Physiological and GPS data fusion

2015 ◽  
Author(s):  
H. Charvatova ◽  
A. Prochazka ◽  
S. Vaseghi ◽  
O. Vysata ◽  
D. Janacova ◽  
...  
Keyword(s):  
Data Fusion ◽  
Gps Data

scholarly journals Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

2019 ◽  
Vol 9 (17) ◽  
pp. 3597 ◽  
Author(s):  
Zilin Huang ◽  
Lunhui Xu ◽  
Yongjie Lin ◽  
Pan Wu ◽  
Bin Feng
Keyword(s):  
Data Fusion ◽  
Smart Card ◽  
Smart Cards ◽  
P Value ◽  
Gps Data ◽  
Fusion Method ◽  
Transfer Time ◽  
Time Threshold ◽  
Smart Card Data ◽  
Combined Data

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

scholarly journals Applied Research on InSAR and GPS Data Fusion in Deformation Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ziwen Zhang ◽  
Xuelian Wang ◽  
Yongdong Wu ◽  
Zengpeng Zhao ◽  
Yang E
Keyword(s):  
Data Fusion ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Inverse Distance Weighting ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Gps Data ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Aperture Radar

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

Data Fusion of Commercial Vehicle GPS and Roadside Intercept Survey Data

2018 ◽  
Vol 2672 (44) ◽  
pp. 10-20
Author(s):  
Sirui Zhu ◽  
Glareh Amirjamshidi ◽  
Matthew J. Roorda
Keyword(s):  
Data Fusion ◽  
Survey Data ◽  
Commercial Vehicle ◽  
Synthetic Data ◽  
Gps Tracking ◽  
Gps Data ◽  
Large Sample Size ◽  
Tracking Data ◽  
Data Set ◽  
Intercept Survey

GPS tracking technology produces large amounts of data which represent samples of the commercial vehicle population that are much larger than conventional commercial travel surveys. However, passively collected GPS data lack behavioral detail that a conventional survey offers. This study develops a data fusion method to impute variables of interest for a large GPS data set, by establishing a link to a behaviorally rich commercial travel survey data set. As a case study, this study uses detailed information from the Ministry of Transportation of Ontario’s Commercial Vehicle Survey (CVS), a truck intercept survey conducted in 2012, to enrich a GPS commercial vehicle tracking data set from Xata Turnpike Inc. The enrichment process has three parts: converting raw GPS tracking data into GPS trips, matching CVS trips to GPS trips, and imputing the missing variables for GPS trips. Evaluation of the outcomes concludes that imputation methods can produce a synthetic data set with large sample size (from GPS data) and rich information (from roadside interview data) with good accuracy.

scholarly journals Big Data Fusion to Estimate Urban Fuel Consumption: A Case Study of Riyadh

2018 ◽  
Vol 2672 (24) ◽  
pp. 49-59
Author(s):  
Adham Kalila ◽  
Zeyad Awwad ◽  
Riccardo Di Clemente ◽  
Marta C. González
Keyword(s):  
Big Data ◽  
Data Fusion ◽  
Fuel Consumption ◽  
Gps Data ◽  
Data Sets ◽  
Rapid Urbanization ◽  
Ubiquitous Sensing ◽  
Environmental Goals ◽  
Oil Revenues ◽  
The Impact

Falling oil revenues and rapid urbanization are putting a strain on the budgets of oil-producing nations, which often subsidize domestic fuel consumption. A direct way to decrease the impact of subsidies is to reduce fuel consumption by reducing congestion and car trips. As fuel consumption models have started to incorporate data sources from ubiquitous sensing devices, the opportunity is to develop comprehensive models at urban scale leveraging sources such as Global Positioning System (GPS) data and Call Detail Records. This paper combines these big data sets in a novel method to model fuel consumption within a city and estimate how it may change in different scenarios. To do so a fuel consumption model was calibrated for use on any car fleet fuel economy distribution and applied in Riyadh, Saudi Arabia. The model proposed, based on speed profiles, was then used to test the effects on fuel consumption of reducing flow, both randomly and by targeting the most fuel-inefficient trips in the city. The estimates considerably improve baseline methods based on average speeds, showing the benefits of the information added by the GPS data fusion. The presented method can be adapted to also measure emissions. The results constitute a clear application of data analysis tools to help decision makers compare policies aimed at achieving economic and environmental goals.

Sign in / Sign up

Export Citation Format

Share Document