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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | DRIVESYNC - AI POWERED TRAFFIC MANAGEMENT SYSTEM |
| Authors Name: | Ayavanth Tripathi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_210014
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| Published Paper Id: | IJRTI2602139 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | |
| Abstract: | This paper presents DriveSync, an AI-based adaptive traffic signal control system designed to optimize urban traffic flow using real-time computer vision and deep learning techniques. The system performs live analysis of video feeds to detect and count vehicles, estimate traffic density, and dynamically adjust traffic signal timings based on current road conditions. Unlike conventional fixed-timer traffic lights, DriveSync responds intelligently to fluctuating traffic volumes, thereby reducing unnecessary delays and traffic congestion. The DriveSync project was given the National Best Innovators Award, highlighting its potential as a practical and scalable solution for intelligent traffic management. A key innovation of the proposed system is the use of a single camera mounted on a rotating mechanism, capable of covering multiple traffic approaches by sequentially rotating up to 360 degrees. This design enables comprehensive intersection monitoring using only one camera, significantly reducing hardware cost and infrastructure complexity compared to multi-camera setups. In emergency scenarios, the system supports priority-based signal control, where emergency vehicles such as ambulances are visually detected and dynamically granted a green signal to ensure rapid and unobstructed passage through intersections. The system architecture allows future integration of custom-trained models or multimodal sensing for explicit emergency vehicle classification. DriveSync employs YOLOv8 and OpenCV for real-time vehicle detection and traffic analysis, integrated with an adaptive signal control algorithm that determines optimal green-light durations based on observed traffic density. Signal execution is managed through a microcontroller-based hardware interface (currently implemented using Arduino, with scope for Raspberry Pi integration), enabling reliable real-time control through a low-cost, sensor-free architecture. It also has reduced the time up to 60% for waiting. The system is designed to support cloud-based traffic data storage, city-level traffic coordination, and scalable deployment for smart city applications. A beta prototype implementing the core adaptive signal control mechanism has been developed and experimentally evaluated. Experimental observations indicate improved traffic flow efficiency and reduced idle waiting time compared to traditional fixed-time traffic signal systems, demonstrating an approximate improvement of up to 60% in traffic flow efficiency under observed conditions. By minimizing vehicle idling, DriveSync contributes to reduced fuel consumption, lower carbon emissions, and enhanced road safety. Owing to its affordability, scalability, minimal infrastructure requirements, and single-camera rotating design, DriveSync offers a practical and sustainable solution for intelligent traffic management systems. |
| Keywords: | DriveSync, adaptive traffic signal control, edge AI, single rotating camera, real-time vehicle detection, YOLOv8 / YOLOv5, vehicle counting, object tracking (ByteTrack), traffic density estimation, adaptive green-time optimization, emergency vehicle preemption, low-cost sensor-free system, Raspberry Pi edge deployment, Arduino signal interface, traffic flow optimization, heatmap analysis, inference latency, simulation & Colab evaluation, V2I integration, LiDAR augmentation, reinforcement learning (future work), smart city applications, sustainable urban mobility. |
| Cite Article: | "DRIVESYNC - AI POWERED TRAFFIC MANAGEMENT SYSTEM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.b298-b314, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602139.pdf |
| Downloads: | 00090 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2602139
Registration ID:210014
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier):
Page No: b298-b314 Country: ghaziabad, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602139 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602139 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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