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Traffic congestion anomaly detection and prediction using deep learning [article]

Adriana-Simona Mihaita, Haowen Li, Marian-Andrei Rizoiu
2020 arXiv   pre-print
Lastly, we prove that the anomaly adjustment method brings significant improvements to using deep learning in both time and space.  ...  This paper brings two contributions in terms of: 1) applying an outlier detection an anomaly adjustment method based on incoming and historical data streams, and 2) proposing an advanced deep learning  ...  prediction using Deep Learning  ... 
arXiv:2006.13215v1 fatcat:hl6nw5onuffjrntfuhqmajyj5q

Artificial Intelligence-Enabled Traffic Monitoring System

Vishal Mandal, Abdul Rashid Mussah, Peng Jin, Yaw Adu-Gyamfi
2020 Sustainability  
A pixel-level segmentation approach is applied to detect traffic queues and predict severity.  ...  and recurring congestion on roadways.  ...  We broadly discuss some of the related articles focused on congestion prediction, traffic count and anomaly detection.  ... 
doi:10.3390/su12219177 fatcat:ojozrrgecjfgrorwr4try3zpye

Artificial Intelligence Enabled Traffic Monitoring System [article]

Vishal Mandal, Abdul Rashid Mussah, Peng Jin, Yaw Adu-Gyamfi
2020 arXiv   pre-print
A pixel-level segmentation approach is applied to detect traffic queues and predict severity.  ...  and recurring congestion on roadways.  ...  We broadly discuss some of the related articles focused on congestion prediction, traffic count and anomaly detection.  ... 
arXiv:2010.01217v1 fatcat:7t6ldyzgv5exjm4neyo4f2fr5u

Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks

Sanchita Basak, Afiya Ayman, Aron Laszka, Abhishek Dubey, Bruno Leao
2019 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict  ...  We are also able to detect anomalies with high precision and recall, resulting in an AUC of 0.8507 for the precision-recall curve.  ...  ACKNOWLEDGEMENTS This research is funded in part by a grant from Siemens, CT and the following grants from National Science Foundation: 1818901 and 1647015.  ... 
doi:10.36001/phmconf.2019.v11i1.861 fatcat:odmkzwc7qvdnpovww7jetuuqiq

Role of Machine Learning in WSN and VANETs

Maryam Gillani, Hafiz Adnan Niaz, Muhammad Tayyab
2021 International Journal of Electrical and Computer Engineering Research  
This paper provides excellent coverage of state-of-the-art ML applications that are being used in WSN and VANETs with their comparative analysis.  ...  For such dynamicity, Machine learning (ML) approaches are considered favourable.  ...  The fuzzy detection tree model is used for traffic signal optimization that effectively deals with extreme traffic and network scenarios [35] . 8) Deep Learning Deep learning is doing wonders when it  ... 
doi:10.53375/ijecer.2021.24 fatcat:72mtbdb3uvaepj6efg7lfz6x6q

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion [article]

Fangzhou sun and Abhishek Dubey and Jules White
2018 arXiv   pre-print
We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network.  ...  Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games.  ...  ACKNOWLEDGMENTS This work is supported by The National Science Foundation under the award numbers CNS-1528799 and CNS-1647015 and a TIPS grant from Vanderbilt University.  ... 
arXiv:1802.00002v1 fatcat:tc27t3aamjfodhlmqq6ta3dzjy

Artificial Intelligence for SatCom Operations

Miguel Ángel Vázquez, Pol Henarejos, Juan Carlos Gil, Irene Pappalardo, Ana I. Pérez-Neira
2020 Zenodo  
The first use case is the development of machine learning techniques to discover anomaly events in the telemetry data.  ...  We propose the use of autoencoder Deep Neural Networks (DNN-AE) techniques in order to identify the presence of interferences. As a major gain, we expect to reduce the signal-t [...]  ...  We propose a machine learning-based method that predicts network loads and detects congested areas before they actually experience congestion.  ... 
doi:10.5281/zenodo.4429795 fatcat:cnuw3mhbvvc6xigahgg326sdxy

Urban Anomaly Analytics: Description, Detection, and Prediction [article]

Mingyang Zhang, Tong Li, Yue Yu, Yong Li, Pan Hui, Yu Zheng
2020 arXiv   pre-print
Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically.  ...  Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.  ...  In [64] , [68] , the average vehicle speed is used to detect traffic accidents or congestion.  ... 
arXiv:2004.12094v1 fatcat:ixtrfb546nblbjub7jvzqgumrq

A framework for end-to-end deep learning-based anomaly detection in transportation networks

Neema Davis, Gaurav Raina, Krishna Jagannathan
2020 Transportation Research Interdisciplinary Perspectives  
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks.  ...  We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines.  ...  Unsupervised anomaly detection using deep learning has mainly been hybrid in nature. First, the deep neural network learns the complex patterns of the data.  ... 
doi:10.1016/j.trip.2020.100112 fatcat:4fnnhrd23jgfjj477fdmwblfcm

A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos, Dionisis Kandris
2019 Future Internet  
In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications  ...  As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application.  ...  Furthermore, a review of the existing and emerging deep learning frameworks used for road anomalies detection is performed.  ... 
doi:10.3390/fi11040094 fatcat:6xneyx7ynrgn7p2yl5efy76cee

Generative Anomaly Detection for Time Series Datasets [article]

Zhuangwei Kang, Ayan Mukhopadhyay, Aniruddha Gokhale, Shijie Wen, Abhishek Dubey
2022 arXiv   pre-print
Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems.  ...  Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score.  ...  Prediction models can naturally be used as anomaly detector, relying on the fact that normal samples are more predictable than anomalies.  ... 
arXiv:2206.14597v1 fatcat:pcwusjljfrdd5lnyplffejieiy

IEEE Access Special Section Editorial: Artificial Intelligence (AI)-Empowered Intelligent Transportation Systems

Edith Ngai, Chao Chen, Amr M. Tolba, Mohammad S. Obaidat, Fanzhao Wang
2021 IEEE Access  
In the article "nLSALog: An anomaly detection framework for log sequence in security management," by Yang et al., a general anomaly detection framework is proposed.  ...  Current challenges include: how to run computing-intensive applications on vehicles; how to enable real-time feedback between vehicles and the traffic management server based on the current Vehicle-to-Vehicle  ...  CHAO CHEN received the B.Sc. and M.Sc. degrees in control science and control engineering from Northwestern Polytechnical University, Xi'an, China, in 2007 and 2010, respectively, and the dual Ph.D. degree  ... 
doi:10.1109/access.2021.3074996 fatcat:dfyrghfswff6vmdlpa55jxtkjm

A Study on Building a "Real-Time Vehicle Accident and Road Obstacle Notification Model" Using AI CCTV

Chaeyoung Lee, Hyomin Kim, Sejong Oh, Illchul Doo
2021 Applied Sciences  
This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion.  ...  the deep learning object-detection algorithm YOLO (You Only Look Once).  ...  Using object-detection technology, w to solve traffic congestion and traffic accidents.  ... 
doi:10.3390/app11178210 fatcat:b5wtrllzq5g57gwgh7fqvlyzz4

On the Use of AI for Satellite Communications

Miguel Ángel Vázquez, Pol Henarejos, Ana I. Pérez-Neira, Elena Grechi, Andreas Voight, Juan Carlos Gil, Irene Pappalardo, Federico Di Credico, Rocco Michele Lancellotti
2019 Zenodo  
Along with those use cases, we present an initial way of automatizing some of those tasks and we show the key AI tools capable of dealing with those challenges.  ...  This document presents an initial approach to the investigation and development of artificial intelligence (AI) mechanisms in satellite communication (SatCom) systems.  ...  In this work we introduce four use cases; namely anomaly detection in telemetry data, flexible payload optimization, interference detection and classification and beam congestion prediction, which we consider  ... 
doi:10.5281/zenodo.3982669 fatcat:jmwpgyzoereghboy63jk33k3ou

On the Use of AI for Satellite Communications [article]

Miguel Ángel Vázquez, Pol Henarejos, Ana I. Pérez-Neira, Elena Grechi, Andreas Voight, Juan Carlos Gil, Irene Pappalardo, Federico Di Credico, Rocco Michele Lancellotti
2020 arXiv   pre-print
Along with those use cases, we present an initial way of automatizing some of those tasks and we show the key AI tools capable of dealing with those challenges.  ...  This document presents an initial approach to the investigation and development of artificial intelligence (AI) mechanisms in satellite communication (SatCom) systems.  ...  In this work we introduce four use cases; namely anomaly detection in telemetry data, flexible payload optimization, interference detection and classification and beam congestion prediction, which we consider  ... 
arXiv:2007.10110v1 fatcat:nlzpchhwevgq7bdrtajhpxcxsq
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