scholarly journals CUR decomposition for compression and compressed sensing of large-scale traffic data

2013 ◽  
Author(s):  
Nikola Mitrovic ◽  
Muhammad Tayyab Asif ◽  
Umer Rasheed ◽  
Justin Dauwels ◽  
Patrick Jaillet
Keyword(s):  
Compressed Sensing ◽  
Large Scale ◽  
Traffic Data

scholarly journals Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data

2015 ◽  
Vol 16 (5) ◽  
pp. 2949-2954 ◽  
Author(s):  
Nikola Mitrovic ◽  
Muhammad Tayyab Asif ◽  
Justin Dauwels ◽  
Patrick Jaillet
Keyword(s):  
Compressed Sensing ◽  
Large Scale ◽  
Traffic Data ◽  
Dimensional Models ◽  
Low Dimensional

When Taxi Meets Bus: Night Bus Stop Planning over Large-Scale Traffic Data

2016 ◽  
Author(s):  
Luyan Xiao ◽  
Xiaopeng Fan ◽  
Haixia Mao ◽  
Chengzhong Xu ◽  
Ping Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Traffic Data ◽  
Bus Stop

scholarly journals Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory

2020 ◽  
Vol 7 (4) ◽  
pp. 2205-2218 ◽  
Author(s):  
Chaocan Xiang ◽  
Zhao Zhang ◽  
Yuben Qu ◽  
Dongyu Lu ◽  
Xiaochen Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Data Recovery ◽  
Low Rank ◽  
Traffic Data

Common Data Format Archiving of Large-Scale Intelligent Transportation Systems Data for Efficient Storage, Retrieval, and Portability

2003 ◽  
Vol 1836 (1) ◽  
pp. 111-117
Author(s):  
Taek M. Kwon ◽  
Nirish Dhruv ◽  
Siddharth A. Patwardhan ◽  
Eil Kwon
Keyword(s):  
Sensor Networks ◽  
Large Scale ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Sensor Data ◽  
Traffic Data ◽  
Retrieval Performance ◽  
Science Data ◽  
Data Format ◽  
Data Archiving

Intelligent transportation system (ITS) sensor networks, such as road weather information and traffic sensor networks, typically generate enormous amounts of data. As a result, archiving, retrieval, and exchange of ITS sensor data for planning and performance analysis are becoming increasingly difficult. An efficient ITS archiving system that is compact and exchangeable and allows efficient and fast retrieval of large amounts of data is essential. A proposal is made for a system that can meet the present and future archiving needs of large-scale ITS data. This system is referred to as common data format (CDF) and was developed by the National Space Science Data Center for archiving, exchange, and management of large-scale scientific array data. CDF is an open system that is free and portable and includes self-describing data abstraction. Archiving traffic data by using CDF is demonstrated, and its archival and retrieval performance is presented for the Minnesota Department of Transportation–s 30-s traffic data collected from about 4,000 loop detectors around Twin Cities freeways. For comparison of the archiving performance, the same data were archived by using a commercially available relational database, which was evaluated for its archival and retrieval performance. This result is presented, along with reasons that CDF is a good fit for large-scale ITS data archiving, retrieval, and exchange of data.

Automatic incident classification for large-scale traffic data by adaptive boosting SVM

2018 ◽  
Vol 467 ◽  
pp. 59-73 ◽  
Author(s):  
Li-Li Wang ◽  
Henry Y.T. Ngan ◽  
Nelson H.C. Yung
Keyword(s):  
Large Scale ◽  
Traffic Data ◽  
Adaptive Boosting

scholarly journals A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Liang Fu Lu ◽  
Zheng-Hai Huang ◽  
Mohammed A. Ambusaidi ◽  
Kui-Xiang Gou
Keyword(s):  
Network Traffic ◽  
Large Scale ◽  
Low Rank ◽  
Prototype System ◽  
Traffic Data ◽  
Proximal Gradient Method ◽  
Large Scale Network ◽  
Network Operation ◽  
Efficient Data ◽  
Scale Network

With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.

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