An IoT-Based System: Big Urban Traffic Data Mining Through Airborne Pollutant Gases Analysis

• URL: http://arxiv.org/abs/2002.06374v1
• Date: Sat, 15 Feb 2020 13:04:37 GMT
• Title: An IoT-Based System: Big Urban Traffic Data Mining Through Airborne Pollutant Gases Analysis
• Authors: Daniel. Firouzimagham, Mohammad. Sabouri, and Fatemeh. Adhami
• Abstract summary: In developing countries such as Iran, the number of vehicles is increasing due to growing population. It is necessary to control traffic congestion by traffic police officers, expand paths efficiently and choose the best way for decreasing the traffic by citizens. Todays, many traffic organization services such as traffic police officer and urban traffic control system use traffic cameras, inductive sensors, satellite images, radar sensors, ultrasonic technology and radio-frequency identification (RFID) for urban traffic diagnosis. Our method suggested in this article detects traffic congestion based on IOT containing a smart system that gives us traffic congestion by calculating the air pollution amount in that
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, in developing countries including Iran, the number of vehicles is increasing due to growing population. This has recently led to waste time getting stuck in traffic, take more time for daily commute, and increase accidents. So it is necessary to control traffic congestion by traffic police officers, expand paths efficiently and choose the best way for decreasing the traffic by citizens. Therefore, it is important to have the knowledge of instant traffic in each lane. Todays, many traffic organization services such as traffic police officer and urban traffic control system use traffic cameras, inductive sensors, satellite images, radar sensors, ultrasonic technology and radio-frequency identification (RFID) for urban traffic diagnosis. But this method has some problems such as inefficiency in heavy traffic influenced by condition of the air and inability to detect parallel traffic. Our method suggested in this article detects traffic congestion based on IOT containing a smart system that gives us traffic congestion by calculating the air pollution amount in that area. According to conducted experiment, the results were satisfied.

Related papers

• Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data [45.13152172664334]
  We design traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles. We leverage real-world trajectory data from the I-24 highway in Tennessee. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves.
  arXiv  Detail & Related papers  (2024-01-18T00:50:41Z)
• AVARS -- Alleviating Unexpected Urban Road Traffic Congestion using UAVs [1.350455609556855]
  We propose a system called "AVARS" that explores the potential of using UAVs to reduce unexpected urban traffic congestion using DRL-based traffic light signal control. Our simulation results show that AVARS can effectively recover the unexpected traffic congestion in Dublin, Ireland, back to its original un-congested level.
  arXiv  Detail & Related papers  (2023-09-10T09:40:20Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• 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)
• 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)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Modeling Traffic Congestion in Developing Countries using Google Maps Data [4.3117073200815375]
  This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We show that even with these simple models, we could predict the traffic congestion ahead of time.
  arXiv  Detail & Related papers  (2020-10-29T13:46:34Z)
• Defining Traffic States using Spatio-temporal Traffic Graphs [9.861775841965386]
  We propose a way to understand the traffic state of smaller spatial regions at intersections using traffic graphs. The way these traffic graphs reveals over time different traffic states - a) a congestion is forming (clumping), the congestion is dispersing (unclumping), or c) the traffic is flowing normally (neutral)
  arXiv  Detail & Related papers  (2020-07-27T17:27:52Z)
• iTV: Inferring Traffic Violation-Prone Locations with Vehicle Trajectories and Road Environment Data [12.715237421592624]
  We propose a framework to infer traffic violation-prone locations in cities based on the largetemporal-scale vehicle trajectory data and environment data. To evaluate the effectiveness of the proposed method, we conduct extensive studies on large-scale, real-world vehicle trajectories collected from two Chinese cities.
  arXiv  Detail & Related papers  (2020-05-09T08:52:43Z)
• HOG, LBP and SVM based Traffic Density Estimation at Intersection [4.199844472131922]
  High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations. Traditional traffic management and control systems fail to tackle this problem. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow.
  arXiv  Detail & Related papers  (2020-05-04T18:08:35Z)

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.