scholarly journals Day-to-Day Traffic Assignment Model considering Information Fusion and Dynamic Route Adjustment Ratio

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Manman Li ◽  
Jian Lu ◽  
Jiahui Sun
Keyword(s):  
Fixed Point ◽  
Traffic Management ◽  
Traffic Assignment ◽  
Influence Factors ◽  
Traffic Information ◽  
Assignment Model ◽  
Double Exponential ◽  
The Difference ◽  
Double Exponential Smoothing ◽  
Dynamic Route

A new day-to-day traffic assignment model is proposed to describe travelers’ day-to-day behavioral changes with advanced traffic information system. In the model, travelers’ perception is updated by a double exponential-smoothing learning process combining experience and traffic information that is explicitly modelled. Route adjustment ratio is dynamically determined by the difference between perceived and expected utilities. Through theoretical analyses, we investigate the existence of its fixed point and the influence factors of uniqueness of the fixed point. An iterative-based algorithm that can solve the fixed point is also given. Numerical experiments are then conducted to investigate effects of several main parameters on its convergence, which provides insights for traffic management. In addition, we compare the system efficiencies under the static route adjustment ratio and dynamic route adjustment ratio and show the application of the model.

scholarly journals A Comparison between Brown’s and Holt’s Double Exponential Smoothing for Forecasting Applied Generation Electrical Energies in Kurdistan Region

2021 ◽  
Vol 5 (2) ◽  
pp. 56-63
Author(s):  
Ameera W. Omer ◽  
Hazhar T. A. Blbas ◽  
Dler H. Kadir
Keyword(s):  
Time Series ◽  
Golden Section ◽  
Evaluation Process ◽  
Electrical Energy ◽  
Exponential Smoothing ◽  
Electricity Production ◽  
Kurdistan Region ◽  
Double Exponential ◽  
The Difference ◽  
Double Exponential Smoothing

The process of producing electricity from sources of energy is known as electricity production. Electric also isn't freely accessible in environment, thus it should be "manufactured" (i.e., converting another kinds of energy to electrical energy) by utilities with in electricity industry (transportation, distributing, and so on).Moreover, the objective of this study is to compared of Brown’s as well as Holt’s Double Exponential Smoothing also build a best forecasting time series model among two smoothing model forecasting, as well as focuses on optimizing characteristics to use the golden section technique.  This exponential smoothing approach has been one of the time series forecasting methods that would be used to forecast (Generation Electrical) with in Kurdistan area. The issue that arises with this technique is determining the appropriate parameters to reduce predict inaccuracy. In addition, Data used in this paper are (Generation Electrical) in Kurdistan region for (132) months from 2010 to 2020. The study revealed that such data is trending modeled, indicating that a double exponential smoothing (DES) approach from Brown & Holt can be used with the (Stratigraphic & Minitab) software. There are the same results but the Result of analysis more depend on the R-program. The difference among the forecast findings acquired with optimum parameters as well as the assaying data was utilized to assess the feasibility of the forecast by completing normality and randomness tests. Ultimately, the outcomes of parameterization show that the optimal value of α that in DES Brown is (0.22) as well as the optimal MAPE is 9.23616 percent, whereas in DES Holt the optimal is (0.95) as well as the optimal β is (0.05) via the optimal MAPE of 8.08586 percent. This MAPE of a DES Brown technique is greater than the MAPE of a DES Holt approach. Feasibility experiments revealed that both approaches are capable of predicting. Depending on the value of MAPE as well as evaluation process, DES Holt's was recognized as the main prediction model.

scholarly journals Efficient Prediction of Network Traffic for Real-Time Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Faisal Iqbal ◽  
Muhammad Zahid ◽  
Durdana Habib ◽  
Lizy Kurian John
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Power Management ◽  
Dynamic Resource Allocation ◽  
Computation Complexity ◽  
Real Time Traffic ◽  
Double Exponential ◽  
Efficient Prediction ◽  
Dynamic Resource ◽  
Double Exponential Smoothing

Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.

On the probabilistic origin of dial's multipath traffic assignment model

1976 ◽  
Vol 10 (5) ◽  
pp. 339-341 ◽  
Author(s):  
Michael Florian ◽  
Bennett Fox
Keyword(s):  
Traffic Assignment ◽  
Assignment Model
Sign in / Sign up

Export Citation Format

Share Document