scholarly journals Testing the Generality of a Passenger Disregarded Train Dwell Time Estimation Model at Short Stops: Both Comparison and Theoretical Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Dewei Li ◽  
Yonghao Yin ◽  
Hong He
Keyword(s):  
Dwell Time ◽  
Time Estimation ◽  
Work Conditions ◽  
Critical Issue ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Estimation Model ◽  
Railway Stations ◽  
Theoretical Approaches ◽  
Improved Model

Train dwell time estimation is a critical issue in both scheduling and rescheduling phases. In a previous paper, the authors proposed a novel dwell time estimation model at short stops which did not require the passenger data. This model shows promising results when applied to Dutch railway stations. This paper focuses on testing and improving the generality of the model by two steps: first, the model is tested by applying more independent datasets from another city and comparing the estimation accuracy with the previous Dutch case; second, the model’s generality is tested by a theoretical approach through the analysis of individual model parameters, variables, model scenarios, and model structure as well as work conditions. The validation results during peak hours show that the MAPE of the model is 11.4%, which is slightly better than the results for the Dutch railway stations. A more generalized predictor called “dwell time at the associated station” is used to replace the square root term in the original model. The improved model can estimate train dwell time in all the investigated stations during both peak and off-peak periods. We conclude that the proposed train dwell time estimation model is generic in the given condition.

scholarly journals Estimation of Evapotranspiration in Sparse Vegetation Areas by Applying an Optimized Two-Source Model

2021 ◽  
Vol 13 (7) ◽  
pp. 1344
Author(s):  
Changlong Li ◽  
Zengyuan Li ◽  
Zhihai Gao ◽  
Bin Sun
Keyword(s):  
Arid Region ◽  
Source Region ◽  
Aerodynamic Resistance ◽  
Estimation Method ◽  
Source Model ◽  
Vegetation Coverage ◽  
Estimation Accuracy ◽  
Estimation Model ◽  
Sparse Vegetation ◽  
Improved Model

Evapotranspiration (ET) is an important part of the water, carbon, and energy cycles in ecosystems, especially in the drylands. However, due to the particularity of sparse vegetation, the estimation accuracy of ET has been relatively low in the drylands. Therefore, based on the dry climate and sparse vegetation distribution characteristics of the drylands, this study optimized the core algorithms (canopy boundary resistance, aerodynamic resistance, and sparse vegetation coverage) and explored an ET estimation method in the Shuttleworth–Wallace two-layer model (SW model). Then, the Beijing–Tianjin sandstorm source region (BTSSR) was used as the study area to evaluate the applicability of the improved model in the drylands. Results show that: (1) The R2 value of the improved model results was increased by 1.4 and the RMSE was reduced by 1.9 mm, especially in extreme value regions of ET (maximum or minimum). (2) Regardless of the spatial distribution and seasonal changes of the ET (63–790 mm), the improved ET estimation model could accurately capture the differences. Furtherly, the different vegetation regions could stand for the different climate regions to a certain extent. The accuracy of the optimized model was higher in the semi-arid region (R2 = 0.92 and 0.93), while the improved model had the best improvement effect in the arid region, with R2 increasing by 0.12. (3) Precipitation was the decisive factor affecting vegetation transpiration and ET, with R2 value for both exceeding 0.9. The effect of vegetation coverage (VC) was less. This method is expected to provide a more accurate and adaptable model for the estimation of ET in the drylands.

scholarly journals Bayesian DINA Modeling Incorporating Within-Item Characteristic Dependency

2018 ◽  
Vol 43 (2) ◽  
pp. 143-158 ◽  
Author(s):  
Peida Zhan ◽  
Hong Jiao ◽  
Manqian Liao ◽  
Yufang Bian
Keyword(s):  
Bivariate Normal Distribution ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Estimation Algorithms ◽  
Modeling Approach ◽  
Dina Model ◽  
Item Parameters ◽  
Different Types ◽  
Item Characteristics ◽  
Improved Model

The within-item characteristic dependency (WICD) means that dependencies exist among different types of item characteristics/parameters within an item. The potential WICD has been ignored by current modeling approaches and estimation algorithms for the deterministic inputs noisy “and” gate (DINA) model. To explicitly model WICD, this study proposed a modified Bayesian DINA modeling approach where a bivariate normal distribution was employed as a joint prior distribution for correlated item parameters. Simulation results indicated that the model parameters were well recovered and that explicitly modeling WICD improved model parameter estimation accuracy, precision, and efficiency. In addition, when potential item blocks existed, the proposed modeling approach still demonstrated good performance and high robustness. Furthermore, the fraction subtraction data were analyzed to illustrate the application and advantage of the proposed modeling approach.

scholarly journals An ELM-Based Approach for Estimating Train Dwell Time in Urban Rail Traffic

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Wen-jun Chu ◽  
Xing-chen Zhang ◽  
Jun-hua Chen ◽  
Bin Xu
Keyword(s):  
Regression Models ◽  
Dwell Time ◽  
Polynomial Regression ◽  
Time Estimation ◽  
Estimation Model ◽  
Urban Rail ◽  
Overall Performance ◽  
Polynomial Regression Models ◽  
Learning Machine ◽  
Microsimulation Models

Dwell time estimation plays an important role in the operation of urban rail system. On this specific problem, a range of models based on either polynomial regression or microsimulation have been proposed. However, the generalization performance of polynomial regression models is limited and the accuracy of existing microsimulation models is unstable. In this paper, a new dwell time estimation model based on extreme learning machine (ELM) is proposed. The underlying factors that may affect urban rail dwell time are analyzed first. Then, the relationships among different factors are extracted and modeled by ELM neural networks, on basis of which an overall estimation model is proposed. At last, a set of observed data from Beijing subway is used to illustrate the proposed method and verify its overall performance.

Vascular ageing and peripheral pulse: an improved model for assessing their relationship

2021 ◽  
Author(s):  
Cosimo Aliani ◽  
Eva Rossi ◽  
Piergiorgio Francia ◽  
Leonardo Bocchi
Keyword(s):  
Vascular System ◽  
Single Pulse ◽  
Exponential Model ◽  
Estimation Accuracy ◽  
Compact Representation ◽  
Model Parameters ◽  
Pulse Waveform ◽  
Fitting Procedure ◽  
Vascular Age ◽  
Improved Model

Abstract Objective:Vascular ageing is associated with several alterations, including arterial stiffness and endothelial dysfunction. Such alterations represent an independent factor in the development of cardiovascular disease. In our previous works we demonstrated the alterations occurring in the vascular system are themselves reflected in the shape of the peripheral waveform; thus, a model that describes the waveform as a sum of Gaussian curves provides a set of parameters that successfully discriminate between under(<= 35 years old) and over subjects (> 35 years old). In the present work, we explored the feasibility of a new decomposition model, based on a sum of exponential pulses, applied to the same problem. Approach: The first processing step extracts each pulsation from the input signal and removes the long-term trend using a cubic spline with nodes between consecutive pulsations. After that, a Least Squares fitting algorithm determines the set of optimal model parameters that best approximates each single pulse. The vector of model parameters gives a compact representation of the pulse waveform that constitutes the basis for the classification step. Each subject is associated to his/her "representative" pulse waveform, obtained by averaging the vector parameters corresponding to all pulses. Finally, a Bayesan classifier has been designed to discriminate the waveforms of under and over subjects, using the leave-one-subject-out validation method. Main results: Results indicate that the fitting procedure reaches a rate of 96% in under subjects and 95% in over subjects and that the Bayesan classifier is able to correctly classify 91\% of the subjects with a specificity of 94% and a sensibility of 84%. Significance: This study shows a sensible vascular age estimation accuracy with a multi-exponential model, which may help to predict cardiovascular diseases.

scholarly journals Bus Queue Time Estimation Model for a Curbside Bus Stop Considering the Blocking Effect

2021 ◽  
Author(s):  
Tian LUO ◽  
Xiaobin LIU ◽  
Hui JIN
Keyword(s):  
Dwell Time ◽  
Joint Probability ◽  
Time Estimation ◽  
Negative Influence ◽  
Estimation Model ◽  
Lognormal Distributions ◽  
Bus Stop ◽  
And Performance ◽  
Queue Delay ◽  
The Relationship

Abstract Bus queue time estimation of a curbside bus stop is essential to evaluate the operation, reliability and performance of a bus system. Arriving buses and served buses on upstream berths form an overflow queue considering the no overtaking principle and limited overtaking principle. Therefore, the bus dwelling time at the C-th berth may directly influence the stop capacity. The bus queue delay is modeled as a function of bus dwell time at the C-th berth and the dwell time at every berth using different distributions (normal and lognormal distributions) and data fitting approaches. This study aims to estimate the queue time attributed to dwell time at the C-th berth. The results indicate that the queue time should be evaluated by bus dwell time and joint probability density to quantify the negative influence on queue delay. Therefore, in some cases, where decisions must be taken into consideration for more than two buses, the relationship between dwell time at a downstream berth and dwell time at the C-th berth must be considered.

scholarly journals Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoshi Miyamoto ◽  
Zu Soh ◽  
Shigeyuki Okahara ◽  
Akira Furui ◽  
Taiichi Takasaki ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Neurological Complications ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Perfusion Flow ◽  
Postsurgical Complication ◽  
Tenfold Cross Validation ◽  
Suction Flow ◽  
The Relationship

AbstractThe need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

Sign in / Sign up

Export Citation Format

Share Document