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.     

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.     

道路线形对双车道公路通行能力的影响研究

双车道公路在选线时,由于平、纵线形指标选取的范围较大,它们之间的组合导致了双车道公路线形差异较大,通行能力也不尽相同。因此,平纵线形是影响双车道公路通行能力的主要因素。根据实测数据和已有研究,认为半径大于400m的平曲线和坡度小于3%的纵坡不影响道路的通行能力。定义有效曲度和有效梯度2个概念来分别表示平曲线的弯曲程度和竖曲线的陡峭程度,给出计算方法,并探讨其有关性质。根据仿真结果和速度差原则分别将有效曲度和有效梯度分为了7级和6级,将有效曲度和有效梯度进行组合,构造出42组不同道路线形下的双车道公路,在进行的仿真实验中,得到了相应的小客车速度-流量、流量-跟车率关系,最终得出不同道路线形条件下的双车道公路通行能力值。     

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.     

Use of Heading Direction for Recreating the Horizontal Alignment of an Existing Road

This article proposes a new method for fitting the horizontal alignment of a road to a set of (x, y) points. Those points can be obtained from digital imagery or GPS‐data collection. Unlike current methods that represent road alignment through its curvature, the proposed method describes the horizontal alignment as a sequence of headings. An analytic–heuristic approach is introduced. The proposed method produces unique solutions even for complex horizontal alignments. Some examples and a case study are presented. This solution may not be accurate enough for road redesign, but it allows researchers and departments of transportation to obtain accurate geometric features.     

An innovative approach for generation of a time location plan in road construction projects

Existing linear scheduling methods for earthwork activities lack accurate scheduling locations in road construction projects. Project planners and construction managers largely depend on subjective decisions for the allocation of resources at correct locations. This has caused uncertainties in planning and scheduling, and consequently delays and cost overruns of projects. Accurate information of working locations is vital for efficient resource planning, scheduling and equipment mobilisation. A theoretical framework for a virtual construction prototype model is developed using the theory of location‐based planning and this is used as the basis of sensitivity analysis to identify critical factors affecting road construction. An arithmetic algorithm is developed by incorporating road design data, sectional quantities, variable productivity data, unit cost, site access points and haulage distance. The model generates a time location plan automatically with the aim to provide location‐based scheduling information of earthwork activities. Weekly progress profiles, terrain surfaces, cost profiles and S‐curve are the other outputs of the model. Data and information collected from the case studies are used to demonstrate functionalities of the model. Critical factors controlling the productivity of earthwork activities such as different types of equipment, soil characteristics and site access points were used to display the sensitivity effect by means of ‘what‐if scenarios’. The model is a valuable tool in analysing impacts of different factors associated with productivity data and resource planning from location aspects in the earthwork construction projects.     

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.     

Integrated Planning of Supply Chain Networks and Multimodal Transportation Infrastructure Expansion: Model Development and Application to the Biofuel Industry

Abstract: As the biofuel industry continues to expand, the construction of new biorefinery facilities induces a huge amount of biomass feedstock shipment from supply points to the refineries and biofuel shipment to the consumption locations, which increases traffic demand in the transportation network and contributes to additional congestion (especially in the neighborhood of the refineries). Hence, it is beneficial to form public‐private partnerships to simultaneously consider transportation network expansion and biofuel supply chain design to mitigate congestion. This article presents an integrated mathematical model for biofuel supply chain design where the near‐optimum number and location of biorefinery facilities, the near‐optimal routing of biomass and biofuel shipments, and possible highway/railroad capacity expansion are determined. The objective is to minimize the total cost for biorefinery construction, transportation infrastructure expansion, and transportation delay (for both biomass/biofuel shipment and public travel) under congestion. A genetic algorithm framework (with embedded Lagrangian relaxation and traffic assignment algorithms) is developed to solve the optimization model, and an empirical case study for the state of Illinois is conducted with realistic biofuel production data. The computational results show that the proposed solution approach is able to solve the problem efficiently. Various managerial insights are also drawn. It shall be noted that although this article focuses on the booming biofuel industry, the model and solution techniques are suitable for a number of application contexts that simultaneously involve network traffic equilibrium, infrastructure expansion, and facility location choices (which determine the origin/destination of multi‐commodity flow).     

Patrol Route Planning for Incident Response Vehicles under Dispatching Station Scenarios

Traffic incidents often contribute to major safety concerns, impose additional congestion in the neighboring transportation networks, and induce indirect costs to economy. As roughly a third of traffic crashes are secondary accidents, effective incident management activities are critical, especially on roadways with high traffic volume, to detect, respond to, and clean up incidents in a timely fashion, which supports safety constraints and restores traffic capacity in the transportation network. Hence, it is beneficial to simultaneously plan for first respondents’ dispatching station location and patrol route design to mitigate congestion. This article presents an optimal route planning for patrolling vehicles to facilitate quick response to potential accidents. A mixed‐integer nonlinear program is proposed that minimizes the respondents’ patrolling travel cost based on the expected maximum response time from each arbitrary location to all incident locations (a.k.a. hotspots) with various incident occurrence probabilities. We have developed a column generation‐based solution technique to solve the route optimization model under different station design scenarios. To investigate the impact of dispatching station design on the routing cost, an integrated genetic algorithm framework with embedded continuous approximation approach is developed that reduces the complexity of the hybrid location design and route planning problem. Numerical experiments on hypothetical networks of various sizes are conducted to indicate the performance of the proposed algorithm and to draw managerial insights. The models and solution techniques, developed in this article, are applicable to a number of network problems that simultaneously involve routing and facility location choices.     

Simultaneous size and topology optimization of 3D outrigger‐braced tall buildings with inclined belt truss using genetic algorithm

To investigate the optimum location of the outrigger system, a metaheuristic‐based size and topology optimization of the outrigger‐braced tall buildings is carried out by various three‐dimensional structural frames with different shapes of belt trusses. By considering the elastic behavior, the whole elements of the structural models such as beams, columns, core, and trusses are optimized simultaneously in conjunction with the location of the outrigger. Furthermore, to reach more optimality, several novel types of belt truss are proposed having inclined and inverse‐inclined belt trusses with better structural and architectural features and optimum performance in comparison with the horizontal one. Different models with 25 to 40 stories having various span numbers are optimized using the genetic algorithm, and the results are compared with each other. In the modeling process, the exact wind load distribution is applied to the structure based on the ASCE7‐16 rather than the uniform or triangular ones. According to the results, the optimum cross‐sectional size and outrigger locations of different models are obtained, and it is indicated that the proposed novel belt trusses are optimal solution for the problem.     

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.     

A three‐dimensional distance transform for optimizing constrained mountain railway alignments

Railway alignment optimization is considered one of the most complicated and time‐consuming problems in railway planning and design. It requires searching among the infinite potential alternatives in huge three‐dimensional (3D) search spaces for a near‐optimal alignment, while considering complex constraints and a nonlinear objective function. In mountainous regions, the complex terrain and constructions require additional and more complex constraints than in topographically simpler regions. In this paper, the authors solve this problem with an algorithm based on a 3D distance transform (3D‐DT). Compared with previous two‐dimensional distance transform (2D‐DT) methods developed in this field, the feasible search spaces of 3D‐DT are greatly increased. Consequently, this new method can find more alternatives with higher qualities. In this approach, an erythrocyte‐shaped 3D neighboring mask is developed to narrow local search spaces and speed up the search process. Besides, a stepwise‐backstepping strategy is designed to dynamically determine feasible 3D search spaces and efficiently search the study area. During the 3D‐DT search process, multiple constraints, including geometric, construction, and location constraints, are effectively handled. After the 3D‐DT search, a genetic algorithm is employed to optimize the 3D‐DT paths into final alignments. Finally, this novel approach is applied to an actual case in a complex mountainous region. The comprehensive cost of the best solution generated by 3D‐DT is 16% below a manual solution produced by very experienced human designers. Furthermore, the total number of feasible alternatives found by 3D‐DT is 4.3 times greater than by 2D‐DT. The comprehensive cost of the best 3D‐DT solution is 10% below the best one generated by 2D‐DT.     

Location Optimization of Road Weather Information System (RWIS) Network Considering the Needs of Winter Road Maintenance and the Traveling Public

This study presents an innovative approach to the planning of a critical highway sensor infrastructure ‐ road weather information system (RWIS). The problem is formulated to minimize the spatially averaged kriging variance of hazardous road surface conditions while maximizing the coverage of accident‐prone areas. This optimization framework takes explicit account of the value of information from an RWIS network, providing the potential to enhance the overall efficacy of winter maintenance operations and the safety of the travelers. Spatial simulated annealing is used to solve the resulting optimization problem and its performance is demonstrated using a real‐world case study from Minnesota, United States. The case study illustrates the distinct features of the proposed model, assesses the effectiveness of the current location setting, and recommends additional stations locations. The findings of our study suggest that the proposed model could become a valuable decision‐support tool for planning a new RWIS network and evaluating the performance of alternative RWIS expansion plans.     

Two algorithms for reconstructing vertical alignments exploring the neural dynamics model of Adeli and Park

Vertical alignment reconstruction obtains alignment parameters by fitting geometric components to a set of measured points representing the profile of an existing road or railroad, which is essential in alignment consistency analysis and maintenance to ensure safety and comfort. The neural dynamics model of Adeli and Park is explored and improved for reconstructing vertical alignments with constraints. The structure of the dynamics model is modified to include three layers: parameter layer, intermediate layer, and energy layer. The number of nodes in the parameter or intermediate layers corresponds to the number of independent parameters defining a vertical alignment. The number of nodes in the energy layer is the sum of the number of deviations and the number of constraints in the alignment reconstruction problem. The coefficients connecting nodes between the parameter layer and the intermediate layer determine the integral operations, which define the Levenberg–Marquardt algorithm of the dynamics model (LMADM) and the steepest descent algorithm of the dynamics model (SDADM). Both the LMADM and SDADM methods satisfy the Lyapunov stability theorem, but the LMADM method outperforms the SDADM method in its objective function value and computation time. Experiment results demonstrate that there are multiple local optima for a vertical alignment reconstruction, and the solutions obtained by the LMADM method are the best obtained so far, compared with those reported in the literature, with 57.1% and 23.4% decreases of the mean squared error for the highway and the railroad examples, respectively.     

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.     

Intersection Modeling for Highway Alignment Optimization

Abstract:   When optimizing a highway alignment, it is desirable to consider new and modified intersections along it. This article develops methods for locally optimizing intersections within highway alignment optimization processes. Design and operational characteristics for intersections are reviewed from the literature. The formulation considers the major costs that are sensitive to intersection characteristics. Genetic algorithms are used for optimal search. The proposed methods are implemented on an artificial study area and on a real one through the use of geographic information systems. The results show how the methods work for local optimization of intersections as well as for optimizing entire alignments. These methods can be used for improving search flexibility, thus allowing more effective intersections. They also provide a basis for extending the alignment optimization from single highways to networks .     

A deep reinforcement learning approach to mountain railway alignment optimization

The design and planning of railway alignments is the dominant task in railway construction. However, it is difficult to achieve self-learning and learning from human experience with manual as well as automated design methods. Also, many existing approaches require predefined numbers of horizontal points of intersection or vertical points of intersection as input. To address these issues, this study employs deep reinforcement learning (DRL) to optimize mountainous railway alignments with the goal of minimizing construction costs. First, in the DRL model, the state of the railway alignment optimization environment is determined, and the action and reward function of the optimization agent are defined along with the corresponding alignment constraints. Second, we integrate a recent DRL algorithm called the deep deterministic policy gradient with optional human experience to obtain the final optimized railway alignment, and the influence of human experience is demonstrated through a sensitivity analysis. Finally, this methodology is applied to a real-world case study in a mountainous region, and the results verify that the DRL approach used here can automatically explore and optimize the railway alignment, decreasing the construction cost by 17.65% and 7.98%, compared with the manual alignment and with the results of a method based on the distance transform, respectively, while satisfying various alignment constraints.     

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.     

Automatic geometry measurement for curved ramps using inertial measurement unit and 3D LiDAR system

Although road geometry data can be automatically collected using instruments mounted on survey vehicle, measurement of curved ramp geometry is still of low effectiveness and accuracy due to manual or semi-automatic detection of PC (Point of Curvature)/PT (Point of Tangent) as well as influences of vehicle vibration and wandering. In this study a new method is presented for automatic measurement of ramp geometry in network level using IMU (Inertial Measurement Unit) and 3D LiDAR (Light Detection And Ranging) system. Firstly, horizontal alignment measurements are implemented: 1) an improved K-Mean clustering method and linear fitting method are integrated for automatic PC/PT station detection; 2) an algorithm is developed for automatic lane marking identification and localization for vehicle's trajectory calibration; 3) curve radius and length are measured based on roadway centerline. Subsequently, pavement slope is calibrated using IMU and transverse profiling data. Finally, nine segments are chosen from highway ramps as test bed, and validation tests are conducted using the field measurement. The test results show the average errors for curve detection and curve radius measurement are 5.89% and 1.99% respectively, and the P-value for longitudinal and cross slope measurement are 0.621 and 0.989 respectively, which indicate the proposed method is robust in ramp geometry measurement. The significant of the proposed method is three folds. First, it integrates and synchronizes the IMU and 3D LiDAR system in geometry measurement. Second, it solves the common problems of mobile survey on vehicle wandering and vibration. Third, it is of high accuracy and effectiveness, and can be used for roadway survey in network level.     

Horizontal Roadway Curvature Computation Algorithm Using Vision Technology

Abstract: Horizontal roadway curvature data are essential for roadway safety analysis. However, collecting such data is time‐consuming, costly, and dangerous using traditional, manual surveying methods. It is especially difficult to perform such manual measurement when roadways have high traffic volumes. Thus, it would be valuable for transportation agencies if roadway curvature data could be computed from photographic images taken using low‐cost digital cameras. This is the first article that develops an algorithm using emerging vision technology to acquire horizontal roadway curvature data from roadway images to perform roadway safety assessment. The proposed algorithm consists of four steps: (1) curve edges image processing, (2) mapping edge positions from an image domain to the real‐world domain, (3) calibrating camera parameters, and (4) calculating the curve radius and center from curve points. The proposed algorithm was tested on roadways having various levels of curves and using different image sources to demonstrate its capability. The ground truth curvatures for two cases were also collected to evaluate the error of the proposed algorithm. The test results are very promising, and the computed curvatures are especially accurate for curves of small radii (less than 66 m/200 ft) with less than 1.0% relative errors with respect to the ground truth data. The proposed algorithm can be used as an alternative method that complements the traditional measurement methods used by state DOTs to collect roadway curvature data.