AIoT-based smart traffic management system

• URL: http://arxiv.org/abs/2502.02821v1
• Date: Tue, 04 Feb 2025 11:38:42 GMT
• Title: AIoT-based smart traffic management system
• Authors: Ahmed Mahmoud Elbasha, Mohammad M. Abdellatif,
• Abstract summary: This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments. By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware. The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control.
• Score: 0.0
• License:
• Abstract: This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments. By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware, thereby minimizing both deployment costs and ongoing maintenance expenses. The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control that prioritizes directions with higher traffic volumes. This real-time adaptability ensures smoother traffic flow, reduces congestion, and minimizes waiting times for drivers. Additionally, the proposed system is simulated using PyGame to evaluate its performance under various traffic conditions. The simulation results demonstrate that the AI-based system out-performs traditional static traffic light systems by 34%, leading to significant improvements in traffic flow efficiency. The use of AI to optimize traffic signals can play a crucial role in addressing urban traffic challenges, offering a cost-effective, scalable, and efficient solution for modern cities. This innovative system represents a key advancement in the field of smart city infra-structure and intelligent transportation systems.

Related papers

• Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring [6.648291808015463]
  This research leverages the LLaVA visual grounding multimodal large language model (LLM) for traffic monitoring tasks on the real-time Quanser Interactive Lab simulation platform. Cameras placed at multiple urban locations collect real-time images from the simulation, which are fed into the LLaVA model with queries for analysis. The system achieves 84.3% accuracy in recognizing vehicle locations and 76.4% in determining steering direction, outperforming traditional models.
  arXiv  Detail & Related papers  (2025-02-16T23:03:26Z)
• Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management [0.0]
  This paper introduces a novel approach by combining Federated Learning (FL) and Meta-Learning (ML) to create a decentralized, scalable, and adaptive traffic management system. We implement our model across a simulated network of smart traffic devices, demonstrating that Meta-Federated Learning significantly outperforms traditional models in terms of prediction accuracy and response time. Our approach shows remarkable adaptability to sudden changes in traffic patterns, suggesting a scalable solution for real-time traffic management in smart cities.
  arXiv  Detail & Related papers  (2025-01-28T07:24:24Z)
• 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. It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition. The benefits of AI in traffic management are also diverse.
  arXiv  Detail & Related papers  (2024-12-16T18:15:49Z)
• Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion [2.733700237741334]
  This paper explores the use of Reinforcement Learning to enhance traffic signal operations at intersections. We introduce two RL-based algorithms: a turn-based agent, which dynamically prioritizes traffic signals based on real-time queue lengths, and a time-based agent, which adjusts signal phase durations according to traffic conditions. Simulation results demonstrate that both RL algorithms significantly outperform conventional traffic signal control systems.
  arXiv  Detail & Related papers  (2024-08-28T12:35:56Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• An ASP Framework for Efficient Urban Traffic Optimization [0.0]
  This paper presents a framework which allows to efficiently simulate and optimize traffic flow in a large roads' network with hundreds of vehicles. The framework leverages on an Answer Set Programming (ASP) encoding to formally describe the movements of vehicles inside a network. It is then possible to optimize the routes of vehicles inside the network to reduce a range of relevant metrics.
  arXiv  Detail & Related papers  (2022-08-05T10:50:38Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)

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.