Prediction of Traffic Volume in Highway Tunnel Group Region Based on Grey Markov Model

2013 ◽  
Vol 712-715 ◽  
pp. 2981-2985 ◽  
Author(s):  
Wei Zhan ◽  
Qing Lu ◽  
Yue Quan Shang
Keyword(s):  
Markov Model ◽  
Early Warning ◽  
Prediction Accuracy ◽  
Development Trend ◽  
Traffic Volume ◽  
Grey Model ◽  
Prediction Methods ◽  
Highway Tunnel ◽  
The Development Trend ◽  
Highway Management

Based on the investigation and analysis of the traffic volume in highway tunnel group region, the development trend of traffic volume is analyzed by Grey model. Then the prediction accuracy is improved by Markov optimization. The method in this paper has a better prediction accuracy and practicality in a period than other common prediction methods. It can be used for the prediction analysis of traffic volume and for early warning by highway management.

scholarly journals Using weighted Markov SCGM(1,1)c model to forecast gold/oil ,DJIA/gold and USD/XAU ratios

2017 ◽  
Vol 12 (4) ◽  
Author(s):  
Hamidreza Mostafaei ◽  
Shaghayegh Kordnoori ◽  
Shirin Kordnoori
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Transition Probability ◽  
Single Gene ◽  
Development Trend ◽  
Transition Probability Matrix ◽  
Grey Model ◽  
Autocorrelation Coefficient ◽  
Dynamic Information ◽  
The Development Trend

Grey model can be counted as a potent approximation for extracting system dynamic information with only small amount of data. A weighted Markov model is appropriate for predicting the stochastic fluctuating dynamic by a transition probability matrix and normalizing autocorrelation coefficient as weighted and a single gene system cloud grey SCGM(1,1)c  model. It is applied to regulate the development trend of time series. In this paper we employed a weighted Markov SCGM(1,1)c model for predicting the Gold/Oil ,DJIA/Gold and USD/XAU ratios. By examining the forecasted results, it was concluded that the weighted Markov SCGM(1,1)c model is a reliable and effective modeling method.

scholarly journals Predication of life cycle cost of equipment base on unbiased grey Markov models

2020 ◽  
Vol 309 ◽  
pp. 05005
Author(s):  
Yonghong Chen ◽  
Ping Hu ◽  
Dong Zhang
Keyword(s):  
Life Cycle ◽  
Markov Model ◽  
Life Cycle Cost ◽  
Prediction Accuracy ◽  
Markov Models ◽  
Grey Model ◽  
Model Accuracy ◽  
Integrated Logistics ◽  
And Control ◽  
Whole Life Cycle

Life cycle cost(LCC) is an important content of equipment integrated logistics support. While the LCC includes the whole life cycle of equipment from development, production, service and maintenance to retirement, in order to effectively manage and control the LCC and better develop integrated logistics support, it is necessary to analyze and predict it. The unbiased grey markov model(UGMM) was introduced into the LCC prediction in the paper, in order to check model accuracy, the posterior difference method(PDM) was used, also the influence by the number of state intervals in UGMM on the prediction accuracy is analyzed and studied. The result indicate that UGMM can be used to predict the LCC, also have the highest prediction accuracy comparing with unbiased grey model and grey separating model, and in order to ensure the prediction accuracy, the state interval should be divided according to the number of sequence.

Application of GM(1,1) Model Established by Moving Average Method in Stress Prediction of Rigid Frame Bridge Construction

2014 ◽  
Vol 644-650 ◽  
pp. 5050-5053
Author(s):  
Tian Zhi Hao ◽  
Hong Qiang He
Keyword(s):  
Average Method ◽  
Prediction Accuracy ◽  
Moving Average ◽  
Development Trend ◽  
Bridge Construction ◽  
Stress Variation ◽  
Rigid Frame ◽  
Stress Prediction ◽  
Rigid Frame Bridge ◽  
The Development Trend

Stress variation is complicate during the course of rigid frame bridge construction, it is difficult to grasp the development trend of stress because of too many influencing factors. When using GM(1,1) model solely, the prediction accuracy is low and it is critical to collect much raw data of stress. Method of moving average improves GM(1,1) model by processing data before modeling. The paper takes Hu Lu-ding Bridge as an example and the results show that the GM(1,1) model established by moving-average method has higher prediction accuracy and can be efficiently used in stress prediction of rigid frame bridge.

scholarly journals A research on early warning method of Distribution Network Cyber Physical System

2021 ◽  
Vol 248 ◽  
pp. 02054
Author(s):  
Feng Chen ◽  
Wei Wei Xu ◽  
Zong Heng Wang ◽  
Tao Yang ◽  
Hong Yang Huang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Early Warning ◽  
Physical System ◽  
Hidden Markov ◽  
Distribution Network ◽  
Development Trend ◽  
Cyber Physical System ◽  
Stable Operation ◽  
Time Accuracy

The high integration of cyber and physics is the development trend of intelligent distribution network in the future, but the cyber system not only supports the stable operation of the physical system, also brings some security risks to the cyber physical system of distribution network. Aiming at the requirements of real-time, accuracy, efficiency and other characteristics of distribution network monitoring, this paper proposes an early warning method of distribution network cyber physical system based on Hidden Markov model. Firstly, the online monitoring and early warning system architecture of distribution network information physical system is proposed, and then the early warning method of distribution network cyber physical system based on Hidden Markov model is established. Finally, an example is given to verify that the proposed strategy can accurately and efficiently early warn the fault.

Forecast of Railway Freight Ton-Kilometers Based on the UBGPM-Markov Model

2014 ◽  
Vol 1030-1032 ◽  
pp. 2199-2202
Author(s):  
Hong Bing Lu ◽  
Rui Song
Keyword(s):  
Markov Model ◽  
Development Trend ◽  
Power Model ◽  
Railway Freight ◽  
Forecast Precision ◽  
Markov Chain Method ◽  
Scope Of Application ◽  
The Mean ◽  
Random Fluctuations ◽  
The Development Trend

To raise the forecast precision of railway freight ton-kilometers, the unbiased GM (1, 1) power model was applied to predict the development trend. The Markov chain method was used to process the random fluctuations and correct the forecast values. Thus the optimized UBGPM-Markov model was established. The example analysis shows that the unbiased GM (1, 1) power model is superior to the GM (1, 1) model in both scope of application and prediction accuracy. Furthermore, the UBGPM-Markov model has reduced the mean absolute prediction error (MAPE) from 1.72% to 0.70%.

scholarly journals Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

2020 ◽  
Vol 12 (1) ◽  
pp. 626-636
Author(s):  
Wang Song ◽  
Zhao Yunlin ◽  
Xu Zhenggang ◽  
Yang Guiyan ◽  
Huang Tian ◽  
...  
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Markov Model ◽  
Prediction Accuracy ◽  
Driving Mechanism ◽  
Economic Factors ◽  
Chain Model ◽  
The Impact ◽  
County Scale

AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

scholarly journals The development trend of AC-style PDPs

1998 ◽  
Vol 82 (Appendix) ◽  
pp. 311-311
Author(s):  
Tetsuji Okajima ◽  
Chizuka Tani
Keyword(s):  
Development Trend ◽  
The Development Trend
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