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Automated Traffic Control System using Big Data and Cognitive Analysis
2016
International Journal of Computer Applications
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Using combination of computer vision, big data and machine learning it is possible to design a reliable and scalable system which will help to resolve these traditional problems. ...
Today people spend about 4.8 billion hours every year in congestion which could be used productively. The traffic control system currently being used is outdated and heavily dependent on humans. ...
INTRODUCTION Traffic congestion is a major problem. An efficient decision making can play a pivotal role in solving this problem. ...
doi:10.5120/ijca2016911924
fatcat:7dghrp73xzbrzb4lml4h6msj6q
Application of Reinforcement Learning as a Tool of Adaptive Traffic Signal Control on Isolated Intersections
2012
International Journal of Engineering and Technology
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These systems are characterized with the ability to accumulate and use knowledge, set a problem, learn, process, conclude, solve the problem and exchange knowledge. ...
First, the problem of reinforcement learning has been set. The first computation results of the Q-learning application for adaptive traffic signal control are presented. ...
These systems are characterized with the ability to accumulate and use knowledge, set a problem, learn, process, conclude, solve the problem, and exchange knowledge. ...
doi:10.7763/ijet.2012.v4.332
fatcat:fxiw5p5eibb3fa7smy2mckyxh4
Traffic Flow Prediction Using Machine Learning Algorithms
2022
Zenodo
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Traffic control is the biggest problem and challenge in all over the world, in this project we tried to solve the problem with the help of machine learning algorithm to deal with traffic challenges.in ...
this project we have used reinforcement learning for controlling traffic light and we have used artificial environment for simulation purpose which is SUMO, in we can see the vehicle in action and outcome ...
To ensure our traffic control system can solve the real-world problem we operated to present traffic data to alter traffic lights accordingly using the existing Deep Reinforcement Learning Technique integrating ...
doi:10.5281/zenodo.6510586
fatcat:onh6cdy2kzgw5bm7xrinxkylxu
Is Machine Learning Suitable for Solving RWA Problems in Optical Networks?
2018
2018 European Conference on Optical Communication (ECOC)
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We show that classical supervised Machine Learning techniques, after trained with a large number of optimal RWA configurations solved via ILP, can rapidly procure the most appropriate RWA configuration ...
to be applied for a new traffic matrix. ...
interest in applications of Machine Learning (ML) tools to solve typical optical networking problems in the recent years. ...
doi:10.1109/ecoc.2018.8535562
fatcat:bk7jp7u2jfbjbbibtnzhhmvf7m
A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer
[article]
2020
arXiv
pre-print
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First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand ...
This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering ...
In [44, 47, 123] SVMs are used as the machine learning algorithm to classify signals among a given set of possible modulation schemes. For instance, Huang et al. ...
arXiv:2001.04561v2
fatcat:kbbvgechmjgwla6noolrf6ds7u
Collaborative Decision Making in IoT Network for Sustainable Smart Cities: An Artificial Intelligence Planning Method Based Solution
2021
International Journal of Advanced Trends in Computer Science and Engineering
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A usecase of adaptive traffic signal explains the working of the system and also proves its feasibility. ...
However, the focus of ongoing research is limited to utilizing machine learning paradigm. Artificial intelligence is a vast field consisting of many diverse methods and techniques. ...
USECASE: ADAPTIVE TRAFFIC SIGNAL The AI Planning based system is used to construct adaptive traffic signals as an usecase. ...
doi:10.30534/ijatcse/2021/041042021
fatcat:hxbnzhejkbbltofjmqj2z2digm
Machine Learning and Answer Set Program Rules towards Traffic Light Management
2020
International Journal of Advanced Trends in Computer Science and Engineering
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The proposed approach starts with extracting rules from the data set using Weka. ...
To achieve this there are many approaches and one of them, is developing an adaptive traffic light signal in order to tackle this problem. ...
In machine learning, TN is not used. 1 = 2( . ) ( + ) (5
6.ANSWER SET PROGRAM The values of attributes are changed from time to time. ...
doi:10.30534/ijatcse/2020/08932020
fatcat:en6ses7qibd53clirf3p4asqsi
A Traffic Signal Recognition Algorithm Based on Self-paced Learning and Deep Learning
2020
Ingénierie des Systèmes d'Information
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To solve the problem, this paper introduces self-paced learning (SPL) to the image recognition of traffic signs. Based on complexity, the SPL automatically classifies samples into multiple sets. ...
Traffic signal recognition is a critical function of the intelligent vehicle system (IVS). Many algorithms can achieve a high accuracy in traffic signal recognition. ...
Since the 1990s, many machine learning (ML) algorithms have been applied to realize traffic signal recognition of the IVS, namely, principal component analysis (PCA) [1] , random forest (RF) [2] , support ...
doi:10.18280/isi.250211
fatcat:3iuscxdtvrh7dg5nf35llojyke
DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS
2021
Chronos Journal
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Real-time adaptive traffic control is an important problem in modern world. Historically, various optimization methods have been used to build adaptive traffic signal control systems. ...
Recently, reinforcement learning has been advanced, and various papers showed efficiency of Deep-Q-Learning (DQN) in solving traffic control problems and providing real-time adaptive control for traffic ...
Dynamic programming is a mathematical optimization method, which is essentially an approach to solving an optimization problem by dividing it into a set of sub-problems and solving them separately, obtaining ...
doi:10.52013/2658-7556-57-7-6
fatcat:75f3xef4gnb7xoakoobd4lvtm4
A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer
2021
Electronics
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First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand ...
This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering ...
In [44, 47, 121] SVMs are used as the machine learning algorithm to classify signals among a given set of possible modulation schemes. For instance, Huang et al. ...
doi:10.3390/electronics10030318
fatcat:p6jslz26dvfvbpnqzmrpptloim
Applications Of Artificial Intelligence In Human Life
2018
Zenodo
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Also, it will show artificial intelligence principle and how they were applying in traffic signal control, how they solve some traffic problem in actual. ...
AI and its Applications gets used in various fields of life of humans as expert system solve the complex problems in various areas as science, engineering, business, medicine, video games and Advertising ...
of transformation rules which can be used to predict the behavior and relationship between some set of real-world objects or entities. • The Representation Problem for Problem Solving Systems
Robots ...
doi:10.5281/zenodo.1302458
fatcat:wkipxybsvbempj7inldxyfe5pa
A Traffic Classification Method Based on Wavelet Spectrum of Scatter Factor and Improved K-means
2017
DEStech Transactions on Engineering and Technology Research
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machine learning by combining the advantage of wavelet transform in solving multi-fractal network traffic and proposes a traffic identification method based on wavelet spectrum of scatter factor and improved ...
Based on the problem that supervised machine learning requires labeled samples and fails to identify unknown traffic, the author innovatively integrates wavelet transform and K-means algorithm of unsupervised ...
With respect to the above-mentioned problems of supervised machine learning, the author combines the advantage of wavelet transform in solving multi-fractal network traffic, innovatively integrates wavelet ...
doi:10.12783/dtetr/iceta2016/7015
fatcat:zyypmoonmzczlfzlaa7yl36vta
A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control
2020
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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An intelligent transport system to use the machine learning methods likes reinforcement learning and to explain the acknowledged transportation approaches and a list of recent literature in traffic signal ...
A Traffic signal control is a challenging problem and to minimize the travel time of vehicles by coordinating their movements at the road intersections. ...
For the 'brain' part, reinforcement learning, as a type of machine learning techniques, is a promising way to solve the problem. ...
doi:10.32628/cseit206458
fatcat:iyp2r73tqjdq5pqpvxv77mrdra
Model controlled prediction: A reciprocal alternative of model predictive control
2022
IEEE/CAA Journal of Automatica Sinica
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Existing state-of-art research is to transform the traffic prediction problem into a regression problem in machine learning. ...
Since the features in both the training and test sets are in the same m -dimensional input space, the traffic patterns learned by the model in the training set can be used for prediction in the test set ...
doi:10.1109/jas.2022.105611
fatcat:jafnio2tsneprnaf7rgnkbtqnm
Traffic Signal Settings Optimization Using Gradient Descent
2018
Schedae Informaticae
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We investigate performance of a gradient descent optimization (GR) applied to the traffic signal setting problem and compare it to genetic algorithms. ...
We used neural networks as metamodels evaluating quality of signal settings and discovered that both optimization methods produce similar results, e.g., in both cases the accuracy of neural networks close ...
the traffic signal setting problem and similar combinatorial optimization problems with surrogate models. ...
doi:10.4467/20838476si.18.002.10407
fatcat:tnjpdupqynhgfcfbkz4uozfxfa
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