VisionFlowX: The Future of Traffic Intelligence

Sourish Dey; Lubdhak Nairith Saha Arnish; Sparsh Bajoria; Pamela Dey Sarkar; KOUSHIK PAUL; Shreyanshu Ranjan

doi:10.36948/ijfmr.2025.v07i01.36807

VisionFlowX: The Future of Traffic Intelligence

Author(s)	Sourish Dey, Lubdhak Nairith Saha Arnish, Sparsh Bajoria, Pamela Dey Sarkar, KOUSHIK PAUL, Shreyanshu Ranjan
Country	India
Abstract	This document details the design and implementation of a novel smart urban traffic management system, synergistically integrating the capabilities of the Internet of Things (IoT) and computer vision. Addressing the multifaceted challenges of modern urban traffic, including congestion, safety concerns, and regulatory adherence, the system employs a hybrid edge-cloud architecture. A distributed network of intelligent IoT devices, encompassing smart cameras equipped with on-device AI processing, LIDAR, radar, and environmental sensors, captures real-time traffic data. Edge computing nodes, strategically deployed at intersections, perform localizeddata analysis, enabling immediate responses such as adaptive traffic signal adjustments and prioritized emergency vehicle movement. Simultaneously, the cloud platform aggregates data from all edge nodes, facilitating comprehensive traffic pattern analysis, predictive modeling, and system-wide optimization strategies. Advanced computer vision algorithms, including YOLOv8-based object detection, lane tracking, and pedestrian activity recognition, provide critical insights into traffic dynamics and potential infractions. Machine learning models, trained on both real- time and historical traffic data, empower the system to dynamically adapt signal timings and forecast congestion hotspots. Integration with existing traffic infrastructure and a user-friendly mobile application for real-time traffic information dissemination are also key features. This document explores the system's architecture, the interplay of hardware and software components, communication protocols, the development lifecycle, and the mitigation of critical challenges like scalability, security, and latency.
Keywords	IoT-based Traffic Management ;Computer Vision in Urban Mobility; Real-time Vehicle Detection; AI-driven Traffic Optimization;
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.36807
Short DOI	https://doi.org/g84xh6

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VisionFlowX: The Future of Traffic Intelligence

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