Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility
The delay in transporting essential goods is primarily attributed to widespread traffic congestion globally. This issue not only results in significant time and fuel wastage but also poses a considerable challenge in efficiently disseminating traffic information and managing road conditions for auth...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10948423/ |
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| author | Anita Mohanty Ambarish G. Mohapatra Subrat Kumar Mohanty Tiansheng Yang Rajkumar Singh Rathore Ahmed Alkhayyat Deepak Gupta |
| author_facet | Anita Mohanty Ambarish G. Mohapatra Subrat Kumar Mohanty Tiansheng Yang Rajkumar Singh Rathore Ahmed Alkhayyat Deepak Gupta |
| author_sort | Anita Mohanty |
| collection | DOAJ |
| description | The delay in transporting essential goods is primarily attributed to widespread traffic congestion globally. This issue not only results in significant time and fuel wastage but also poses a considerable challenge in efficiently disseminating traffic information and managing road conditions for authorities. Addressing these challenges, vehicular ad hoc networks (VANETs) have emerged as a crucial component of the cognitive intelligent transportation system (C-ITS). To tackle this issue effectively, vehicle-to-vehicle (V2V) communication plays a crucial role in fostering cooperation and optimizing route management within transportation networks. This paper proposes an innovative congestion detection system that integrates the fuzzy k-means (FKM) clustering technique with the fuzzy analytical hierarchy process (FAHP). Utilizing the simulation of urban mobility (SUMO) simulator, a detailed transport network is modeled where vehicle parameters indicative of congestion are collected, integrated using sensor fusion, and analyzed. These parameters are processed using FKM clustering and a mathematical mean algorithm to identify key congestion indicators. Subsequently, FAHP prioritizes these collected parameters, pinpointing congestion hotspots within specific routes. By incorporating cognitive intelligence, the system continuously refines congestion detection and response strategies, enhancing traffic flow efficiency and enabling proactive congestion avoidance. This approach promises a more effective congestion detection methodology with minimal installation costs. Moreover, it can be effortlessly integrated into vehicles to facilitate congestion avoidance strategies, thereby enhancing overall traffic flow efficiency and mitigating the negative impacts of traffic congestion on transportation networks globally. |
| format | Article |
| id | doaj-art-3bfee41174b3413eb62d461ec6f0f30d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3bfee41174b3413eb62d461ec6f0f30d2025-08-20T02:12:34ZengIEEEIEEE Access2169-35362025-01-0113615386154810.1109/ACCESS.2025.355727610948423Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban MobilityAnita Mohanty0Ambarish G. Mohapatra1https://orcid.org/0000-0001-5139-8889Subrat Kumar Mohanty2Tiansheng Yang3https://orcid.org/0000-0001-7833-5386Rajkumar Singh Rathore4Ahmed Alkhayyat5https://orcid.org/0000-0002-0962-3453Deepak Gupta6Department of Electronics Engineering, Silicon University, Bhubaneswar, Odisha, IndiaDepartment of Electronics Engineering, Silicon University, Bhubaneswar, Odisha, IndiaDepartment of Electronics and Communication Engineering, Einstein Academy of Technology and Management, Bhubaneswar, Odisha, IndiaUniversity of South Wales, Pontypridd, U.K.Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Cardiff, U.K.Computer Technical Engineering Department, College of Technical Engineering, Islamic University, Najaf, IraqDepartment of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, IndiaThe delay in transporting essential goods is primarily attributed to widespread traffic congestion globally. This issue not only results in significant time and fuel wastage but also poses a considerable challenge in efficiently disseminating traffic information and managing road conditions for authorities. Addressing these challenges, vehicular ad hoc networks (VANETs) have emerged as a crucial component of the cognitive intelligent transportation system (C-ITS). To tackle this issue effectively, vehicle-to-vehicle (V2V) communication plays a crucial role in fostering cooperation and optimizing route management within transportation networks. This paper proposes an innovative congestion detection system that integrates the fuzzy k-means (FKM) clustering technique with the fuzzy analytical hierarchy process (FAHP). Utilizing the simulation of urban mobility (SUMO) simulator, a detailed transport network is modeled where vehicle parameters indicative of congestion are collected, integrated using sensor fusion, and analyzed. These parameters are processed using FKM clustering and a mathematical mean algorithm to identify key congestion indicators. Subsequently, FAHP prioritizes these collected parameters, pinpointing congestion hotspots within specific routes. By incorporating cognitive intelligence, the system continuously refines congestion detection and response strategies, enhancing traffic flow efficiency and enabling proactive congestion avoidance. This approach promises a more effective congestion detection methodology with minimal installation costs. Moreover, it can be effortlessly integrated into vehicles to facilitate congestion avoidance strategies, thereby enhancing overall traffic flow efficiency and mitigating the negative impacts of traffic congestion on transportation networks globally.https://ieeexplore.ieee.org/document/10948423/Cognitive intelligent transportation systemFAHPfuzzy k-meansSUMOurban mobilityV2V |
| spellingShingle | Anita Mohanty Ambarish G. Mohapatra Subrat Kumar Mohanty Tiansheng Yang Rajkumar Singh Rathore Ahmed Alkhayyat Deepak Gupta Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility IEEE Access Cognitive intelligent transportation system FAHP fuzzy k-means SUMO urban mobility V2V |
| title | Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility |
| title_full | Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility |
| title_fullStr | Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility |
| title_full_unstemmed | Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility |
| title_short | Integrating Cognitive Intelligence and VANET for Effective Traffic Congestion Detection in Smart Urban Mobility |
| title_sort | integrating cognitive intelligence and vanet for effective traffic congestion detection in smart urban mobility |
| topic | Cognitive intelligent transportation system FAHP fuzzy k-means SUMO urban mobility V2V |
| url | https://ieeexplore.ieee.org/document/10948423/ |
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