Modeling Traffic Volume Based on Highway Toll Database Using GM (1,1)

2011 ◽  
Vol 66-68 ◽  
pp. 563-568
Author(s):  
Yi Sheng An ◽  
Hua Cui ◽  
Shan Guan Wei ◽  
Xiang Mo Zhao
Keyword(s):  
Prediction Accuracy ◽  
Influence Factors ◽  
Traffic Volume ◽  
Forecasting Model ◽  
Traffic Data ◽  
Grey Forecasting ◽  
The Poor ◽  
Simulation Results ◽  
Volume Models ◽  
Real Traffic

To circumvent the poor prediction accuracy of traffic volume models available due to the lack of traffic data and inaccurate judgments on the traffic influence factors, in this paper we established a traffic volume prediction model using grey forecasting model GM(1,1) based on the real traffic data from the highway toll database. The GM(1,1) method has advantage of the strong adaptiveness to Complex system, thus getting a great advantage over other methods for modeling such a complex nonlinear traffic volume system with many uncertain influence factors. Simulation results show that our GM(1,1) model has mean relative prediction error of 3.9%, which accomplishes our intended prediction accuracy.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

scholarly journals Foundation Settlement Prediction Based on a Novel NGM Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peng-Yu Chen ◽  
Hong-Ming Yu
Keyword(s):  
Prediction Accuracy ◽  
Forecasting Model ◽  
Foundation Settlement ◽  
Engineering Construction ◽  
Settlement Prediction ◽  
Exponential Law ◽  
Grey Forecasting ◽  
Index Sequence ◽  
Settlement Time ◽  
Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

Improved Grey Forecasting Model for Taiwans Green Accounting

2013 ◽  
Vol 404 ◽  
pp. 398-403 ◽  
Author(s):  
Ching I Lin ◽  
Shin Li Lu ◽  
Shih Hung Tai
Keyword(s):  
Prediction Accuracy ◽  
Economic Indicator ◽  
Percentage Error ◽  
Forecasting Model ◽  
Environmental Costs ◽  
Green Accounting ◽  
Grey Forecasting ◽  
A Value ◽  
Grey Forecasting Model ◽  
The Mean

This paper applies the grey forecasting model to forecast the green accounting of Taiwan from 2002 to 2010. Green accounting is an effective economic indicator of human environmental and natural resources protection. Generally, Green accounting is a type of accounting that attempts to factor environmental costs into the financial results of operations. This paper modifies the original GM(1,1) model to improve prediction accuracy in green accounting and also provide a value reference for government in drafting relevant economic and environmental policies. Empirical study shows that the mean absolute percentage error of RGM(1,1) model is 2.05% lower than GM(1,1) and AGM(1,1), respectively. Results are very encouraging as the RGM(1,1) forecasting model clearly enhances the prediction accuracy.

scholarly journals Experiences with Establishing a Simulation Scenario of the City of Duisburg with Real Traffic Volume

2021 ◽  
Vol 11 (3) ◽  
pp. 1193
Author(s):  
Xiaoyi Ma ◽  
Xiaowei Hu ◽  
Thomas Weber ◽  
Dieter Schramm
Keyword(s):  
Road Network ◽  
Traffic Volume ◽  
Data Conversion ◽  
Traffic Data ◽  
The Road ◽  
Traffic Volumes ◽  
Traffic Scenario ◽  
Simulation Scenario ◽  
Set Up ◽  
Real Traffic

This article presents the experience of building a simulation scenario of the whole city of Duisburg using real traffic data. The establishment of the simulation scenario is based on road network and traffic volume. In most cases, it is hard to collect all data sources with high precision. Moreover, it is time-consuming to set up a realistic traffic scenario. Even with available data, conversion, calibration, and validation all take a large effort. With the increase of the respective simulation area, the difficulty and workload rise. In this study, a simulation scenario of the whole city of Duisburg with the road network area of 232 km2 and Origin/Destination (OD) matrix area over 800 km2 was established in the software package SUMO. Four cases with different networks and traffic volumes were built and compared with real traffic data collected from induction loops. The percentage of simulated traffic volume in real traffic volume range can be up to 72.22%.

scholarly journals Application of Gray B6 P Neural Network in Henan Coal Demand Forecast

2018 ◽  
Vol 175 ◽  
pp. 03015
Author(s):  
GOU Guohua
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Prediction Problem ◽  
Demand Forecast ◽  
Total Consumption ◽  
Simulation Results ◽  
Residual Correction

This paper studied the coal demand in the prediction accuracy problems. The traditional gray GM(1,1)model has the theoretical prediction problem of poor accuracy which leaded to less accurate prediction. A modified gray BP Neural Network forecasting model was used to predict the residual correction. The total consumption of coal as a major factor in variables was selected to construct forecast of coal demand The simulation results show that the proposed algorithm has better prediction accuracy and is an effective demand forecasting algorithm

Novel High-Precision Grey Forecasting Model and its Application of Deformation of Tunnel Surrounding Rock

2011 ◽  
Vol 90-93 ◽  
pp. 2869-2874 ◽  
Author(s):  
Ning Gao ◽  
Xi Min Cui ◽  
Cai Yun Gao
Keyword(s):  
Prediction Accuracy ◽  
Surrounding Rock ◽  
Forecasting Model ◽  
Initial Condition ◽  
Important Work ◽  
Fast Calculation ◽  
Grey Forecasting ◽  
Data Prediction ◽  
Novel Approach ◽  
Optimized Model

Accurately estimating the deformation of tunnel surrounding rock is a very important work for surveyors, and we adopted grey model as a forecasting means because of its fast calculation with as few as four data inputs needed, however, the original GM (1, 1) model is not fit for dynamic and long data prediction. For this purpose, we propose a novel approach to improve prediction accuracy of GM(1,1) model through optimization of the initial condition and adoption the technique of rolling modeling, the new forecasting model termed RnGM(1,1). By using the optimized model to analyze and predict the deformation of tunnel surrounding rock and comparing this optimized model with other models, we finally draw a conclusion that this optimized model is able to improve the precision of prediction and therefore can be applied to deformation data analysis.

Grey Logistic Model to Predict the Effective Irrigation Area in Liaoning Province

2014 ◽  
Vol 641-642 ◽  
pp. 179-182 ◽  
Author(s):  
Jin Liang Chen
Keyword(s):  
Logistic Model ◽  
Structural Adjustment ◽  
Logistic Equation ◽  
Liaoning Province ◽  
Forecasting Model ◽  
Irrigation Area ◽  
Agricultural Resources ◽  
Upper Limit ◽  
Simulation Results ◽  
Good Agreement

Through grey estimation of the parameters of logistic equation, a grey logistic forecasting model is established. The effective irrigation area in Liaoning Province was simulated by the model. The simulation results had good agreement with the available data, with a correlation of 0.95. The effective irrigation area was predicted to be 1.583 million hectares in 2018, very close to the predicted upper limit of 1.588 million hectares. Thus, there is little potential for the development of the effective irrigation area, rendering the structural adjustment of agricultural resources very necessary.

scholarly journals Discussion on the Theoretical Basis for Cross-Over Method Applied to Downhole Wave Velocity Test

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qing Dong ◽  
Zheng-hua Zhou ◽  
Su Jie ◽  
Bing Hao ◽  
Yuan-dong Li
Keyword(s):  
Numerical Simulation ◽  
Numerical Simulations ◽  
Wave Velocity ◽  
Theoretical Basis ◽  
Influence Factors ◽  
P Wave ◽  
Engineering Practice ◽  
S Wave ◽  
Simulation Results ◽  
The Cross

At engineering practice, the theoretical basis for the cross-over method, used to obtain shear wave arrival time in the downhole method of the wave velocity test by surface forward and backward strike, is that the polarity of P-wave keeps the same, while the polarity of S-wave transforms when the direction of strike inverted. However, the characteristics of signals recorded in tests are often found to conflict with this theoretical basis for the cross-over method, namely, the polarity of the P-wave also transforms under the action of surface forward and backward strike. Therefore, 3D finite element numerical simulations were conducted to study the validity of the theoretical basis for the cross-over method. The results show that both shear and compression waves are observed to be in 180° phase difference between horizontal signal traces, consistent with the direction of excitation generated by reversed impulse. Furthermore, numerical simulation results prove to be reliable by the analytic solution; it shows that the theoretical basis for the cross-over method applied to the downhole wave velocity test is improper. In meanwhile, numerical simulations reveal the factors (inclining excitation, geophone deflection, inclination, and background noise) that may cause the polarity of the P-wave not to reverse under surface forward and backward strike. Then, as to reduce the influence factors, we propose a method for the downhole wave velocity test under surface strike, the time difference of arrival is based between source peak and response peak, and numerical simulation results show that the S-wave velocity by this method is close to the theoretical S-wave velocity of soil.

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