Optimization of Transportation Traffic using Agile Approach

-д хадгалсан:
Номзүйн дэлгэрэнгүй
-д хэвлэсэн:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2025)
Үндсэн зохиолч: Ajay, K
Бусад зохиолчид: Sundaramoorthi, Abhishek, S Prince Mary
Хэвлэсэн:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Нөхцлүүд:
Онлайн хандалт:Citation/Abstract
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LEADER 00000nab a2200000uu 4500
001 3185346739
003 UK-CbPIL
024 7 |a 10.1109/ICEARS64219.2025.10940872  |2 doi 
035 |a 3185346739 
045 2 |b d20250101  |b d20251231 
084 |a 228229  |2 nlm 
100 1 |a Ajay, K  |u Sathyabama Institute of Science and Technology,Dept. of CSE,Chennai,Tamilnadu 
245 1 |a Optimization of Transportation Traffic using Agile Approach 
260 |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  |c 2025 
513 |a Conference Proceedings 
520 3 |a Conference Title: 2025 International Conference on Electronics and Renewable Systems (ICEARS)Conference Start Date: 2025, Feb. 11 Conference End Date: 2025, Feb. 13 Conference Location: Tuticorin, IndiaTraffic congestion and its related delays present significant challenges to urban infrastructure and societal efficiency. This project demonstrates an intelligent traffic management system that combines real-time vehicle detection, adaptive signal control, and predictive traffic modeling to solve the problem of urban congestion. The implementation of YOLOv8 for the identification and counting of vehicles with accurate precision in low-quality surveillance footage will feed into the production of the real-time data important for the efficient management of traffic flow. Such information feeds into a Signal Switching Algorithm, which continually adjusts signal timings according to the instantaneous level of traffic to optimize intersections for smoother flow. Validation of its effectiveness is done with a computer simulation of a 4-way intersection via Pygame where different scenarios affecting the reduction of congestion are tested upon the algorithm. With all this being said, another model makes use of historical data about traffic to foretell such patterns of flow, allowing pre-optimized signal timings to filter into traffic management to optimize flow even more. Such a system would provide an overall city traffic management approach that operates in real time and has data-driven insight into predicting more sustainable city transportation based on predictive analysis. 
653 |a Traffic flow 
653 |a Data transfer (computers) 
653 |a Flow pattern 
653 |a Traffic management 
653 |a Optimization 
653 |a Predictive control 
653 |a Algorithms 
653 |a Computer simulation 
653 |a Traffic intersections 
653 |a Traffic congestion 
653 |a Switching theory 
653 |a Real time 
653 |a Urban transportation 
653 |a Economic 
700 1 |a Sundaramoorthi, Abhishek  |u Sathyabama Institute of Science and Technology,Dept. of CSE,Chennai,Tamilnadu 
700 1 |a S Prince Mary  |u Sathyabama Institute of Science and Technology,Dept. of CSE,Chennai,Tamilnadu 
773 0 |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings  |g (2025) 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3185346739/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch