A Computer Vision-Based Framework for Snow Removal Operation Routing
During snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Circuits and Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10500496/ |
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| author | Mohamed Karaa Hakim Ghazzai Yehia Massoud Lokman Sboui |
| author_facet | Mohamed Karaa Hakim Ghazzai Yehia Massoud Lokman Sboui |
| author_sort | Mohamed Karaa |
| collection | DOAJ |
| description | During snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-based image-based approach for estimating snow depth and traffic volume on roads. For segments monitored by CCTV cameras, we exploit images and supervised learning models to perform this task. For unmonitored roads, we use the Graph Convolutional Network architecture to predict parameters in a semi-supervised manner. Following that, we assign priority weights to all graph edges as a function of image-based attributes and road categories. We test the method using a real-world example, simulating snow removal within a study area in Montreal, Quebec, Canada. As input for the framework, we collect CCTV image data and combine it with a 2D map. As a result, more efficient snow removal operation can be achieved by optimizing the trajectories of trucks based on the computer vision module outputs. |
| format | Article |
| id | doaj-art-e97d9e3e149648f9ae0c66ec797da231 |
| institution | Kabale University |
| issn | 2644-1225 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Circuits and Systems |
| spelling | doaj-art-e97d9e3e149648f9ae0c66ec797da2312025-01-21T00:02:43ZengIEEEIEEE Open Journal of Circuits and Systems2644-12252024-01-015819110.1109/OJCAS.2023.332627410500496A Computer Vision-Based Framework for Snow Removal Operation RoutingMohamed Karaa0https://orcid.org/0009-0006-9759-3392Hakim Ghazzai1https://orcid.org/0000-0002-8636-4264Yehia Massoud2https://orcid.org/0000-0002-6701-0639Lokman Sboui3Systems Engineering Department, Ecole de Technologie Supérieure, Montreal, CanadaInnovative Technologies Laboratories, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaInnovative Technologies Laboratories, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaSystems Engineering Department, Ecole de Technologie Supérieure, Montreal, CanadaDuring snowfall, the utility of the road infrastructure is critical. Roads must be effectively cleared to ensure access to important locations and services. In this paper, we present an end-to-end framework for snow removal vehicle routing based on road priority. We offer an artificial intelligence-based image-based approach for estimating snow depth and traffic volume on roads. For segments monitored by CCTV cameras, we exploit images and supervised learning models to perform this task. For unmonitored roads, we use the Graph Convolutional Network architecture to predict parameters in a semi-supervised manner. Following that, we assign priority weights to all graph edges as a function of image-based attributes and road categories. We test the method using a real-world example, simulating snow removal within a study area in Montreal, Quebec, Canada. As input for the framework, we collect CCTV image data and combine it with a 2D map. As a result, more efficient snow removal operation can be achieved by optimizing the trajectories of trucks based on the computer vision module outputs.https://ieeexplore.ieee.org/document/10500496/Snow removalroad service prioritizationgraph analyticscomputer visionvehicle routing |
| spellingShingle | Mohamed Karaa Hakim Ghazzai Yehia Massoud Lokman Sboui A Computer Vision-Based Framework for Snow Removal Operation Routing IEEE Open Journal of Circuits and Systems Snow removal road service prioritization graph analytics computer vision vehicle routing |
| title | A Computer Vision-Based Framework for Snow Removal Operation Routing |
| title_full | A Computer Vision-Based Framework for Snow Removal Operation Routing |
| title_fullStr | A Computer Vision-Based Framework for Snow Removal Operation Routing |
| title_full_unstemmed | A Computer Vision-Based Framework for Snow Removal Operation Routing |
| title_short | A Computer Vision-Based Framework for Snow Removal Operation Routing |
| title_sort | computer vision based framework for snow removal operation routing |
| topic | Snow removal road service prioritization graph analytics computer vision vehicle routing |
| url | https://ieeexplore.ieee.org/document/10500496/ |
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