An Alignment Optimization Model for a Simple Highway Network

A new highway addition to an existing road network is typically considered for improving traffic performance in that road network. However, finding the new highway that best improves the existing network is a very complex problem since many factors affect the road construction. Besides changes in traffic flow patterns due to the new highway, various costs associated with highway construction as well as design specifications, safety, environmental, and political issues affect such a project. Until recently, many studies have dealt separately with the problems of highway alignment optimization and network design. However, no models have been found that integrate these problems comprehensively and effectively. This dissertation seeks to find a realistic three-dimensional highway alignment that best improves an existing network, while considering its costs, geometric design, and environmental impacts on the study area. To fulfill this objective, an effective network model is developed that can simultaneously optimize (i) highway alignments and (ii) junction points with existing roads. In addition, the model's optimization process considers traffic impacts due to the highway addition as well as factors associated with its construction. This dissertation starts by investigating the major cost components and important constraints in the highway design processes. Next, existing models for optimizing highway alignments are reviewed by assessing their advantages and disadvantages. Effective solution search methods are then developed to help solve the complex optimization problem. Development of the search methods is essential since an equilibrium traffic assignment as well as alignment optimization is undertaken in the proposed network model. Precise formulations of various highway costs and constraints are also developed for evaluating the various candidate alternatives. Cost functions for system improvements that can be obtained from the new highway addition are proposed. These are calculated based on the equilibrium traffic flows found from the assignment process. Complex geographical constraints including user preferences and environmentally sensitive areas are realistically represented, along with design standards required for highways. To represent highway alignments, sets of tangents, circular curves and transition spirals are used; in addition, three-leg structure models are also developed for representing the highway endpoints. Finally, several case studies are conducted to test the performance of the proposed models

Designing an ecologically optimized road corridor surrounding restricted urban areas: a mathematical methodology

The use of optimization techniques for the optimal design of roads and railways has increased in recent years. The environmental impact of a layout is usually given in terms of the land use where it runs (avoiding some ecologically protected areas), without taking into account air pollution (in these or other sensitive areas) due to vehicular traffic on the road. This work addresses this issue and proposes an automatic method for obtaining a specific corridor (optimal in terms of air pollution), where the economically optimized road must be designed in a later stage. Combining a 1D traffic simulation model with a 2D air pollution model, and using classical techniques for optimal control of partial differential equations, the problem is formulated and solved in the framework of Mixed Integer Nonlinear Programming. The usefulness of this approach is shown in a real case study posed in a region that suffers from serious episodes of environmental pollution, the Guadalajara Metropolitan Area (México)Xunta de Galicia | Ref. ED431C 2018/50Sistema Nacional de Investigadores | Ref. SNI-52768Programa para el Desarrollo Profesional Docente | Ref. PRODEP/103.5/16/806

An approach to the highway alignment development process using genetic algorithm based optimisation

Highway alignment development is recognised as a non-linear constrained optimisation problem. It is affected by many economical, social, and environmental factors subject to many design constraints. The highway alignment development process is therefore considered complex but very important. Highway alignment development is about finding an optimum alignment solution between two termini points in a 3D space, subject to several constraints. The development process using the current method is considered complex because of the number of the design elements involved, their interactions, and the formulations required to relate them to a realistic highway alignment. Moreover, it is considered that an alignment, generated using the existing method, results in a sub-optimal solution. This is due to the fact that the two alignments (horizontal and vertical alignments) are found in two independent stages and from only a handful number of alternative evaluations. This research reports on a new approach for improving the process of highway alignment development by utilising modern technologies. It proposes a novel design approach, as an alternative to the existing method, for highway alignment development in a three-dimensional space (considering the horizontal and vertical alignments simultaneously). It describes a method for highway alignment development through station points. Station points, as points along the centre line of alignment which are defined by their X, Y, and Z coordinates, are used to define the alignment configuration. The research also considers the implications of access provision (in term of junctions) and their locations on highway alignment. The environmental factors (noise and air pollution in terms of proximity distance) and accessibility (user and link construction costs in terms of access costs) are embedded in the formulations required to represent junctions in the model. The proposed approach was tested through the development of a genetic algorithms based optimisation model. To achieve this, several algorithms were developed to perform the search. The evaluation of the solutions was handled by a fitness function that includes construction (length), location (land acquisition, environmentally sensitive areas, and soil condition), and earthwork (fill and cut material) dependent costs. Other forms of costs that are quantifiable can also be incorporated within the fitness function. The critical constraints, believed important for realistic alignments (horizontal curvature, vertical curvature, and maximum gradient) are also dealt with within the model formulation. The experimental results show that the problem of highway alignment can be better represented using the concept of station points, by which better alignment solutions (global or near global solutions) were achieved. It was also shown that the alignment development process could be simplified through the use of station points, resulting in the efficient evaluation of more alternatives. Furthermore, the results conclude that a highway alignment cannot be optimum unless it is simultaneously optimised with junctions. Further investigations and development are also recommended for future studies

Identifying the shortest log trucking routes and optimizing those constrained by low-weight bridges in Mississippi

Timber haulage in Mississippi incurs the greatest portion of logging expenses because of a myriad of closed and posted (restricted) bridges. This study utilized Dijkstra\u27s algorithm method in ArcGIS Pro to derive 129 feasible shortest optimal trucking routes between 46 harvest sites and 32 softwood sawmills in Mississippi. Among these routes, 30 of them had restricted bridges along the way; however, only 13 viable alternative routes were identified due to distance and weight restrictions. The additional trucking distance for alternative routes ranged between 1.5 to 12.9 miles, whose effect on transportation cost was determined using a Mixed Integer Linear Programming optimization model incorporating weight limits of the restricted bridges. Restricted bridges along optimal routes resulted in an additional transportation cost of $4.09 million, representing a 4.07% increase in total transportation cost or 0.34 per ton of softwood sawlogs transported. All these cost increases were exclusive to softwood sawlogs

GEOMETRIC AND ENVIRONMENTAL CONSIDERATIONS IN HIGHWAY ALIGNMENT OPTIMIZATION

The highway alignment optimization problem is modeled to identify the preferred alignment alternatives which minimize total cost and satisfy the highway design standards. Several mathematical models have been developed during the past decades, among which the Highway Alignment Optimization (HAO) model has been used in several practical highway design projects with satisfactory results. However, several major cost components, such as vehicle operating cost and environmental cost are estimated roughly, and should be improved to yield more precise cost estimates and to allow optimization of lane widths. These are the HAO model features which this thesis seeks to improve. Lane width is an important factor in highway design, which is related to the travel speed, safety, as well as earthwork cost. This thesis employs Newton's method and Finite Difference method to search for the appropriate lane width. The preferred lane width found in the case study is 10.6 feet, for which the total cost is $233 million, and 12.5% less than the total cost at 12 feet lane width. In addition, this thesis improves the vehicle operating cost prediction by calculating the vehicle resistance force and horsepower, and estimating the fuel consumption based on the fuel consumption rate (g/hp-hr). Moreover, the environmental cost, particularly the vehicle emissions cost is incorporated in the newly improved HAO model. It is found that the vehicle emission cost decreases by 9% after including the environmental cost component in the model objective function. The results of the case study and sensitivity analyses indicate that the improved HAO model can find good highway alignments efficiently in tough topographic environmental. Moreover, the model can jointly consider the social, economic and environmental consequences, and result in less fuel consumption and pollutant emissions

Modeling the His-Purkinje Effect in Non-invasive Estimation of Endocardial and Epicardial Ventricular Activation

Inverse electrocardiography (iECG) estimates epi- and endocardial electrical activity from body surface potentials maps (BSPM). In individuals at risk for cardiomyopathy, non-invasive estimation of normal ventricular activation may provide valuable information to aid risk stratification to prevent sudden cardiac death. However, multiple simultaneous activation wavefronts initiated by the His-Purkinje system, severely complicate iECG. To improve the estimation of normal ventricular activation, the iECG method should accurately mimic the effect of the His-Purkinje system, which is not taken into account in the previously published multi-focal iECG. Therefore, we introduce the novel multi-wave iECG method and report on its performance. Multi-wave iECG and multi-focal iECG were tested in four patients undergoing invasive electro-anatomical mapping during normal ventricular activation. In each subject, 67-electrode BSPM were recorded and used as input for both iECG methods. The iECG and invasive local activation timing (LAT) maps were compared. Median epicardial inter-map correlation coefficient (CC) between invasive LAT maps and estimated multi-wave iECG versus multi-focal iECG was 0.61 versus 0.31. Endocardial inter-map CC was 0.54 respectively 0.22. Modeling the His-Purkinje system resulted in a physiologically realistic and robust non-invasive estimation of normal ventricular activation, which might enable the early detection of cardiac disease during normal sinus rhythm

Optimisation of speed camera locations using genetic algorithm and pattern search

Road traffic accidents continue to be a public health problem and are a global issue due to the huge financial burden they place on families and society as a whole. Speed has been identified as a major contributor to the severity of traffic accidents and there is the need for better speed management if road traffic accidents are to be reduced. Over the years various measures have been implemented to manage vehicle speeds. The use of speed cameras and vehicle activated signs in recent times has contributed to the reduction of vehicle speeds to various extents. Speed cameras use punitive measures whereas vehicle activated signs do not so their use depends on various factors. Engineers, planners and decision makers responsible for determining the best place to mount a speed camera or vehicle activated sign along a road have based their decision on experience, site characteristics and available guidelines (Department for Transport, 2007; Department for Transport, 2006; Department for Transport, 2003). These decisions can be subjective and indications are that a more formal and directed approach aimed at bringing these available guidelines together in a model will be beneficial in making the right decision as to where to place a speed camera or vehicle activated sign is to be made. The use of optimisation techniques have been applied in other areas of research but this has been clearly absent in the Transport Safety sector. This research aims to contribute to speed reduction by developing a model to help decision makers determine the optimum location for a speed control device. In order to achieve this, the first study involved the development of an Empirical Bayes Negative Binomial regression accident prediction model to predict the number of fatal and serious accidents combined and the number of slight accidents. The accident prediction model that was used explored the effect of certain geometric and traffic characteristics on the effect of the severity of road traffic accident numbers on selected A-roads within the Nottinghamshire and Leicestershire regions of United Kingdom. On A-roads some model variables (n=10) were found to be statistically significant for slight accidents and (n=6) for fatal and serious accidents. The next study used the accident prediction model developed in two optimisation techniques to help predict the optimal location for speed cameras or vehicle activated signs. Pattern Search and Genetic Algorithms were the two main types of optimisation techniques utilised in this thesis. The results show that the two methods did produce similar results in some instances but different in others. Optimised results were compared to some existing sites with speed cameras some of the results obtained from the optimisation techniques used were within proximity of about 160m. A validation method was applied to the genetic algorithm and pattern search optimisation methods. The pattern search method was found to be more consistent than the genetic algorithm method. Genetic algorithm results produced slightly different results at validation in comparison with the initial results. T-test results show a significant difference in the function values for the validated genetic algorithm (M= 607649.34, SD= 1055520.75) and the validated pattern search function values (M= 2.06, SD= 1.17) under the condition t (79) = 5.15, p=0.000. There is a role that optimisation techniques can play in helping to determine the optimum location for a speed camera or vehicle activated sign based on a set of objectives and specified constraints. The research findings as a whole show that speed cameras and vehicle activated signs are an effective speed management tool. Their deployment however needs to be carefully considered by engineers, planners and decision makers so as to achieve the required level of effectiveness. The use of optimisation techniques which has been generally absent in the Transport Safety sector has been shown in this thesis to have the potential to contribute to improve speed management. There is however no doubt that this research will stimulate interest in this rather new but high potential area of Transport Safety

The Application of Sensors on Guardrails for the Purpose of Real Time Impact Detection

The United States roadway system has deteriorated over time due to its age, increasing delays in completing preventative maintenance, and the lack of timely repairs following damage to the infrastructure. Proper asset management drives the need for generalized methods to integrate new sensing capabilities into existing Intelligent Transportation Systems in a time efficient and cost effective manner. In this thesis, we present a methodology for the deployment of new sensors into an existing ITS system. The proposed methodology employs a three phase approach that incorporates data modeling, spatial analysis in Geographic Information Systems, and cost optimization to provide enhanced decision support when deploying new sensing capabilities within an existing ITS. Additionally, we also demonstrate the usefulness of computing while integrating these new sensors using a guardrail sensor case study and focusing on data modeling. The results of the three phase methodology demonstrate an effective means for planning new sensor deployments by analyzing tradeoffs in equipment selection yielding the minimum cost solution for a given set of requirements. Furthermore, the results of the data models demonstrate necessary considerations that must be made with a systems engineering method. The data models accomplish this while accounting for asset management principles taking a systematic approach and incorporating engineering principles

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings