scholarly journals ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146876-146886
Author(s):  
Claudio Bettini ◽  
Gabriele Civitarese ◽  
Davide Giancane ◽  
Riccardo Presotto
Keyword(s):  
Activity Recognition ◽  
Data Driven ◽  
Knowledge Based ◽  
Probabilistic Knowledge ◽  
Hybrid Data

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.

scholarly journals Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study

2020 ◽  
Vol 53 (2) ◽  
pp. 11692-11697
Author(s):  
M. Hotvedt ◽  
B. Grimstad ◽  
L. Imsland
Keyword(s):  
Data Driven ◽  
Flow Meter ◽  
Hybrid Data

Extracting useful signals from flawed sensor data: Developing hybrid data-driven approaches with physical factors

2020 ◽  
Vol 185 ◽  
pp. 116282
Author(s):  
Cheng Yang ◽  
Glen T. Daigger ◽  
Evangelia Belia ◽  
Branko Kerkez
Keyword(s):  
Data Driven ◽  
Sensor Data ◽  
Physical Factors ◽  
Hybrid Data
Sign in / Sign up

Export Citation Format

Share Document