Macrolevel Classification Yard Capacity Modeling

2017 ◽  
Vol 2608 (1) ◽  
pp. 125-133 ◽  
Author(s):  
Licheng Zhang ◽  
Mingzhou Jin ◽  
Zhirui Ye ◽  
Haodong Li ◽  
David B. Clarke ◽  
...  
Keyword(s):  
Dwell Time ◽  
Historical Data ◽  
Freight Transportation ◽  
The United States ◽  
Highway Capacity ◽  
Proposed Model ◽  
Capacity Modeling ◽  
Capacity Data ◽  
Classification Yard ◽  
Rail Yards

Classification yards play a significant role in railroad freight transportation and are often considered bottlenecks for railroad networks. Based on a generic yard simulation model, the model in the presented study fits the Bureau of Public Roads function, which is widely used in highway capacity to represent the volume–dwell time relationship. The proposed analytical model incorporates major features of rail yards, such as the number and capacity of tracks in each area, the number of engines and humps, the humping speed, and the assemble rate. The model is validated by historical data from 16 classification yards of Class I railroads in the United States. The results show that the proposed model can generate precise capacity data of rail yard, as well as the dwell time of rail cars in yards. The dwell time increases sharply when the volume is greater than the capacity of a rail yard. The identified relationship may help a railroad analyze its network at the macro level and therefore improve the systemwide capacity and efficiency.

Modeling Capacity of Through Movement at Signalized Intersection Affected by Short Left-Turn Bay under Different Signal Settings

2021 ◽  
pp. 036119812110064
Author(s):  
Zihang Wei ◽  
Yunlong Zhang ◽  
Xiaoyu Guo ◽  
Xin Zhang
Keyword(s):  
Cycle Length ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Essential Factor ◽  
Highway Capacity ◽  
Left Turn ◽  
Highway Capacity Manual ◽  
Proposed Model ◽  
Vehicle Demand ◽  
The One

Through movement capacity is an essential factor used to reflect intersection performance, especially for signalized intersections, where a large proportion of vehicle demand is making through movements. Generally, left-turn spillback is considered a key contributor to affect through movement capacity, and blockage to the left-turn bay is known to decrease left-turn capacity. Previous studies have focused primarily on estimating the through movement capacity under a lagging protected only left-turn (lagging POLT) signal setting, as a left-turn spillback is more likely to happen under such a condition. However, previous studies contained assumptions (e.g., omit spillback), or were dedicated to one specific signal setting. Therefore, in this study, through movement capacity models based on probabilistic modeling of spillback and blockage scenarios are established under four different signal settings (i.e., leading protected only left-turn [leading POLT], lagging left-turn, protected plus permitted left-turn, and permitted plus protected left-turn). Through microscopic simulations, the proposed models are validated, and compared with existing capacity models and the one in the Highway Capacity Manual (HCM). The results of the comparisons demonstrate that the proposed models achieved significant advantages over all the other models and obtained high accuracies in all signal settings. Each proposed model for a given signal setting maintains consistent accuracy across various left-turn bay lengths. The proposed models of this study have the potential to serve as useful tools, for practicing transportation engineers, when determining the appropriate length of a left-turn bay with the consideration of spillback and blockage, and the adequate cycle length with a given bay length.

An improved grey Markov chain model with ANN error correction and its application in gross domestic product forecasting

2021 ◽  
pp. 1-11
Author(s):  
Yuan Zou ◽  
Daoli Yang ◽  
Yuchen Pan
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Gross Domestic Product ◽  
Error Correction ◽  
Historical Data ◽  
Markov Chain Model ◽  
Back Propagation ◽  
The United States ◽  
Domestic Product ◽  
Chain Model

Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality.

scholarly journals Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry

2020 ◽  
Vol 32 (3) ◽  
pp. 394-400
Author(s):  
Giovani Trevisan ◽  
Leticia C. M. Linhares ◽  
Bret Crim ◽  
Poonam Dubey ◽  
Kent J. Schwartz ◽  
...  
Keyword(s):  
Robust Regression ◽  
Historical Data ◽  
The United States ◽  
Respiratory Syndrome Virus ◽  
Seasonal Patterns ◽  
Test Results ◽  
Swine Industry ◽  
Cyclic Pattern ◽  
Rna Detection ◽  
Virus Rna

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year’s weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015–2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14–25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14–25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

An Empirical Bargaining Model with Left-Digit Bias: A Study on Auto Loan Monthly Payments

2021 ◽  
Author(s):  
Zhenling Jiang
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Large Data ◽  
The United States ◽  
Nash Bargaining ◽  
Bargaining Model ◽  
Data Set ◽  
Proposed Model ◽  
Lower Interest Rate ◽  
The Empirical Analysis

This paper studies price bargaining when both parties have left-digit bias when processing numbers. The empirical analysis focuses on the auto finance market in the United States, using a large data set of 35 million auto loans. Incorporating left-digit bias in bargaining is motivated by several intriguing observations. The scheduled monthly payments of auto loans bunch at both $9- and $0-ending digits, especially over $100 marks. In addition, $9-ending loans carry a higher interest rate, and $0-ending loans have a lower interest rate. We develop a Nash bargaining model that allows for left-digit bias from both consumers and finance managers of auto dealers. Results suggest that both parties are subject to this basic human bias: the perceived difference between $9- and the next $0-ending payments is larger than $1, especially between $99- and $00-ending payments. The proposed model can explain the phenomena of payments bunching and differential interest rates for loans with different ending digits. We use counterfactuals to show a nuanced impact of left-digit bias, which can both increase and decrease the payments. Overall, bias from both sides leads to a $33 increase in average payment per loan compared with a benchmark case with no bias. This paper was accepted by Matthew Shum, marketing.

Juárez, Maximilian, and the French Occupation of Mexico

2021 ◽  
Author(s):  
Michael Hogan
Keyword(s):  
United States ◽  
Historical Data ◽  
The United States ◽  
Working Poor ◽  
The Other ◽  
Reform Movement ◽  
Mexican History ◽  
Basic Facts ◽  
The Republic ◽  
Benito Juarez

A tumultuous period in Mexican history began with the Reform Movement of President Benito Juárez, followed by the French invasion and installation of Maximillian as emperor, the defeat of his troops by the liberal army, and the restoration of the Mexican Republic in 1877. Although most of the basic facts of these events are not in dispute, the narrowness of the lens used to examine them is. Some data have been systematically ignored by national historians, and there are also contradictory interpretations of the published historical data. One common reflection on this period is the depiction of Maximilian as liberal whom some argue contributed in a positive way to Mexico. However, some Mexican scholars dispute this. The other widely held belief is that Benito Juárez can be credited with the restoration of the republic and the betterment of the working poor and indigenous. Although criticism of Juárez is uncommon in official circles, where he is idolized, some Mexican scholars are more skeptical of these claims. The missing or generally ignored data concern the contribution of the United States to the defeat of the French and Austrian armies, which is not mentioned in any survey texts and is minimized in most articles. The fuller inclusion of these data coupled with a closer look at the contributions and failures of both the Maximilian and Juárez regimes provides a clearer picture of the epoch and generates new insights.

scholarly journals A Concise Binomial Model for Nonlinear Creep-Fatigue Crack Growth Behavior at Elevated Temperatures

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 651
Author(s):  
Jianxing Mao ◽  
Zhixing Xiao ◽  
Dianyin Hu ◽  
Xiaojun Guo ◽  
Rongqiao Wang
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Dwell Time ◽  
Elevated Temperatures ◽  
Nonlinear Creep ◽  
Nickel Based Superalloy ◽  
Proposed Model ◽  
Creep Fatigue ◽  
Creep Fatigue Crack

The creep-fatigue crack growth problem remains challenging since materials exhibit different linear and nonlinear behaviors depending on the environmental and loading conditions. In this paper, we systematically carried out a series of creep-fatigue crack growth experiments to evaluate the influence from temperature, stress ratio, and dwell time for the nickel-based superalloy GH4720Li. A transition from coupled fatigue-dominated fracture to creep-dominated fracture was observed with the increase of dwell time at 600 °C, while only the creep-dominated fracture existed at 700 °C, regardless of the dwell time. A concise binomial crack growth model was constructed on the basis of existing phenomenal models, where the linear terms are included to express the behavior under pure creep loading, and the nonlinear terms were introduced to represent the behavior near the fracture toughness and during the creep-fatigue interaction. Through the model implementation and validation of the proposed model, the correlation coefficient is higher than 0.9 on ten out of twelve sets of experimental data, revealing the accuracy of the proposed model. This work contributes to an enrichment of creep-fatigue crack growth data in the typical nickel-based superalloy at elevated temperatures and could be referable in the modeling for damage tolerance assessment of turbine disks.

Enhanced Forecasting Accuracy of Fuzzy Time Series Model Based on Combined Fuzzy C-Mean Clustering with Particle Swam Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 2050017
Author(s):  
Nghiem Van Tinh
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Pso Algorithm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Proposed Model ◽  
Fuzzy C Means Clustering ◽  
Universe Of Discourse

Over the past 25 years, numerous fuzzy time series forecasting models have been proposed to deal the complex and uncertain problems. The main factors that affect the forecasting results of these models are partition universe of discourse, creation of fuzzy relationship groups and defuzzification of forecasting output values. So, this study presents a hybrid fuzzy time series forecasting model combined particle swarm optimization (PSO) and fuzzy C-means clustering (FCM) for solving issues above. The FCM clustering is used to divide the historical data into initial intervals with unequal size. After generating interval, the historical data is fuzzified into fuzzy sets with the aim to serve for establishing fuzzy relationship groups according to chronological order. Then the information obtained from the fuzzy relationship groups can be used to calculate forecasted value based on a new defuzzification technique. In addition, in order to enhance forecasting accuracy, the PSO algorithm is used for finding optimum interval lengths in the universe of discourse. The proposed model is applied to forecast three well-known numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange —TAIFEX data and yearly deaths in car road accidents in Belgium). These datasets are also examined by using some other forecasting models available in the literature. The forecasting results obtained from the proposed model are compared to those produced by the other models. It is observed that the proposed model achieves higher forecasting accuracy than its counterparts for both first—order and high—order fuzzy logical relationship.

scholarly journals A Novel Discrete Grey Model and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Tensile Strength ◽  
Arbitrary Number ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Prediction Performance ◽  
Grey Model ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Future Data

This paper aims to further increase the prediction accuracy of the grey model based on the existing discrete grey model, DGM(1,1). Herein, we begin by studying the connection between forecasts and the first entry of the original series. The results comprehensively show that the forecasts are independent of the first entry in the original series. On this basis, an effective method of inserting an arbitrary number in front of the first item of the original series to extract messages is applied to produce a novel grey model, which is abbreviated as FDGM(1,1) for simplicity. Incidentally, the proposed model can even forecast future data using only three historical data. To demonstrate the effectiveness of the proposed model, two classical examples of the tensile strength and life of the product are employed in this paper. The numerical results indicate that FDGM(1,1) has a better prediction performance than most commonly used grey models.

scholarly journals The Importance of Corporation Law to Civil Society

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Woods Bowman
Keyword(s):  
European Union ◽  
Historical Data ◽  
Important Determinant ◽  
Nonprofit Sector ◽  
The United States ◽  
Regulatory Regime ◽  
Business Activity ◽  
Transactions Costs ◽  
The European Union ◽  
Corporation Law

The thesis of this paper is that transactions costs related to formation of nonprofit corporations are restraining the size of the nonprofit sector in Europe. An unfettered ability to incorporate is an important determinant of nonprofit formation. The point is illustrated with historical data from the United States. Given that an increasing number of nonprofits have transnational goals, the lack of a common regulatory regime for nonprofits in Europe raises transactions costs and stifles development of the nonprofit sector—just as the lack of a common regulatory regime for commerce prior to founding of the European Union had retarded business activity.

scholarly journals A NEW AND ADAPTIVE SECURITY MODEL FOR PUBLIC COMMUNICATION BASED ON CORRELATION OF DATA FRAMES

2019 ◽  
Vol 59 (2) ◽  
pp. 126-133
Author(s):  
Haider Tarish Haider ◽  
Dhiaa Halboot Muhsen ◽  
Haider Ismael Shahadib ◽  
Ong Hang See
Keyword(s):  
Information Technologies ◽  
High Capacity ◽  
Data Communication ◽  
Superior Performance ◽  
Secret Message ◽  
Communication Model ◽  
Security Model ◽  
Secret Key ◽  
Proposed Model ◽  
Capacity Data

Recent developments in communication and information technologies, plus the emerging of the Internet of Things (IoT) and machine to machine (M2M) principles, create the need to protect data from multiple types of attacks. In this paper, a secure and high capacity data communication model is proposed to protect the transmitted data based on identical frames between a secret and cover data. In this model, the cover data does not convey any embedded data (as in normal steganography system) or modify the secret message (as in traditional cryptography techniques). Alternatively, the proposed model sends the positions of the cover frames that are identical with the secret frames to the receiver side in order to recover the secret message. One of the significant advantages of the proposed model is the size of the secret key message which is considerably larger than the cover size, it may be even hundred times larger. Accordingly, the experimental results demonstrate a superior performance in terms of the capacity rate as compared to the traditional steganography techniques. Moreover, it has an advantage in terms of the required bandwidth to send the data or the required memory for saving when compared to the steganography methods, which need a bandwidth or memory up to 3-5 times of the original secret message. Where the length of the secret key (positions of the identical frames) that should be sent to the receiver increases by only 25% from the original secret message. This model is suitable for applications with a high level of security, high capacity rate and less bandwidth of communication or low storage devices.

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