考虑路网交通流状态的单变量交通噪声预测

为实现路网区域交通噪声预测,克服传统预测模型中路段间交通特性相互独立以及路段内流量与速度相互独立的缺陷,借助Van Aerde交通流模型,在不同道路等级、设计速度约束下,结合道路线声源噪声排放,构建基于速度的单变量交通噪声预测模型.分别对比4种常见城市道路的交通噪声实测值,模型预测值平均偏差为1.63 dB,满足精度需...     

A comparative Full Bayesian before-and-after analysis and application to urban road safety countermeasures in New Jersey

This paper develops a step-by-step methodology for the application of Full Bayes (FB) approach for before-and-after analysis of road safety countermeasures. As part of this methodology, it studies the posterior prediction capability of Bayesian approaches and their use in crash reduction factor (CRF) estimation. A collection of candidate models are developed to investigate the impacts of different countermeasures on road safety when limited data are available. The candidate models include traditional, random effects, non-hierarchical and hierarchical Poisson-Gamma and Poisson-Lognormal (P-LN) distributions. The use of random effects and hierarchical model structures allows treatment of the data in a time-series cross-section panel, and deal with the spatial and temporal effects in the data. Next, the proposed FB estimation methodology is applied to urban roads in New Jersey to investigate the impacts of different treatment measures on the safety of “urban collectors and arterial roads” with speed limits less than 45 mph. The treatment types include (1) increase in lane width, (2) installation of median barriers, (3) vertical and horizontal improvements in the road alignment; and (4) installation of guide rails. The safety performance functions developed via different model structures show that random effects hierarchical P-LN models with informative hyper-priors perform better compared with other model structures for each treatment type. The individual CRF values are also found to be consistent across the road sections, with all showing a decrease in crash rates after the specific treatment except guide rail installation treatment. The highest decrease in the crash rate is observed after the improvement in vertical and horizontal alignment followed by increase in lane width and installation of median barriers. Overall statistical analyses of the results obtained from different candidate models show that when limited data are available, P-LN model structure combined with higher levels of hierarchy and informative priors may reduce the biases in model parameters resulting in more robust estimates.     

Optimal design of multi-response experiments using semi-definite programming

Optimal design of multi-response experiments for estimating the parameters of multi-response linear models is a challenging problem. The main drawback of the existing algorithms is that they require the solution of many optimization problems in the process of generating an optimal design that involve cumbersome manual operations. Furthermore, all the existing methods generate approximate design and no method for multi-response n-exact design has been cited in the literature. This paper presents a unified formulation for multi-response optimal design problem using Semi-Definite Programming (SDP) that can generate D-, A- and E-optimal designs. The proposed method alleviates the difficulties associated with the existing methods. It solves a one-shot optimization model whose solution selects the optimal design points among all possible points in the design space. We generate both approximate and n-exact designs for multi-response models by solving SDP models with integer variables. Another advantage of the proposed method lies in the amount of computation time taken to generate an optimal design for multi-response models. Several test problems have been solved using an existing interior-point based SDP solver. Numerical results show the potentials and efficiency of the proposed formulation as compared with those of other existing methods. The robustness of the generated designs with respect to the variance-covariance matrix is also investigated.     

DVD租赁问题的模型设计及求解

本文讨论了DVD在线租赁的服务供应商可能遇到的问题与其解决方案。模型Ⅱ解决了在已知订单与各种DVD数量的情况下，如何分配DVD使得满意的人数最多，且总的满意度最大。模型Ⅲ解决了在已知订单的情况下，在一定的满意率与总满意度最大的约束下，如何以最小的成本购买各种DVD，并确定分配方案。模型Ⅴ对原问题作了更深入的推广，解决了在DVD数量有限且租赁费用与租赁时间成正比的条件下如何选择性地出租DVD以获得最大收入的问题。本文所讨论的问题均为最优化问题，但是各题的约束条件均不相同，本文对各个问题建立不同的网络模型，经过严密的理论论证，并用统一的费用流算法在微机上实现，充分利用了网络流模型适用面广，结果精确，计算简单的优点，对DVD租赁的现实问题提供了高效的解决方案。     

The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois county-level data

This paper presents analyses of data from the Highway Safety Information System (HSIS) for the State of Illinois. Our analyses focuses on whether various changes in road network infrastructure and geometric design can be associated with changes in road fatalities and reported accidents. We also evaluate models that control for demographic changes. County-level time-series data is used and fixed effect negative binomial models are estimated. Results cannot confirm the hypothesis that changes in road infrastructure and geometric design have been beneficial for safety. Increases in the number of lanes appears to be associated with both increased traffic-related accidents and fatalities. Increased lane widths appears to be associated with increased fatalities. Increases in outside shoulder width appear to be associated with a decrease in accidents. Inclusion of demographic results does not significantly change these results but does capture much of the residual time trend in the models. Potentially mis-leading results are found when the time trend is not included. In this case a negative association between vertical curvature and both accidents and fatalities. No statistical association with changes in safety is found for median widths, inside shoulder widths, and horizontal and vertical curvature.     

Mixed integer simulation optimization for optimal hydraulic fracturing and production of shale gas fields

Optimal development of shale gas fields involves designing a most productive fracturing network for hydraulic stimulation processes and operating wells appropriately throughout the production time. A hydraulic fracturing network design—determining well placement, number of fracturing stages, and fracture lengths—is defined by specifying a set of integer ordered blocks to drill wells and create fractures in a discrete shale gas reservoir model. The well control variables such as bottom hole pressures or production rates for well operations are real valued. Shale gas development problems, therefore, can be mathematically formulated with mixed-integer optimization models. A shale gas reservoir simulator is used to evaluate the production performance for a hydraulic fracturing and well control plan. To find the optimal fracturing design and well operation is challenging because the problem is a mixed integer optimization problem and entails computationally expensive reservoir simulation. A dynamic simplex interpolation-based alternate subspace (DSIAS) search method is applied for mixed integer optimization problems associated with shale gas development projects. The optimization performance is demonstrated with the example case of the development of the Barnett Shale field. The optimization results of DSIAS are compared with those of a pattern search algorithm.     

Accident prediction models for urban roads

This paper describes some of the main findings from two separate studies on accident prediction models for urban junctions and urban road links described in [Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate, 1995; Uheldsmodel for bygader- Del2: Modeller for straekninger. Notat 59, The Danish Road Directorate, 1998] (Greibe and Hemdorff, 1995, 1988).The main objective for the studies was to establish simple, practicable accident models that can predict the expected number of accidents at urban junctions and road links as accurately as possible. The models can be used to identify factors affecting road safety and in relation to 'black spot' identification and network safety analysis undertaken by local road authorities.The accident prediction models are based on data from 1036 junctions and 142 km road links in urban areas. Generalised linear modelling techniques were used to relate accident frequencies to explanatory variables.The estimated accident prediction models for road links were capable of describing more than 60% of the systematic variation ('percentage-explained' value) while the models for junctions had lower values. This indicates that modelling accidents for road links is less complicated than for junctions, probably due to a more uniform accident pattern and a simpler traffic flow exposure or due to lack of adequate explanatory variables for junctions.Explanatory variables describing road design and road geometry proved to be significant for road link models but less important in junction models. The most powerful variable for all models was motor vehicle traffic flow.     

Output-based mesh optimization for hybridized and embedded discontinuous Galerkin methods

This paper presents a method for optimizing computational meshes for the prediction of scalar outputs when using hybridized and embedded discontinuous Galerkin (HDG/EDG) discretizations. Hybridization offers memory and computational time advantages compared to the standard discontinuous Galerkin (DG) method through a decoupling of elemental degrees of freedom and the introduction of face degrees of freedom that become the only globally coupled unknowns. However, the additional equations of weak flux continuity on each interior face introduce new residuals that augment output error estimates and complicate existing element-centric mesh optimization methods. This work presents techniques for converting face-based error estimates to elements and sampling their reduction with refinement in order to determine element-specific anisotropic convergence rate tensors. The error sampling uses fine-space adjoint projections and does not require additional solves on subelements. Together with a degree-of-freedom cost model, the error models drive metric-based unstructured mesh optimization. Adaptive results for inviscid and viscous two-dimensional flow problems demonstrate (i) improvement of EDG mesh optimality when using error models that incorporate face errors, (ii) the relative insensitivity of HDG mesh optimality to the incorporation of face errors, and (iii) degree of freedom and computational-time benefits of hybridized methods, particularly EDG, relative to DG.     

Parameter and tolerance design in the engineering modelling stage

Generalized parameter and tolerance design problems have been formulated as nonlinear optimization problems under a broader set of assumptions. A new approach for parameter design and tolerance design problems is outlined. This approach integrates engineering models and numerical optimization methods so it can work in the early stage of design where a good engineering model is available to simulate the real product or process. The new approach is also able to handle multiple quality characteristics and constraints. Several important theoretical results have been derived by the authors for tolerance design problems that could serve as guidelines for optimal tolerance design and tolerance distribution.     

基于粒子群优化算法和BP神经网络的变频压缩机功率预测

本文针对变频压缩机的功率测量困难,测量误差大等问题,提出了一种仿真测量模型。利用粒子群算法寻找全局最优粒子,用它初始化BP神经网络的阈值和权值,测量变频压缩机的功率。本文共建立了3种仿真模进行对比,分别为BP神经网络模型、GA-BP神经网络模型和PSO-BP神经网络模型,然后分别通过3种模型的内插、蒸发温度外推和冷凝温度外推的测试方法对变频压缩机进行功率测量,对比分析其预测结果的平均相对误差和拟合程度。结果表明:基于粒子群算法优化的BP神经网络模型明显优于其他两个模型,特别是在冷凝温度外推测试中,较其他两个神经网络相对误差降低了1. 11%、2. 64%,3种测试方法下的平均相对误差均小于1%,拟合程度在0. 9以上,表明基于粒子群算法优化的BP神经网络模型对变频压缩机功率有较好的测量能力,而且有较强的泛化能力。     

Development of a multidisciplinary–multiresponse robust design optimization model

Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.     

Hierarchical asphalt pavement deterioration model for climate impact studies

Quantification of the impacts of projected climate change on road pavement performance is possible using predictive models that correctly consider key causal factors of pavement deterioration. These factors include climate, traffic, properties of materials and the design of pavements. This paper presents a new model developed to predict rutting in asphalt surfacing. In addition to the key causal factors of road deterioration, the developed model takes into account several sources of uncertainties, particularly those inherent in future climate change predictions and model input parameters. The asphalt surfacing rut depth progression model was developed from a hierarchical road network data structure using a Bayesian regression approach resulting in a model for each surfacing group. The model was applied within a Monte Carlo simulation framework to derive probabilistic outputs of pavement rut depth progression and maintenance costs under the pre-determined future climate scenarios. This model is useful for application at both the network and project levels to develop road management strategies and policies.     

Optimal Design of Multi-Airgap Electrical Machines: An Unknown Size Mixed-Constrained Global Optimization Formulation

This paper solves, in a rational way, more sophisticated electrical machine design problems by extending the work done by Fitan et al. It addresses the special case of multi-airgap cylindrical concentric machines. The solution requires a reformulation of the inverse problem associated with the design of electrical machines. Thus, a new mixed-constrained global optimization problem has to be solved. From a mathematical point of view, a major difficulty is that the number of variables and constraints varies during the resolution, depending on the number of considered airgaps. Moreover, this involves extensions of analytical models used for permanent-magnet machines. The paper uses numerical tests for concentric rotor machines with one, two, and three (in one case) mechanical airgaps to validate this methodology of design     

Rule extraction from an optimized neural network for traffic crash frequency modeling

This study develops a neural network (NN) model to explore the nonlinear relationship between crash frequency and risk factors. To eliminate the possibility of over-fitting and to deal with the black-box characteristic, a network structure optimization algorithm and a rule extraction method are proposed. A case study compares the performance of the trained and modified NN models with that of the traditional negative binomial (NB) model for analyzing crash frequency on road segments in Hong Kong. The results indicate that the optimized NNs have somewhat better fitting and predictive performance than the NB models. Moreover, the smaller training/testing errors in the optimized NNs with pruned input and hidden nodes demonstrate the ability of the structure optimization algorithm to identify the insignificant factors and to improve the model generalization capacity. Furthermore, the rule-set extracted from the optimized NN model can reveal the effect of each explanatory variable on the crash frequency under different conditions, and implies the existence of nonlinear relationship between factors and crash frequency. With the structure optimization algorithm and rule extraction method, the modified NN model has great potential for modeling crash frequency, and may be considered as a good alternative for road safety analysis.     

Simulation–optimization model for groundwater contamination remediation using meshfree point collocation method and particle swarm optimization

Remediation of the groundwater contamination problem is a tedious, time consuming and expensive process. Pump and treat (PAT) is one of the commonly used techniques for groundwater remediation in which the contaminated groundwater is pumped, treated and put back to the aquifer system or other sources. Developing simulation-optimization (S/O) model proved to be very useful in the design process of an effective PAT system. Simulation models help in predicting the spatial and temporal variation of the contamination plume while optimization models help in minimizing the cost of pumping. Generally, grid or mesh based models such as Finite Difference Method (FDM) or Finite Element Methods (FEM) is used for the groundwater flow and transport simulation. But it is found that grid/mesh generation is a time consuming process. Therefore, recently Meshfree (MFree) based numerical models are developed to avoid this difficulty of meshing and remeshing. MFree Point Collocation Method (PCM) is a simple meshfree method used for the simulation of coupled groundwater flow and contaminant transport. For groundwater optimization problems, even though number of methods such as linear programming, nonlinear programming, etc. are available, evolutionary algorithm based techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) are found to be very effective. In this paper, a simulation model using MFree PCM for confined groundwater flow and transport and a PSO based single objective optimization model are developed and coupled to get an effective S/O model for groundwater remediation using PAT. The S/O model based on PCM and PSO is applied for a polluted hypothetical confined aquifer and its performance is compared with Finite Element Method?CBinary Coded Genetic Algorithm (FEM?CGA) model. It is found that both the models are in good agreement with each other showing the applicability of the present approach. The PCM?CPSO based S/O model is simple and more effective in groundwater contamination remediation design using PAT.     

Reduced order model assisted evolutionary algorithms for multi-objective flow design optimization

Evolutionary algorithms (EAs) have been widely used for flow design optimization problems for their well-known robustness and derivative-free property as well as their advantages in dealing with multi-objective optimization problems and providing global optimal solutions. However, EAs usually involve a large number of function evaluations that are sometimes quite time consuming. In this article a reduced order modelling technique that combines proper orthogonal decomposition and radial basis function interpolation is developed to reduce the computational cost. These models provide an efficient way to simulate the whole flow region with varied geometry parameters instead of solving partial differential equations. As a test case, the design optimization of a heat exchanger is considered. Shape variation is conducted through a free form deformation technique, which deforms the computational grid employed by the flow solver. A comparison between the optimization results when using reduced order models and the exact flow solver is presented.     

ADAPTIVE RESPONSE SURFACE METHOD - A GLOBAL OPTIMIZATION SCHEME FOR APPROXIMATION-BASED DESIGN PROBLEMS

For design problems involving computation-intensive analysis or simulation processes, approximation models are usually introduced to reduce computation lime. Most approximation-based optimization methods make step-by-step improvements to the approximation model by adjusting the limits of the design variables. In this work, a new approximation-based optimization method for computation-intensive design problems - the adaptive response surface method(ARSM), is presented. The ARSM creates quadratic approximation models for the computation-intensive design objective function in a gradually reduced design space. The ARSM was designed to avoid being trapped by local optima and to identify the global design optimum with a modest number of objective function evaluations. Extensive tests on the ARSM as a global optimization scheme using benchmark problems, as well as an industrial design application of the method, are presented. Advantages and limitations of the approach are also discussed     

Multi-objective optimization for the multi-mode finance-based project scheduling problem

The finance-based scheduling problem (FBSP) is about scheduling project activities without exceeding a credit line financing limit. The FBSP is extended to consider different execution modes that result in the multi-mode FBSP (MMFBSP). Unfortunately, researchers have abandoned the development of exact models to solve the FBSP and its extensions. Instead, researchers have heavily relied on the use of heuristics and meta-heuristics, which do not guarantee solution optimality. No exact models are available for contractors who look for optimal solutions to the multi-objective MMFBSP. CPLEX, which is an exact solver, has witnessed a significant decrease in its computation time. Moreover, its current version, CPLEX 12.9, solves multi-objective optimization problems. This study presents a mixed-integer linear programming model for the multi-objective MMFBSP. Using CPLEX 12.9, we discuss several techniques that researchers can use to optimize a multi-objective MMFBSP. We test our model by solving several problems from the literature. We also show how to solve multi-objective optimization problems by using CPLEX 12.9 and how computation time increases as problem size increases. The small increase in computation time compared with possible cost savings make exact models a must for practitioners. Moreover, the linear programming-relaxation of the model, which takes seconds, can provide an excellent lower bound.