scholarly journals Data-driven Modeling of Transportation Systems and Traffic Data Analysis During a Major Power Outage in the Netherlands

2015 ◽  
Vol 66 ◽  
pp. 336-345 ◽  
Author(s):  
Valentin R. Melnikov ◽  
Valeria V. Krzhizhanovskaya ◽  
Alexander V. Boukhanovsky ◽  
Peter M.A. Sloot
Keyword(s):  
Data Analysis ◽  
The Netherlands ◽  
Transportation Systems ◽  
Data Driven ◽  
Traffic Data ◽  
Major Power ◽  
Power Outage ◽  
Data Driven Modeling

scholarly journals A Cyber-ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yingjie Xia ◽  
Jia Hu ◽  
Michael D. Fontaine
Keyword(s):  
Parallel Computing ◽  
Data Analysis ◽  
State Estimation ◽  
Data Representation ◽  
Heterogeneous Traffic ◽  
Data Driven ◽  
Traffic Data ◽  
Traffic State ◽  
Traffic State Estimation ◽  
Cyber Infrastructure

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

scholarly journals Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

Computation ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 46 ◽  
Author(s):  
David Sadowsky ◽  
Andrew Abboud ◽  
Anthony Cyr ◽  
Lena Vodovotz ◽  
Paulo Fontes ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Data Analysis ◽  
Ex Vivo ◽  
Data Driven ◽  
Dynamic Data ◽  
Data Driven Modeling

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

scholarly journals Data-driven deep density estimation

2021 ◽  
Author(s):  
Patrik Puchert ◽  
Pedro Hermosilla ◽  
Tobias Ritschel ◽  
Timo Ropinski
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Population Data ◽  
Training Data ◽  
Data Driven ◽  
Discrete Observations ◽  
Efficient Manner ◽  
Continuous Models ◽  
3D Scans ◽  
Spatial Locations

AbstractDensity estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

Hybrid-Data Approach for Estimating Trip Purposes

2021 ◽  
pp. 036119812110184
Author(s):  
Xiaoling Luo ◽  
Adrian Cottam ◽  
Yao-Jan Wu ◽  
Yangsheng Jiang
Keyword(s):  
Transportation Systems ◽  
Data Driven ◽  
Trip Purpose ◽  
Additional Information ◽  
Point Of Interest ◽  
Average Accuracy ◽  
Hybrid Data ◽  
Added Benefit ◽  
Data Source ◽  
High Level

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

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