Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities

• URL: http://arxiv.org/abs/2503.02967v1
• Date: Tue, 04 Mar 2025 19:50:42 GMT
• Title: Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities
• Authors: Seyed Hossein Hosseini DolatAbadi, Sayyed Mohammad Hossein Hashemi, Mohammad Hosseini, Moein-Aldin AliHosseini,
• Abstract summary: This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems.<n>By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors.
• Score: 0.9374652839580183
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.

Related papers

• AIoT-based smart traffic management system [0.0]
  This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments.<n>By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware.<n>The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control.
  arXiv  Detail & Related papers  (2025-02-04T11:38:42Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks. A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• Artificial Intelligence in Traffic Systems [0.0]
  This article endeavors to review the topics where AI and traffic management intersect.<n>It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition.<n>The benefits of AI in traffic management are also diverse.
  arXiv  Detail & Related papers  (2024-12-16T18:15:49Z)
• AI-Driven Scenarios for Urban Mobility: Quantifying the Role of ODE Models and Scenario Planning in Reducing Traffic Congestion [0.0]
  This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic congestion dynamics.<n>We assess the role of AI innovations, such as autonomous vehicles and intelligent traffic management, in mitigating congestion under varying regulatory frameworks.
  arXiv  Detail & Related papers  (2024-10-25T18:09:02Z)
• GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [81.82487256783674]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.<n>This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems [0.0]
  This study delves into the application of cutting-edge technological methods in smart cities. We present a novel comprehensive dataset for traffic accident detection. This dataset is expected to advance academic research and also enhance real-time accident detection applications.
  arXiv  Detail & Related papers  (2024-01-07T21:50:24Z)
• Uncertainty Quantification for Image-based Traffic Prediction across Cities [63.136794104678025]
  Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning. We investigate their application to a large-scale image-based traffic dataset spanning multiple cities. We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow.
  arXiv  Detail & Related papers  (2023-08-11T13:35:52Z)
• From Data to Action: Exploring AI and IoT-driven Solutions for Smarter Cities [0.0]
  This work introduces an intelligent city management system that provides a data-driven approach to three use cases. A case study in Aveiro City demonstrates the system's effectiveness in generating actionable insights.
  arXiv  Detail & Related papers  (2023-06-06T10:22:43Z)
• Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis [71.2468615993246]
  Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness.
  arXiv  Detail & Related papers  (2022-10-18T17:28:58Z)
• AI in Smart Cities: Challenges and approaches to enable road vehicle automation and smart traffic control [56.73750387509709]
  SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
  arXiv  Detail & Related papers  (2021-04-07T14:31:08Z)
• Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
  Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050. The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
  arXiv  Detail & Related papers  (2020-05-09T13:53:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.