Detection of clusters in traffic networks based on spatio‐temporal flow modeling

2019 ◽  
Vol 23 (2) ◽  
pp. 312-333 ◽  
Author(s):  
Yan Shi ◽  
Min Deng ◽  
Jianya Gong ◽  
Chang‐Tien Lu ◽  
Xuexi Yang ◽  
...  
Keyword(s):  
Traffic Networks ◽  
Flow Modeling ◽  
Spatio Temporal ◽  
Temporal Flow

scholarly journals Dynamic spatio‐temporal flow modeling with raster DEMs

2021 ◽  
Author(s):  
Hampus Nilsson ◽  
Petter Pilesjö ◽  
Abdulghani Hasan ◽  
Andreas Persson
Keyword(s):  
Flow Modeling ◽  
Spatio Temporal ◽  
Temporal Flow

scholarly journals A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

2019 ◽  
Vol 34 (4) ◽  
pp. 802-823 ◽  
Author(s):  
Yibin Ren ◽  
Huanfa Chen ◽  
Yong Han ◽  
Tao Cheng ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Spatio Temporal ◽  
Temporal Flow ◽  
Deep Learning Model

scholarly journals AUTOMATIC SPATIO-TEMPORAL FLOW VELOCITY MEASUREMENT IN SMALL RIVERS USING THERMAL IMAGE SEQUENCES

2018 ◽  
Vol IV-2 ◽  
pp. 201-208 ◽  
Author(s):  
D. Lin ◽  
A. Eltner ◽  
H. Sardemann ◽  
H.-G. Maas
Keyword(s):  
Flow Velocity ◽  
Velocity Measurement ◽  
Feature Tracking ◽  
Image Sequences ◽  
Small Rivers ◽  
Thermal Camera ◽  
Flow Velocity Measurement ◽  
Spatio Temporal ◽  
Temporal Flow ◽  
Thermal Feature

An automatic spatio-temporal flow velocity measurement approach, using an uncooled thermal camera, is proposed in this paper. The basic principle of the method is to track visible thermal features at the water surface in thermal camera image sequences. Radiometric and geometric calibrations are firstly implemented to remove vignetting effects in thermal imagery and to get the interior orientation parameters of the camera. An object-based unsupervised classification approach is then applied to detect the interest regions for data referencing and thermal feature tracking. Subsequently, GCPs are extracted to orient the river image sequences and local hot points are identified as tracking features. Afterwards, accurate dense tracking outputs are obtained using pyramidal Lucas-Kanade method. To validate the accuracy potential of the method, measurements obtained from thermal feature tracking are compared with reference measurements taken by a propeller gauge. Results show a great potential of automatic flow velocity measurement in small rivers using imagery from a thermal camera.

Spatio-temporal Flow Analysis in Bileaflet Heart Valve Hinge Regions: Potential Analysis for Blood Element Damage

2007 ◽  
Vol 35 (8) ◽  
pp. 1333-1346 ◽  
Author(s):  
Hélène A. Simon ◽  
Lakshmi P. Dasi ◽  
Hwa-Liang Leo ◽  
Ajit P. Yoganathan
Keyword(s):  
Heart Valve ◽  
Flow Analysis ◽  
Potential Analysis ◽  
Spatio Temporal ◽  
Temporal Flow
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