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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67902
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Application
JCEI’s Jaihind Institute of Management and Research, Vadgaon Sahani
Master of Computer Application,
JCEI’s Jaihind Institute of Management and Research, Vadgaon Sahani
Urban growth and the increasing number of vehicles have led to various transportation problems. These include road congestion, travel delays, fuel wastage and air pollution. Conventional traffic control approaches often struggle to manage continuously changing road conditions. This project explores the use of large-scale data analysis and machine learning to improve traffic monitoring and transportation planning. Information gathered from GPS-enabled devices, roadside sensors and monitoring cameras can be processed using distributed computing technologies such as Apache Hadoop and Apache Spark. Artificial intelligence techniques can help identify traffic patterns estimate congestion levels. Support dynamic traffic signal control. In addition, visual data representations and route analysis can assist in locating roads and identifying alternative travel paths. The suggested system focuses on improving traffic flow, cutting travel delays and using existing road infrastructure effectively. Overall combining data-processing methods with intelligent traffic analysis can support the development of reliable and sustainable urban transportation systems.
KEYWORDS
I believe that the traffic management system uses sensors, CCTV, GPS and other data sources to monitor traffic in areas. The traffic project can detect traffic jams, accidents and traffic congestion. The traffic system can control traffic by managing traffic lights and giving drivers routes. I believe that this study will promote traffic safety, cut traffic waiting time and optimize traffic flow. By doing it will reduce fuel waste and air pollution and it .
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Publication Date
10/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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