Congestion Classification Research Articles

A Solution for Detection and Control For Congested Roads Using Vehicular Networks

Traffic congestion is an urban mobility problem, which generates stress to drivers as well as economic losses. Intelligent transportation systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this work proposes a solution for the detection and control of congested roads based on inter-vehicle communication. The main goal of the proposed solution is to decrease CO2 emissions, the average trip time, and fuel consumption by avoiding congested roads. Simulation results show that the proposed solution yields (i) a high acceptance rate in the classification of congestion levels., (ii) reduction in average trip time, (iii) reduction in fuel consumption, and (iv) reduced CO2 emissions. In particular, the trip time was decreased approximately 70%, the fuel consumption 46%, and the CO2 emission 58%.

Dynamic changes in bioelectrical impedance vector analysis and phase angle in acute decompensated heart failure

ObjectivesTo evaluate whether changes in hydration status (reflecting fluid retention) would be detected by bioelectrical impedance vector analysis (BIVA) and phase angle during hospitalization for acute decompensated heart failure (ADHF) and after clinical stabilization. MethodsPatients admitted to ADHF were evaluated at admission, discharge and after clinical stabilization (3 mo after discharge) for dyspnea, weight, brain natriuretic peptide, bioelectrical impedance resistance, reactance, and phase angle. Generalized estimating equations and chi-square detected variations among the three time points of evaluation. ResultsWere included 57 patients: Mean age was 61 ± 13 y, 65% were male, LVEF was 25 ± 8%. During hospitalization there were improvements in clinical parameters and increase in resistance/height (from 250 ± 72 to 302 ± 59 Ohms/m, P < 0.001), reactance/height (from 24 ± 10 to 31 ± 9 Ohms/m, P < 0.001), and phase angle (from 5.3 ± 1.6 to 6 ± 1.6°, P = 0.007). From discharge to chronic stability, both clinical and BIVA parameters remained stable. At admission, 61% of patients had significant congestion by BIVA, and they lost more weight and had higher improvement in dyspnea during hospitalization (P < 0.05). At discharge, more patients were in the upper half of the graph (characterizing some degree of dehydration) while at chronic stability normal hydration status was more prevalent (P < 0.001). ConclusionsBIVA and phase angle were able to detect significant changes in hydration status during ADHF, which paralleled the clinical course of recompensation, both acutely and chronically. The classification of congestion by BIVA at admission identified patients with more pronounced changes in weight and dyspnea during compensation.

Multi-Lane Detection and Road Traffic Congestion Classification for Intelligent Transportation System

Multi-Lane Detection and Road Traffic Congestion Classification for Intelligent Transportation System