Buffer-Aware Analysis for Worst-Case Traversal Time of Real-Time Traffic over RRA-based NoCs

2017 ◽  
Author(s):  
Meng Liu ◽  
Matthias Becker ◽  
Moris Behnam ◽  
Thomas Nolte
Keyword(s):  
Real Time ◽  
Worst Case ◽  
Traversal Time ◽  
Real Time Traffic

New Scheduling Algorithm Proposed for Real Time Traffic Over LTE Femto-Cell

2015 ◽  
Vol 5 (6) ◽  
pp. 383
Author(s):  
Mohammed Mahfoudi ◽  
Moulhime El Bekkali ◽  
Abdellah Najid ◽  
Mohamed El Ghazi ◽  
Said Mazer
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Real Time Traffic

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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