A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City
Vehicular pollution becomes a crucial issue within the travel planning of a smart city. Especially, the pollution level at Sensitive Points (SP) like Schools and Hospitals should be kept within a threshold level while a routing solution is offered. In the existing works, the attempt to consider envi...
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2025-01-01
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| author | Pratik Dutta Soumyadeep Sur Sankhayan Choudhury Sunirmal Khatua |
| author_facet | Pratik Dutta Soumyadeep Sur Sankhayan Choudhury Sunirmal Khatua |
| author_sort | Pratik Dutta |
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| description | Vehicular pollution becomes a crucial issue within the travel planning of a smart city. Especially, the pollution level at Sensitive Points (SP) like Schools and Hospitals should be kept within a threshold level while a routing solution is offered. In the existing works, the attempt to consider environmental pollution within traffic planning is minimal. In this attempt, we have proposed a framework for offering a routing strategy maintaining the desired pollution level at Sensitive Points. However, the most crucial challenge is to generate an estimation model for measuring pollution at Sensitive Points in an accurate way. In the proposed estimation model, we have attempted to accommodate the meteorological and other essential factors to make it more accurate. The pollution measures as computed by the model within SPs are analyzed for identifying the hot-spots, i.e., the alarming points where the pollution measure is supposed to be higher than the pre-defined threshold. Finally, the rerouting is executed on the affected road segments to maintain the desired level of pollution measured at the hot spots. Moreover, the re-routing has been done (if needed) so that the average remaining travel time of the vehicles will be minimal. Thus, the solution not only focuses on the environmental issues but also addresses the users’ satisfaction in terms of travel time. In the experiment phase, the traffic network is simulated by SUMO, and the entire proposal is implemented to compare with the notable existing comparable works. The proposed approach performs better in terms of the identified metrics, achieving a reduction in Average Vehicle Rerouting (AVR) to 17.26% compared to 20.10% in OPFTCaAP and maintaining a minimal Average Travel Time (ATT) increase for buses (-0.06%). |
| format | Article |
| id | doaj-art-305e64990b9f4453b601403967036d90 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-305e64990b9f4453b601403967036d902025-01-21T00:01:55ZengIEEEIEEE Access2169-35362025-01-0113100431006110.1109/ACCESS.2025.352898710839414A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart CityPratik Dutta0https://orcid.org/0000-0002-0742-9016Soumyadeep Sur1https://orcid.org/0009-0009-9979-7833Sankhayan Choudhury2Sunirmal Khatua3https://orcid.org/0000-0002-2103-6040Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, IndiaDepartment of Computer Science and Engineering, University of Calcutta, Kolkata, IndiaDepartment of Computer Science and Engineering, University of Calcutta, Kolkata, IndiaDepartment of Computer Science and Engineering, University of Calcutta, Kolkata, IndiaVehicular pollution becomes a crucial issue within the travel planning of a smart city. Especially, the pollution level at Sensitive Points (SP) like Schools and Hospitals should be kept within a threshold level while a routing solution is offered. In the existing works, the attempt to consider environmental pollution within traffic planning is minimal. In this attempt, we have proposed a framework for offering a routing strategy maintaining the desired pollution level at Sensitive Points. However, the most crucial challenge is to generate an estimation model for measuring pollution at Sensitive Points in an accurate way. In the proposed estimation model, we have attempted to accommodate the meteorological and other essential factors to make it more accurate. The pollution measures as computed by the model within SPs are analyzed for identifying the hot-spots, i.e., the alarming points where the pollution measure is supposed to be higher than the pre-defined threshold. Finally, the rerouting is executed on the affected road segments to maintain the desired level of pollution measured at the hot spots. Moreover, the re-routing has been done (if needed) so that the average remaining travel time of the vehicles will be minimal. Thus, the solution not only focuses on the environmental issues but also addresses the users’ satisfaction in terms of travel time. In the experiment phase, the traffic network is simulated by SUMO, and the entire proposal is implemented to compare with the notable existing comparable works. The proposed approach performs better in terms of the identified metrics, achieving a reduction in Average Vehicle Rerouting (AVR) to 17.26% compared to 20.10% in OPFTCaAP and maintaining a minimal Average Travel Time (ATT) increase for buses (-0.06%).https://ieeexplore.ieee.org/document/10839414/Intelligent transportation system (ITS)pollution hotspotair qualityreroutingsmart cityvehicle selection |
| spellingShingle | Pratik Dutta Soumyadeep Sur Sankhayan Choudhury Sunirmal Khatua A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City IEEE Access Intelligent transportation system (ITS) pollution hotspot air quality rerouting smart city vehicle selection |
| title | A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City |
| title_full | A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City |
| title_fullStr | A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City |
| title_full_unstemmed | A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City |
| title_short | A Traffic Management System by Identifying Pollution Hotspots Among Sensitive Points in a Smart City |
| title_sort | traffic management system by identifying pollution hotspots among sensitive points in a smart city |
| topic | Intelligent transportation system (ITS) pollution hotspot air quality rerouting smart city vehicle selection |
| url | https://ieeexplore.ieee.org/document/10839414/ |
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