Cumulative Traffic Prediction Method for Long-Term Pavement Performance Models

2002 ◽  
Vol 1816 (1) ◽  
pp. 111-121 ◽  
Author(s):  
Christopher R. Byrum ◽  
Starr D. Kohn
Keyword(s):  
Prediction Method ◽  
Pavement Performance ◽  
Traffic Prediction ◽  
Performance Models

Calibration of Performance Models for Jointed Plain Concrete Pavements Using Long-Term Pavement Performance Database

1998 ◽  
Vol 1629 (1) ◽  
pp. 108-116 ◽  
Author(s):  
Marcelo Bustos ◽  
Hernáan E. De Solminihac ◽  
Michael I. Darter ◽  
Andres Caroca ◽  
Juan Pablo Covarrubias
Keyword(s):  
Plain Concrete ◽  
Pavement Performance ◽  
Concrete Pavements ◽  
Performance Models ◽  
Climatic Regions ◽  
Transverse Cracking ◽  
Global Calibration ◽  
Regional Factors

A methodology for calibrating performance models for jointed plain concrete pavements (JPCP) is presented; it is based on statistical analysis of data from the Long-Term Pavement Performance (LTPP) database. The methodology provides calibration factors to pavements in four climatic regions (dry-freeze, dry-nonfreeze, wet-freeze, and wet-nonfreeze) for the JPCP performance models in HDM-4: joint faulting, transverse cracking, joint spalling, and roughness. The procedure allows calculation of global calibration factors, which does not affect significantly the quality of the prediction compared with the quality achieved through the use of regional factors.

PREDICTING CHARACTERISTICS OF SELF SIMILAR TRAFFIC BY SPLINE-EXTRAPOLATION

2019 ◽  
Author(s):  
Irina Strelkovskay ◽  
Irina Solovskaya ◽  
Anastasija Makoganjuk ◽  
Nikolaj Severin
Keyword(s):  
Quality Characteristics ◽  
Traffic Prediction ◽  
Normative Values ◽  
Spline Functions ◽  
Network Nodes ◽  
Traffic Forecasting ◽  
Network Equipment ◽  
Self Similar ◽  
Accuracy Of Prediction

The problem of forecasting self-similar traffic, which is characterized by a considerable number of ripples and the property of long-term dependence, is considered. It is proposed to use the method of spline extrapolation using linear and cubic splines. The results of self-similar traffic prediction were obtained, which will allow to predict the necessary size of the buffer devices of the network nodes in order to avoid congestion in the network and exceed the normative values ​​of QoS quality characteristics. The solution of the problem of self-similar traffic forecasting obtained with the Simulink software package in Matlab environment is considered. A method of extrapolation based on spline functions is developed. The proposed method has several advantages over the known methods, first of all, it is sufficient ease of implementation, low resource intensity and accuracy of prediction, which can be enhanced by the use of quadratic or cubic interpolation spline functions. Using the method of spline extrapolation, the results of self-similar traffic prediction were obtained, which will allow to predict the required volume of buffer devices, thereby avoiding network congestion and exceeding the normative values ​​of QoS quality characteristics. Given that self-similar traffic is characterized by the presence of "bursts" and a long-term dependence between the moments of receipt of applications in this study, given predetermined data to improve the prediction accuracy, it is possible to use extrapolation based on wavelet functions, the so-called wavelet-extrapolation method. Based on the results of traffic forecasting, taking into account the maximum values ​​of network node traffic, you can give practical guidance on how traffic is redistributed across the network. This will balance the load of network objects and increase the efficiency of network equipment.

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

Use of Long-Term Pavement Performance Data to Develop Traffic Defaults in Support of Mechanistic-Empirical Pavement Design Procedures

2003 ◽  
Vol 1855 (1) ◽  
pp. 176-182 ◽  
Author(s):  
Weng On Tam ◽  
Harold Von Quintus
Keyword(s):  
Pavement Design ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Traffic Data ◽  
Design Procedures ◽  
Load Spectra ◽  
State Highway ◽  
Pavement Structures ◽  
Empirical Design

Traffic data are a key element for the design and analysis of pavement structures. Automatic vehicle-classification and weigh-in-motion (WIM) data are collected by most state highway agencies for various purposes that include pavement design. Equivalent single-axle loads have had widespread use for pavement design. However, procedures being developed under NCHRP require the use of axle-load spectra. The Long-Term Pavement Performance database contains a wealth of traffic data and was selected to develop traffic defaults in support of NCHRP 1-37A as well as other mechanistic-empirical design procedures. Automated vehicle-classification data were used to develop defaults that account for the distribution of truck volumes by class. Analyses also were conducted to determine direction and lane-distribution factors. WIM data were used to develop defaults to account for the axle-weight distributions and number of axles per vehicle for each truck type. The results of these analyses led to the establishment of traffic defaults for use in mechanistic-empirical design procedures.

Evaluation of Leakage Probability of Non-Asbestos Fiber Sheet Gasket at Elevated Temperature Based on Percolation Theory

2010 ◽  
Author(s):  
Masahiro Hagihara ◽  
Hirokazu Tsuji ◽  
Atsushi Yamaguchi
Keyword(s):  
High Temperature ◽  
Percolation Theory ◽  
Life Prediction ◽  
Prediction Method ◽  
Acoustic Tomography ◽  
Leakage Path ◽  
Temperature Environment ◽  
Fiber Sheet ◽  
Bolted Flange

A long-term life prediction method for a compressed fiber sheet gasket under a high-temperature environment is studied. Non-asbestos compressed fiber sheet gaskets are now being used as a substitute for asbestos in the bolted flange joint, for instance petrochemical factories. Consequently, there is a real need for a technology to predict the lifetime of non-asbestos compressed fiber sheet gaskets quantitatively. In this report, the facing surface of the gasket and flange is visualized with scanning acoustic tomography (SAT). Voids were observed on the facing surface of the gasket and increased with the increase in exposure time at high temperature. If a leakage path for inner fluids is created by the increasing number of voids, the leak occurs on the facing surface of the gasket. The probability of a leak due to voids and the lifetime of this gasket are predicted by applying the percolation theory, which describes the connectedness of clusters.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

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