | Road Anomalies Detection System Evaluation |
| title_full | Road Anomalies Detection System Evaluation |
| title_fullStr | Road Anomalies Detection System Evaluation |
| title_full_unstemmed | Road Anomalies Detection System Evaluation |
| title_short | Road Anomalies Detection System Evaluation |
| title_sort | road anomalies detection system evaluation |
| topic | road anomalies PCA Fi-Ware data-mining collaborative mobile sensing |
| url | http://www.mdpi.com/1424-8220/18/7/1984 |
| work_keys_str_mv | AT nunosilva roadanomaliesdetectionsystemevaluation AT vaibhavshah roadanomaliesdetectionsystemevaluation AT joaosoares roadanomaliesdetectionsystemevaluation AT helenarodrigues roadanomaliesdetectionsystemevaluation |