A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem

Abstract: Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near‐optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet the constraints of highway design while minimizing total cost as the objective function. The model uses geographical information system (GIS) maps as an efficient and fast way to calculate right‐of‐way costs, earthwork costs, and any other spatial information and constraints that should be implemented in the design process. The efficiency of the algorithm was verified through a case study using an artificial map as the study region. Finally, we applied the algorithm to a real‐world example and the results were compared with the alignment found by traditional methods.     

Prescreening and Repairing in a Genetic Algorithm for Highway Alignment Optimization

Abstract:   The method of handling infeasible solutions in an evolutionary search algorithm [e.g., genetic algorithms (GAs)] is crucial to the effectiveness of the solution search process. This problem arises because solution search steps, techniques, and operators used in GAs (such as reproduction, mutation, and recombination) are normally  " blind " to the constraints, and thus GAs can generate solutions that do not satisfy the requirements of the problems. In GA-based highway alignment optimization (HAO), many infeasible solutions, which violate model constraints, are also possibly generated, and evaluation of such solutions is wasteful. This study focuses on ways to avoid wasting computation time on evaluating infeasible solutions generated from the GA-based HAO, and develops a prescreening and repairing (P&R) method for an efficient search of highway alignments. The key idea of the P&R method is to repair (before the very detailed alignment evaluation) any candidate alignments whose violations of design constraints can be fixed with reasonable modifications. However, infeasible alignments whose violations of constraints are too severe to repair are discarded (prescreened) before any detailed evaluation procedure is applied. The proposed P&R method is simple, but significantly improves computation time and solution quality in the GA-based HAO process. Such improvements are demonstrated with a test example for a real road project. Through the example study, it is shown that the model incorporating the P&R method can find a good solution much faster (by approximately 23%) than the model with the conventional penalty method. In addition, the P&R method allows the model to evaluate about 70% more solutions than that it can evaluate with the penalty method for the same number of generations.     

Using GIS, Genetic Algorithms, and Visualization in Highway Development

A model for highway development is presented, which uses geographic information systems (GIS), genetic algorithms (GA), and computer visualization (CV). GIS serves as a repository of geographic information and enables spatial manipulations and database management. GAs are used to optimize highway alignments in a complex search space. CV is a technique used to convey the characteristics of alternative solutions, which can be the basis of decisions. The proposed model implements GIS and GA to find an optimized alignment based on the minimization of highway costs. CV is implemented to investigate the effects of intangible parameters, such as unusual land and environmental characteristics not considered in optimization. Constrained optimization using GAs may be performed at subsequent stages if necessary using feedback received from CVs. Implementation of the model in a real highway project from Maryland indicates that integration of GIS, GAs, and CV greatly enhances the highway development process.     

Vertical alignment optimization of mountain railways with terrain-driven greedy algorithm improved by Monte Carlo tree search

Vertical alignment design is an important process for railway construction which fundamentally affects the infrastructure investment cost. Determining an optimized vertical alignment is a challenging task since the objective function is non-linear, non-differentiable, and quite unsmooth. Great efforts have been invested in solving the vertical alignment optimization problem and many methods have been proposed. However, for vertical alignment designs in complex mountainous regions, the terrain conditions impose great difficulties and, hence, many bridges and tunnels are generally required. Thus, reasonably locating bridges and tunnels along the entire alignment (EA) is a major concern that deserves further investigations. To solve this problem, this study develops a terrain-driven greedy algorithm improved by Monte Carlo tree search (T-GRA-MCTS). A terrain-driven method is proposed to determine the number and longitudinal distribution of vertical points of intersection (VPIs). In order to trade off the local section of an alignment versus the EA when optimizing each VPI along the alignment to locate bridges and tunnels reasonably, an MCTS is employed and integrated with a GRA. The basic MCTS is modified for vertical alignment optimization with a novel equation for computing the upper confidence bounds for trees and a customized termination criterion is provided. A real-world railway case is used to demonstrate the effectiveness of the proposed method. The results show that the T-GRA-MCTS performs better than a greedy search method without MCTS or a widely used nature-inspired algorithm (i.e., a particle swarm optimization). Moreover, it can find a less expensive solution than the one designed by experienced human engineers.     

GPS–GIS-Based Procedure for Tracking Vehicle Path on Horizontal Alignments

Abstract:   This article presents a global positioning system–geographic information system (GPS–GIS)-based procedure for the deduction of the horizontal alignment of a road based on the path of a control vehicle. Using differential GPS surveying, field data were collected at a 0.1-second interval, under different speed conditions on a 25-km section of a two-lane rural highway in eastern Ontario. The raw GPS data were post-processed to filter out the possible errors and then imported into a GIS environment for analysis and interpretation of the results. An extension for ArcView was written to determine the geometric features of the highway horizontal alignment, including the tangents, spirals, and circular curves. Values were obtained for the radius and length of nine circular curves, length of spirals, and the lateral position of the vehicle path along the straight and curved segments. These values were compared with the same features of the actual highway alignment. The results showed that the developed procedure and ArcView extension could produce the horizontal alignment of a road quickly, accurately, and for a relatively low cost. In addition to the extraction of the horizontal alignment of a road, the procedure can be used to track the actual vehicle path under normal driving conditions and compare it with the horizontal alignment of a road in an investigation concerning driver behavior.     

True Optimization of Pavement Maintenance Options with What-If Models

Abstract:  A prototypical problem road agencies are faced with is to find the optimal application schedule of maintenance works for a given road section. To solve such problems what-if models such as the road transport investment model (RTIM), the highway economic requirements system (HERS), and the highway development and management tool (HDM-4) are widely used to predict the consequences of different maintenance options. With these models maintenance options to be compared must be exogenously specified by an analyst, and the "optimization" with these routines simply chooses the best among those compared. As there are usually infinite numbers of options, it is impossible to exhaust all of them and only suboptimal optimizers may be found with this approach. The present article proposes the use of gradient search methods with what-if models to find the true optima without requiring exogenously specified alternatives. It demonstrates through a case study the feasibility of the use of the steepest descent method and the conjugate gradient method along with HDM-4 to find the true optimum maintenance options.     

Reliability-Based Optimal Design of Electrical Transmission Towers Using Multi-Objective Genetic Algorithms

Abstract:   A hybrid methodology for performing reliability-based structural optimization of three-dimensional trusses is presented. This hybrid methodology links the search and optimization capabilities of multi-objective genetic algorithms (MOGA) with structural performance information provided by finite element reliability analysis. To highlight the strengths of the proposed methodology, a practical example is presented that concerns optimizing the topology, geometry, and member sizes of electrical transmission towers. The weight and reliability index of a tower are defined as the two objectives used by MOGA to perform Pareto ranking of tower designs. The truss deformation and the member stresses are compared to threshold values to assess the reliability of each tower under wind loading. Importance sampling is used for the reliability analysis. Both the wind pressure and the wind direction are considered as random variables in the analysis. The research results presented demonstrate the benefit of implementing MOGA optimization as an integral part of a reliability-based optimization procedure for three-dimensional trusses.     

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

Abstract:   In this article a dynamic system-optimal traffic assignment model is formulated for a congested urban road network with a number of signalized intersections. A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods.     

Mountain railway alignment optimization integrating layouts of large-scale auxiliary construction projects

Mountain railway alignment design is an important but complex civil engineering problem. To overcome the drastically undulating terrain, long tunnels and high bridges are major structures used along a mountain railway, which poses great challenges for railway design and construction. Unfortunately, despite being studied for many years, the crucial construction factors of complex structures have received slight attention in alignment optimization. In this paper, for the first time, the layout of large-scale auxiliary construction projects (LACPs), including tunnel shafts and access roads, is incorporated into the alignment design process in order to consider construction practicability and economy. Primarily, an alignment–LACPs concurrent optimization model is built. After defining the comprehensive design variables, the alignment–LACPs total construction cost is formulated as the objective function. Besides, the separate constraints for designing the alignment and LACPs are considered. Also, a construction duration computation is proposed for constraining the alignment–LACPs integration. To solve the model, a four-step hybrid solution method is developed. Specifically, the alignment is first generated with a particle swarm optimization (PSO). Afterward, a new divide and conquer approach is devised to search for shaft alternatives along the alignment. Then, a customized Dijkstra algorithm is developed to search for complex access roads. Finally, a novel polynomial mechanism for time-varying acceleration coefficients (TVAC) is designed for PSO to evolve the alignment–LACPs solutions. The above model and methods have been applied to two complex actual mountain railway examples. Their effectiveness is demonstrated through detailed analysis of resulting railway solutions and control experiments with contemporary TVAC-based methods.     

Preliminary Highway Design with Genetic Algorithms and Geographic Information Systems

A method that integrates geographic information systems (GIS) with genetic algorithms (GAs) for optimizing horizontal highway alignments between two given end points is presented in this article. The proposed approach can be used to optimize alignments in highly irregular geographic spaces. The resulting alignments are smooth and satisfy minimum-radius constraints, as required by highway design standards. The objective function in the proposed model considers land-acquisition cost, environmental impacts such as wetlands and flood plains, length-dependent costs (which are proportional to the alignment length), and user costs. A numerical example based on a real map is employed to demonstrate application of the proposed model to the preliminary design of horizontal alignments.     

Optimizing Design of Highway Horizontal Alignments: New Substantive Safety Approach

Abstract:   Highway agencies are continually facing safety problems on highways, especially on horizontal alignments. Traditionally, the geometric design implicitly considers safety through satisfying minimum design requirements for different geometric elements. This article presents a new substantive-safety approach for the design of horizontal alignments based not only on minimum design guidelines, but also on actual collision experience. The curve radii, spiral lengths, lane width, shoulder width, and tangent lengths are determined to optimize the mean collision frequency along the highway. The model allows the parameters of the horizontal alignment to vary within specified ranges. The model also considers any specified physical obstructions in selecting the optimal alignment. Collision experience is addressed using existing collision prediction models for horizontal alignments and cross sections. The model is applicable to two-lane rural highways for which collision prediction models exist. Application of the model is presented using numerical examples. The proposed substantive-safety approach takes horizontal alignment design one step further beyond the minimum-guideline concept, and therefore should be of interest to highway designers.     

探讨线形优化下的维护费

介绍了公路设计优化模型,从基本建设费、维护费等方面分析了公路边坡、土类对费用的影响,以挖方断面为例对最优坡度进行了计算,国内外大量实例显示:土类及边坡对整个公路线形优化有着非常重要的影响.     

立交匝道平面线形优化设计及MATLAB的应用

本文在探讨最优化计算理论与方法的基础上,分析立交匝道平面线形设计的特点与方法,拟定线形组合型式,建立平面线形优化设计的数学模型,给出相应的优化计算方法,并应用大型通用的辅助计算软件MATLAB优化工具箱里的优化函数,实现线形参数的优化设计。     

淮北平原典型地基土动态变形模量特征及与承载力相关关系研究

 针对淮北平原新近沉积层土质变化大、普遍发育钙质结核等地质特征,依托在建高速公路开展大量的动态平板载荷试验,统计分析不同土体动态变形模量 的值域范围和变化规律,探讨淮北平原不同地质区域 的分布特征。在此基础上,通过静力触探、螺旋板载荷和平板载荷对比试验,建立动态变形模量 与比贯入阻力、地基承载力的相关关系式。研究表明,动态平板载荷试验作为一种便捷快速的原位检测方法,测试值 能很好地反映原地基土体力学特性和变化规律,与比贯入阻力 值、地基承载力具有较好的相关性,可在高速公路工程中作为地基承载力测定指标加以推广应用。     

Mountain railway alignment optimization based on landform recognition and presetting of dominating structures

Mountain railway alignment optimization has always been a challenge for designers and researchers in this field. It is extremely difficult for existing methods that optimize alignments before major structures to generate a better alignment than the best one provided by human designers when the terrain is drastically undulating between the start and endpoints. To fill this gap, a “structures before alignments” design process is proposed in this paper. Primarily, a landform recognition method is devised for recognizing dominating landforms. Then, a bi-level alignment optimization model is proposed, with the upper level dedicated to characterizing dominating structures and the lower level focusing on optimizing the entire alignments. To solve this bi-level model, a three-stage optimization method is designed. At the first stage, a scanning process and screening operators are devised for generating all the possible locations of dominating structures. At the second stage, a hierarchical multi-criteria decision-making procedure is applied for selecting the optimized dominating structure layouts. At the third stage, alignments are optimized based on the determined structure layouts using a bi-objective optimization method, which minimizes construction cost and geo-hazard risk simultaneously. The proposed model and solution method are applied to two real-world cases whose results verify their capabilities in producing alignment alternatives with better combinations of construction cost and geo-hazard risk than manually designed alternatives.     

平面交叉口的渠化改造

以佛山市龙江镇325国道交叉口采用平面渠化设计改造的工程实例,阐述了渠化交叉口具有适行能力大、投资省、人车分流互不干扰的特点,可大大提高车辆和行人的适行安全性。该文还就平面渠化交叉口的改造设计中应注意的问题进行了探讨,只有根据道路性质和交通量、交运流向等不同条件,选择合适的渠化处理模式,才能取得令人满意的效果。     

An Implementation of the Mode-Split Traffic-Assignment Method

A software called Optimal Traffic Signal Control System (OTSCS) was developed by us for testing the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It also was designed as a research tool to study the learning behavior of artificial neural networks and the properties of heuristic search methods. It consists of a level-of-service evaluation model that is based on an artificial neural network and a heuristic optimization model that interacts with the level-of-service evaluation model. This article discusses the latter model, named the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of two-phase traffic signals to evaluate the model's performance. Two search methods were employed: a depth-first search method (an enumeration method) and a direction-search method that the authors developed. It was found that the OTSTM with the direction search resulted in "optimal" signal timings similar to the depth-first search, which would always produce a global optimal timing. Yet the cost of the direction search, as measured by the CPU time of the computer used for analysis, was found to be much less than the cost of obtaining an optimal solution by the depth-first search cases—more than 10 times less. The study showed that once the artificial neural network is properly trained, heuristic optimal signal timing combined with artificial networks can be used as a decision-support tool for dynamic signal control. This article demonstrates how OTSTM can quickly find an optimal signal-timing solution for two-phase traffic signals.     

A sequential exploration algorithm for the design optimization of horizontal road alignment

Most computer-aided optimization procedures for horizontal alignment optimization of roads require the use of information such as horizontal points of intersection (PIs) to determine an alignment. In these methods, to obtain parameters such as the radius of the curve corresponding to a specific PI, the previous and next PIs must be known. In this paper, a sequential exploration algorithm (SEA) is proposed, and the algorithm continuously explores the entire optimization space through certain steps. Only the parameters of the previous node are required to determine the current node's parameters during the exploration process, avoiding the tight coupling between PIs in traditional optimization algorithms. Furthermore, the proposed SEA does not require assumptions about the positions and numbers of the PIs, and it can design near-optimal road alignments that match geometric restrictions and automatically take transition curves into account. Another feature of the proposed algorithm is that it directly optimizes the geometric element parameters based on the actual milepost, and it is a fully collaborative optimization approach that does not require secondary optimization nesting during the optimization process. Analyses comparing the optimization effects of different algorithms are performed on a numerical case, that is, a problem of avoiding obstacles, and two actual cases from the literature, that is, a new road design problem and an existing road reconstruction problem. It is discovered that the proposed SEA results in an approximately 3% to 10% improvement in optimization effects when compared to two current cutting-edge optimization algorithms. This work offers a new perspective on road alignment optimization by merging discrete and continuous optimizations, with a discrete component handling optimization accuracy and a continuous component handling real optimization.     

Design of water distribution networks using particle swarm optimization

Application of particle swarm optimization (PSO) is demonstrated through design of a water distribution pipeline network. PSO is an evolutionary algorithm that utilizes the swarm intelligence to achieve the goal of optimizing a specified objective function. This algorithm uses the cognition of individuals and social behaviour in the optimization process. For the optimization of water distribution system, a simulation – optimization model, called PSONET is developed and used in which the optimization is by PSO. This formulation is applied to two benchmark optimization design problems. The results are compared with the results obtained by other optimization methods. The results show that the PSO is more efficient than other optimization methods as it requires fewer objective function evaluations.     

Development and Evaluation of a Tangible Terrain Representation System for Highway Route Planning

Abstract:   In recent years, mixed or/and augmented reality, which aims to integrate virtual space with real space, has received a significant amount of attention in research and development. In particular, the concept of a tangible interface is an interesting research area in this growing field of study. This article develops and evaluates a tangible terrain representation system (TTRS), a tangible interface that recognizes the terrain in three dimensions and provides a more efficient approach to highway route planning and design. The developed system represents a terrain surface by controlling the shape of a stretchable screen using 64 actuators (8 × 8) and by projecting an aerial photograph on a screen. In applying TTRS to highway route planning, the user establishes control points using a magnetic positioning device and projects the image of a highway alignment on the TTRS. The developed system was evaluated by comparing its usability to an alternative system (virtual reality-based visual three-dimensional representation) using a group of evaluators at Miyagi University, Japan. The article concludes that TTRS is an effective tool for terrain representation and highway route planning and design .