scholarly journals Risk Distribution Characteristics and Optimization of Short Weaving Area for Complex Municipal Interchanges

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yonggang Liao ◽  
Yong Yang ◽  
Zhenzhong Ding ◽  
Kaimin Tong ◽  
Yanjie Zeng
Keyword(s):  
Standard Deviation ◽  
Optimization Method ◽  
Distribution Characteristics ◽  
Original Design ◽  
Average Speed ◽  
Absolute Value ◽  
Risk Distribution ◽  
Result Analysis ◽  
Traffic Organization ◽  
Speed Standard Deviation

This paper is focused on analyzing the risk distribution characteristics in short weaving areas of urban interchanges. The study was carried out on merge-diverge weaving areas with different lengths of 350 m, 450 m, and 550 m. To evaluate and identify the risk, the average speed, speed standard deviation, acceleration range, and average absolute value of acceleration were selected as indicators. Vissim simulation was applied to collect the identification indicator value of 21 typical lane sections. The results show that the risk is concentrated at the 3/4 section and exit section of the outer lane. The vehicle-operating status of the inner and middle lanes is almost unaffected. The operating speed of the outer lane is approximately 4/5 of the same position in the inner lane at 3/4 of the length of the weaving segment, while the speed standard deviation is approximately 2 times greater, and the acceleration range is approximately 2–3 times greater. Moreover, the acceleration of the average absolute value is also approximately 2–3 times greater. To balance the risk distribution, an optimization method is proposed based on the result analysis. Compared with the original design, the results show that a reasonable method of traffic organization for the complex weaving area can effectively improve the risk distribution in the weaving area and reduce the high peak of risk concentration. These results provide a basis for the optimization method and traffic organization of short weaving areas of municipal interchanges.

scholarly journals Relationship between Speed Characteristics and Traffic Safety on Freeways in Mainland China

2018 ◽  
Vol 47 (4) ◽  
pp. 318-328 ◽  
Author(s):  
Ziyue Tang
Keyword(s):  
Standard Deviation ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Mainland China ◽  
Practical Significance ◽  
Vehicle Speed ◽  
Average Speed ◽  
Regression Methods ◽  
Nonlinear Regression Method ◽  
Speed Standard Deviation

Based on the traffic accidents statistical data of 10 typical freeways in mainland China, by using of some kinds of regression model, the influences of the average vehicle speed and the speed standard deviation on the traffic safety are studied. According to the regression results, the accidents show an increasing trend with the increase of the vehicle average speed and the speed standard deviation. On this basis, in view of the regression results, the strategy is put forward for controlling the vehicle average speed and the speed standard deviation, which has important theoretical and practical significance for improving highway safety. After a comprehensive comparison among these regression methods, it is found that the nonlinear regression method of user-defined model expression has the best fitting effect, and it can also more accurately describe the objective reality. It has high practicality and popularized value.

scholarly journals A Systematic Study of the Best Fitting Line Problem

2021 ◽  
Author(s):  
Giorgio Gambirasio
Keyword(s):  
Center Of Mass ◽  
Vertical Axis ◽  
Absolute Error ◽  
Optimization Method ◽  
Least Square ◽  
Absolute Value ◽  
Meeting Point ◽  
Best Fitting ◽  
The Subject ◽  
Comprehensive Study

AbstractThe classical approach for tackling the problem of drawing the 'best fitting line' through a plot of experimental points (here called a scenario) is the least square process applied to the errors along the vertical axis. However, more elaborate processes exist or may be found. In this report, we present a comprehensive study on the subject. Five possible processes are identified: two of them (respectively called VE, HE) measure errors along one axis, and the remaining three (respectively called YE, PE, and AE) take into consideration errors along both axes. Since the axes and their corresponding errors may have different physical dimensions, a procedure is proposed to compensate for this difference so that all processes could express their answers in the same consistent dimensions. As usual, to avoid mutual cancellation, errors are squared or taken in their absolute value. The two cases are separately studied.In the case of squared errors, the five processes are tested in many scenarios of experimental points, both analytically (using the software Mathematica) and numerically (with programs written on Python platform employing the Nelder-Mead optimization method). The investigation showed the possible existence of multiple solutions. Different answers originating from different starting points in Nelder?Mead correspond to solutions revealed by analytic search with Mathematica. For each scenario of experimental points, it was found that the best lines of the five processes intercept at a common point. Furthermore, the point of intercept happens to coincide with the 'center of mass' of the scenario. This fact is described by stating the existence of an empirical 'Meeting Point Law'. The case of absolute errors is only treated numerically, with Nelder?Mead minimizer. As expected, the absolute error option shows greater robustness against outliers than the square error option, for all processes. The Meeting Point Law is not valid in this case.By taking the value of minimized error as a criterion, the five processes are compared for efficiency. On average, processes PE and AE, that consider errors along both axes, resulted in the smallest minimized error and may be considered the best processes. Processes that rely on errors along a single axis (VE, HE) stay at the second place. In all cases, YE is the process that results in the largest minimized errors

Particle Swarm Optimization Method for Optimal Prioritization of Pavement Sections for Maintenance and Rehabilitation Activities

2013 ◽  
Vol 343 ◽  
pp. 43-49 ◽  
Author(s):  
Jia Ruey Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Particle Swarm ◽  
Cost Savings ◽  
Optimization Method ◽  
Alternative Solution ◽  
Swarm Optimization ◽  
Pavement Condition ◽  
Significant Time ◽  
Maintenance And Rehabilitation

Optimal prioritization of maintenance and rehabilitation (M&R) activities for pavement sections can enable significant time and cost-savings. In this study, we used the particle swarm optimization (PSO) method to achieve optimal prioritization of 135 pavement sections based on eight pavement condition parameters. The parameters included standard deviation (SD) for smoothness, rutting, deflections, cracking, pothole, bleeding, patching, and shoving. SD for smoothness, rutting, and deflections were inspected using instruments, while cracking, pothole, bleeding, patching, and shoving were surveyed visually. The PSO method was used to quickly calculate the synthetic pavement condition for each pavement section and then obtain the optimal prioritization of pavement sections. With this approach, pavement engineers are able to efficiently perform appropriate and timely M&R activities for pavement sections, according to their priority. This study provides an alternative solution to current approaches for prioritization of pavement sections.

A Generalized and Robust Anti-Predatory Nature-Inspired Algorithm for Complex Problems

2019 ◽  
Vol 10 (1) ◽  
pp. 75-91 ◽  
Author(s):  
Rohit Kumar Sachan ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Standard Deviation ◽  
Defense Mechanism ◽  
Mathematical Formulation ◽  
Predatory Behavior ◽  
Rank Test ◽  
Bee Colony ◽  
Self Defense ◽  
Nature Inspired Algorithm ◽  
Nature Inspired Algorithms ◽  
Result Analysis

This article describes how nature-inspired algorithms (NIAs) have evolved as efficient approaches for addressing the complexities inherent in the optimization of real-world applications. These algorithms are designed to imitate processes in nature that provide some ways of problem solving. Although various nature-inspired algorithms have been proposed by various researchers in the past, a robust and computationally simple NIA is still missing. A novel nature-inspired algorithm that adapts to the anti-predatory behavior of the frog is proposed. The algorithm mimics the self defense mechanism of a frog. Frogs use their reflexes as a means of protecting themselves from the predators. A mathematical formulation of these reflexes forms the core of the proposed approach. The robustness of the proposed algorithm is verified through performance evaluation on sixteen different unconstrained mathematical benchmark functions based on best and worst values as well as mean and standard deviation of the computed results. These functions are representative of different properties and characteristics of the problem domain. The strength and robustness of the proposed algorithm is established through a comparative result analysis with six well-known optimization algorithms, namely: genetic, particle swarm, differential evolution, artificial bee colony, teacher learning and Jaya. The Friedman rank test and the Holm-Sidak test have been used for statistical analysis of obtained results. The proposed algorithm ranks first in the case of mean result and scores second rank in the case of “standard deviation”. This proves the significance of the proposed algorithm.

A Study of Tri-Axial Fluxgate Magnetometer

2012 ◽  
Vol 263-266 ◽  
pp. 69-75
Author(s):  
Yue Zhang ◽  
Chen Xiao Cao
Keyword(s):  
High Temperature ◽  
Silicon On Insulator ◽  
Fluxgate Magnetometer ◽  
Test Results ◽  
Original Design ◽  
Technical Specifications ◽  
Result Analysis ◽  
Development Tendency ◽  
Hybrid Circuit ◽  
Better Than

This paper is devoted to study tri-axial fluxgate magnetometer, and it begin with principle of fluxgate magnetometer, and then its structure, electric design and test result analysis are introduced. At present, its development tendency is to simplify and miniaturize, we carried out study of tri-axial fluxgate magnetometer. Its technical specifications such as resolution, noise, response of step up, setting up time and sensitivity meet or even better than original design requirements. At first design of tri-axial fluxgate magnetometer is determined and a transducer is developed. Secondly, a high temperature circuit is developed with SOI (Silicon on Insulator) electronics wafer and hybrid circuit technology. At last, 5 specifications test results are presented from aspects of high temperature and high accuracies, respectively.

A Structure Optimization for Hooklift Arm Device Based on the Genetic Algorithm

2012 ◽  
Vol 455-456 ◽  
pp. 1504-1508
Author(s):  
Huan Ming Chen ◽  
Da Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Parameter Optimization ◽  
Structure Optimization ◽  
Optimization Method ◽  
Optimized Design ◽  
Design Decision ◽  
Original Design ◽  
Multi Objective ◽  
Engineering Constraint

Based on the theory of FEM, the hooklift arm is modeled with the FEM software, and the structure of the device is optimized with genetic algorithm in a multi-objective/multi-parameter optimization environment, which results in an optimal design decision of the hooklift arm device under the engineering constraint. Comparison between optimized design decision and original design decision shows that the optimization is correct and the proposed multi-objective/multi-parameter optimization method is effective in improving the hooklift arm device.

scholarly journals Reducing the Standard Deviation in Multiple-Assay Experiments Where the Variation Matters but the Absolute Value Does Not

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e78205 ◽  
Author(s):  
Pablo Echenique-Robba ◽  
María Alejandra Nelo-Bazán ◽  
José A. Carrodeguas
Keyword(s):  
Standard Deviation ◽  
Absolute Value ◽  
The Absolute

Distribution Characteristics as Predictors of Error in Intuitive Estimation of Means

1975 ◽  
Vol 36 (2) ◽  
pp. 367-370
Author(s):  
Mary L. Wolfe
Keyword(s):  
Regression Analysis ◽  
Standard Deviation ◽  
University Students ◽  
Multiple Regression Analysis ◽  
List Length ◽  
Estimation Error ◽  
Distribution Characteristics ◽  
Whole Numbers ◽  
Distribution Shape ◽  
Intuitive Estimation

33 university students made intuitive estimates of the means of 27 lists of two-digit whole numbers. The lists varied independently with respect to length (3, 5 and 7 numbers), standard deviation (approximately 6, 12 and 24) and distribution shape (symmetrical, positively skewed and negatively skewed). Multiple regression analysis showed these three variables taken together to be a fairly efficient set of predictors of absolute estimation error. List standard deviation was the best predictor, with list-length and distribution-shape accounting for much smaller proportions of criterion variance.

An Optimization Design Method of Geometric Parameters of Skid Shoe Referring to Subject Weight of Marine Structures

2014 ◽  
Vol 678 ◽  
pp. 325-332
Author(s):  
Feng Yan Yang ◽  
Xiang Zhen Yan ◽  
Zheng Rong Song ◽  
Ming Wang Yang ◽  
Zi Kun Zhao ◽  
...  
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Plate Thickness ◽  
Optimization Method ◽  
Geometric Parameters ◽  
Frame Structure ◽  
Complex Method ◽  
Optimized Design ◽  
Original Design ◽  
Marine Structures

The optimization design method of geometric parameters of skid shoe which is used to subject weight of marine structures is proposed. Considering skid shoe as steel frame structure, total weight and the bearing capacity of the skid shoe are selected as optimal objectives, and geometric parameters of the skid shoe are taken as design variables. Taking the strength, stiffness, local stability of the skid shoe as the constraint conditions, multi-objectives constraints optimization model of geometric parameters is established, and solved based on complex method. According to research results, a computer program has been developed using VC language. Then geometric optimum parameters of skid shoe in service of CNOOC are analyzed by the program. The results show that optimized design decreases steel volume, steel plate thickness by 28.7%, 18.4%, respectively, compared with original design. The optimization method has a series of advantages, such as simple model, fast calculating speed, high calculation accuracy.

scholarly journals Penerapan Algoritme Genetik Untuk Seleksi Peubah Regresi Logistik

2020 ◽  
Vol 9 (1) ◽  
pp. 51-58
Author(s):  
Dian Ayuningtyas ◽  
Bagus Sartono ◽  
Farit Mochamad Afendi
Keyword(s):  
Genetic Algorithm ◽  
Variable Selection ◽  
Optimization Method ◽  
Response Variable ◽  
Absolute Value ◽  
Independent Variables ◽  
Interaction Factors ◽  
Independent Variable ◽  
Positive Effect ◽  
Excessive Number

In a study, interaction factors are the potential to have important effects on the response variable. But research involving interaction factors often encounters two problems, namely the excessive number of variables and the difficulty of implementing the heredity principle. The alternative solution is to do variable selection using a metaheuristic optimization method, In this study, the logistic regression variable selection was done using a genetic algorithm. The genetic algorithm is modified so that every independent variable has a different probability to be included in the model. That probability is based on the absolute value of the correlation of the independent variable with the response variable. These modifications have a positive effect on the results of variable selection. To choose significant independent variables, 30 repetitions of the genetic algorithm can be performed using the objective function AIC. Of the 30 repetitions, if a variable appears in all formed models, then the variable is an independent variable that has a significant effect on the response variable. The application of this method to Myopia data can show significant variables well.

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