Highway Layout Research Articles

Research on Collaborative Optimization Model of Tourism Resources and Highway Network Based on IoT Network and Deep Learning

With the development of tourism, people's demand and concern for tourist transportation are increasing. Integrating expressway resources into the tourism transportation network is the current trend and direction of expressway network planning. With the rapid development of the Internet, people can easily get the information resources they want, but at the same time, the problem of information overload also arises. The depth of highway access is insufficient, there is a lack of systematic consideration of tourist resources in the area, a systematic tourism network has not been developed, and tourism resources are inaccessible. These issues have stifled regional tourism's fast growth, necessitating research into the convergence of tourist resource development and highway network optimization. The building of highway layouts and the growth of the tourist sector have reached a point where they interact and impact one another, and the degree of this contact has progressively risen. However, there is still a significant disparity in the degree of cooperation across areas. This paper considers the collaborative optimization of tourism resource development and highway network optimization, analyzes its impact on traffic volume distribution, introduces the two-level programming model of collaborative optimization, and determines that the goal of the upper model is to maximize social net income. It also considers the collaborative optimization of tourism resource development and highway network optimization on the basis of clarifying the game relationship between planning decision-makers and traffic travel and considers the collaborative optimization of tourism resource development and highway network optimization on the basis of in-depth learning. For the total investment of tourism construction projects, the time cost saved by highway network optimization, and the total tourism revenue, the lower-level model aims to minimize the travel time of a single user. This paper shows that the layout planning of the regional tourism highway network from the perspective of tourism resources can improve the accessibility of regional high-quality tourism resources and meet the requirements of the coordinated development of highway tourism and resources.

Permutation test for the multivariate coefficient of variation in factorial designs

New inference methods for the multivariate coefficient of variation and its reciprocal, the standardized mean, are presented. While there are various testing procedures for both parameters in the univariate case, it is less known how to do inference in the multivariate setting appropriately. There are some existing procedures but they rely on restrictive assumptions on the underlying distributions. We tackle this problem by applying Wald-type statistics in the context of general, potentially heteroscedastic factorial designs. In addition to the k-sample case, higher-way layouts can be incorporated into this framework allowing the discussion of main and interaction effects. The resulting procedures are shown to be asymptotically valid under the null hypothesis and consistent under general alternatives. To improve the finite sample performance, we suggest permutation versions of the tests and show that the tests’ asymptotic properties can be transferred to them. An exhaustive simulation study compares the new tests, their permutation counterparts and existing methods. To further analyze the differences between the tests, we conduct two illustrative real data examples.

QANOVA: quantile-based permutation methods for general factorial designs

Population means and standard deviations are the most common estimands to quantify effects in factorial layouts. In fact, most statistical procedures in such designs are built toward inferring means or contrasts thereof. For more robust analyses, we consider the population median, the interquartile range (IQR) and more general quantile combinations as estimands in which we formulate null hypotheses and calculate compatible confidence regions. Based upon simultaneous multivariate central limit theorems and corresponding resampling results, we derive asymptotically correct procedures in general, potentially heteroscedastic, factorial designs with univariate endpoints. Special cases cover robust tests for the population median or the IQR in arbitrary crossed one-, two- and higher-way layouts with potentially heteroscedastic error distributions. In extensive simulations, we analyze their small sample properties and also conduct an illustrating data analysis comparing children’s height and weight from different countries.

Categorical Vehicle Classification and Tracking using Deep Neural Networks

The classification and tracking of vehicles is a crucial component of modern transportation infrastructure. Transport authorities make significant investments in it since it is one of the most critical transportation facilities for collecting and analyzing traffic data to optimize route utilization, increase transportation safety, and build future transportation plans. Numerous novel traffic evaluation and monitoring systems have been developed as a result of recent improvements in fast computing technologies. However, still the camera-based systems lag in accuracy as mostly the systems are constructed using limited traffic datasets that do not adequately account for weather conditions, camera viewpoints, and highway layouts, forcing the system to make trade-offs in terms of the number of actual detections. This research offers a categorical vehicle classification and tracking system based on deep neural networks to overcome these difficulties. The capabilities of generative adversarial networks framework to compensate for weather variability, Gaussian models to look for roadway configurations, single shot multibox detector for categorical vehicle detections with high precision and boosted efficient binary local image descriptor for tracking multiple vehicle objects are all incorporated into the research. The study also includes the publication of a high-quality traffic dataset with four different perspectives in various environments. The proposed approach has been applied on the published dataset and the performance has been evaluated. The results verify that using the proposed flow of approach one can attain higher detection and tracking accuracy.

Rationality and coordination: Visualizing highway network in Sichuan, China

The highway network is considered to be an effective solution for reducing regional differences and promoting high-quality economic development. This study mapped Sichuan’s highway network on an equal per capita GDP cartogram and on an equal highway freight volumes cartogram, which can provide a unique perspective to recognize the rationality of highway layouts and its coordination relationship with the population and economic development. The research result indicates that highway layouts are seriously affected by the topographical and geologic conditions, and the population and level of economic development are key influencing factors. It also shows that the density of the highway network is still seriously inadequate in the vast mountain and plateau area.

Measuring health of highway network configuration against dynamic Origin-Destination demand network using weighted complex network analysis.

Ideal configuration or layout of highways should resemble the actual demands for the roads represented by Origin-Destination (OD) information. It would be beneficial if existing highways can be evaluated for their configurational fitness against the current demands, and newly planned highways can carefully be designed in terms of their layouts and topologies that would reflect the demands. Analysis techniques used for complex networks in the matured field of network theory can be applied for the highway layout health monitoring against the current OD information. This paper proposes a methodology of measuring the fitness of existing highways by comparing their structural configuration against conceptual OD networks using well-established techniques in network theory for complex networks. In the first phase, this paper conducts an empirical analysis and finds that both structural highway network and OD network follow the “power law” distribution as they are weighted by capacity and traffic volume respectively. It is also found that the power law coefficient of the OD network dynamically changes throughout the day and week. In the second phase, a noble methodology of weighting and measuring the health, of structural highway networks against OD networks by means of comparing their power law coefficients is proposed. It is found that the proposed method is effective at detecting deviations from ideal structural configurations associated with actual demands.

Vulnerability Sensitivity of Curved Precast-Concrete I-Girder Bridges with Various Configurations Subjected to Multiple Ground Motions

Abstract Curved bridges provide a vital benefit to the nation’s infrastructure system by allowing for accessibility and ease of transition in irregular highway layouts. Curved bridges using straight prestressed concrete I-girders with curved decking can fulfill this need. A number of such bridges have been constructed both in seismically and nonseismically active zones. This paper determines the seismic vulnerability sensitivity of the bridges with vastly different configurations via use of conventional fragility-assessment techniques. Experimental design established a statistical setting using core variables to produce a wide variety of hypothetical bridges to represent both as-built and proposed bridge configurations. Each bridge was designed following the strength and serviceability limit states stipulated by current bridge design specifications. As part of the sensitivity analysis, nonlinear time history analyses (NLTHAs) using numerous synthetic ground motions were performed to establish seismic dema...

Safety analysis of passing maneuvers using extreme value theory

The increased availability of detailed trajectory data sets from naturalistic, observational, and simulation-based studies, is a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and various driving maneuvers. Despite the well-recognized research findings on both crash frequency estimation and traffic conflict analysis carried out over the last decades, only recently researchers have started to study and model the link between the two. This link is typically made by statistical association between aggregated conflicts and crashes, which still relies on crash data and ignores heterogeneity in the estimation procedure. More recently, an extreme value (EV) approach has been used to link the probability of a crash occurrence to the frequency of conflicts estimated from observed variability of crash proximity, using a probabilistic framework and without using crash records.In this study the Generalized Extreme Value distribution used in the block maxima (BM) approach and the Generalized Pareto Distribution used in the peak over threshold approach (POT), are tested and compared for the estimation of head-on collisions in passing maneuvers. The minimum time-to-collision with the opposite vehicle is used in both EV methods. Detailed trajectory data of the passing, passed and opposite vehicles from a fixed-based driving simulator experiment was used in this study. One hundred experienced drivers from different demographic strata participated in this experiment on a voluntary basis. Several two-lane rural highway layouts and traffic conditions were considered in the design of the driving simulator scenarios. Raw data was collected at a resolution of 0.1s and included the longitudinal and lateral positions, speeds and accelerations of all vehicles in the scenario. From this raw data, both methods were tested for stationary and non-stationary models. The latter allowed not-only for a better modeling performance in estimating the number of expected crashes, but also for a quantified analysis of the detailed driving choices affecting the head-on crash probability during passing maneuvers. The estimation results showed that the BM approach yielded more stable results compared to the POT approach, but the latter was able to produce crash rate estimates more consistently sensitive to the covariates of interest. Finally, the estimated distributions were validated using a second set of data extracted from an additional driving simulator experiment.The results indicate that this is a promising approach for safety evaluation. On-going work of the authors will attempt to generalize this method to other safety measures related to passing maneuvers, test it for the detailed analysis of the effect of demographic factors on passing maneuvers' crash probability and for its usefulness in a traffic simulation environment.

Advocating a Renewed Culture of Surveying Education

In recent years torrents of ink have been spent describing the pros and cons of removing the field of surveying education from the umbrella of engineering or, more precisely, civil engineering. For a time, the uproar was so loud and incessant that an orchestrated effort by professional associations—none affiliated with the American Society of Civil Engineers ASCE —successfully decreased the career academic standards for surveyors, which have continued to degenerate in the confusing impasse that we are confronting now. This impromptu ill-advised movement brought about the beliefs that the original Engineering Accreditation Commission of the Accreditation Board for Engineering and Technology—ABET EAC —was too scientifically advanced and rigorous in their curriculum requirements and that the creation of new accreditations, more technological and applied in scope, were necessary. Accordingly, we witnessed the birth of less stringent and more permissive educational certifications such as the ABET ASAC , accredited by the Applied Science Accreditation Commission. Was this a wise move? In retrospect, and restricting ourselves to the surveying profession, the answer is probably no. It is becoming perfectly clear that many of the revolutionary measuring technologies sprouting profusely these days are more in tune with the surveying engineering field than with the land surveying field. I am specifically referring to novel and broadly based scientific realms such as Global Navigation Satellite Systems GNSS , inertial navigation systems INS , terrestrial and airborne LiDAR Light Detection and Ranging , laser tracking and sophisticated terrestrial scanning devices, building information modeling BIM , and digital photogrammetry. Do these topics belong in the curriculum of a two-year technology program? The answer is an emphatic no! This limited amount of time gives opportunity to learn the subjects only superficially.—Knowing how to sequentially press instrument keys is not exactly what a professional surveying engineer should be primarily concerned with. Could we draw a time line and start again, practically from scratch, with our expectations focused on the coming decades? In my humble opinion we are compelled to scientifically invigorate the surveying engineering profession for the benefit of future generations of certified professionals. It is imperative that all states require, besides pertinent experience, a minimum of a four-year college degree in surveying engineering before a license is granted. Why emphasize surveying engineering? Because with the exception of boundary location procedures, all surveying specializations have historically been closely intertwined with civil engineering, e.g., road and highway layouts, structural deforma-

A class of rank-score tests in factorial designs

The analysis of factorial designs in a nonparametric setup has been restricted mainly to the one-way layout. Procedures for higher-way layouts are either restricted to semiparametric models or to special designs. Moreover, the continuity of the underlying distribution functions is assumed in general. The aim of this paper is to provide a general theory for the analysis of nonparametric factorial designs with fixed factors. Rank procedures for nonparametric hypotheses based on the distribution functions are proposed and the results are derived for score functions with bounded second derivatives. Unlike in most of the literature, we do not assume the continuity of the underlying distribution functions or the equality of the sample sizes. This means that data from continuous distributions as well as discrete ordinal data are covered by our approach. The results obtained are applied to some special factorial designs. For small sample sizes, the Box-approximation is applied to compute approximate p-values for the statistics. Within this framework, also the question of the rank transform property of rank statistics is briefly addressed. The application of the proposed tests is demonstrated by the analysis of real data for a two-way layout.

Measurement System Assessment via Generalized Inference

Good measurement systems are an important requirement for a successful quality improvement or statistical process control program. A measurement system is assessed by performing a designed experiment known as a gauge repeatability and reproducibility (R & R) study. Confidence intervals for the parameters which describe measurement system quality are an important part of analyzing the data from a gauge R & R study. In this paper, we show how confidence intervals can easily be obtained using the recently developed generalized inference methodology, which can be calculated by exact numerical integration or can be approximated to any desired accuracy using simulation. The methodology is demonstrated on data from two gauge R & R studies based on two-way layouts. The approach is simple and general enough to extend the results to higher-way layouts.

REACT Scatterplot Smoothers: Superefficiency through Basis Economy

Abstract REACT estimators for the mean of a linear model involve three steps: transforming the model to a canonical form that provides an economical representation of the unknown mean vector, estimating the risks of a class of candidate linear shrinkage estimators, and adaptively selecting the candidate estimator that minimizes estimated risk. Applied to one- or higher-way layouts, the REACT method generates automatic scatterplot smoothers that compete well on standard datasets with the best fits obtained by alternative techniques. Historical precursors to REACT include nested model selection, ridge regression, and nested principal component selection for the linear model. However, REACT's insistence on working with an economical basis greatly increases its superefficiency relative to the least squares fit. This reduction in risk and the possible economy of the discrete cosine basis, of the orthogonal polynomial basis, or of a smooth basis that generalizes the discrete cosine basis are illustrated by fitting scatterplots drawn from the literature. Flexible monotone shrinkage of components rather than nested 1–0 shrinkage achieves a secondary decrease in risk that is visible in these examples. Pinsker bounds on asymptotic minimax risk for the estimation problem express the remarkable role of basis economy in reducing risk.

React Scatterplot Smoothers: Superefficiency through Basis Economy

REACT estimators for the mean of a linear model involve three steps: transforming the model to a canonical form that provides an economical representation of the unknown mean vector, estimating the risks of a class of candidate linear shrinkage estimators, and adaptively selecting the candidate estimator that minimizes estimated risk. Applied to one- or higher-way layouts, the REACT method generates automatic scatterplot smoothers that compete well on standard datasets with the best fits obtained by alternative techniques. Historical precursors to REACT include nested model selection, ridge regression, and nested principal component selection for the linear model. However, REACT's insistence on working with an economical basis greatly increases its superefficiency relative to the least squares fit. This reduction in risk and the possible economy of the discrete cosine basis, of the orthogonal polynomial basis, or of a smooth basis that generalizes the discrete cosine basis are illustrated by fitting scatterplots drawn from the literature. Flexible monotone shrinkage of components rather than nested 1–0 shrinkage achieves a secondary decrease in risk that is visible in these examples. Pinsker bounds on asymptotic minimax risk for the estimation problem express the remarkable role of basis economy in reducing risk.

Geotechnical and environmental considerations in highway layouts: an integrated GIS assessment approach

Highway route layout design typically relies on aerial photographs, topographic maps and geologic maps. In this paper, a GIS platform, which incorporates the main digital data needed for evaluating route layouts, is used in a computer-based approach for highway layout assessment. Possible layouts are evaluated based on two sets of criteria. First, traditional evaluation criteria focussing on geometric design factors and impact on man-made features are considered. Next, the developed assessment framework builds on the GIS platform to generate specific environmental and geotechnical criteria for route layout evaluation. The developed approach integrates highway design, slope stability, and traffic noise modeling packages and specifically written codes with the GIS packages ARC/INFO and ArcView. A prototypical application of the assessment framework for a proposed highway south of the city of Beirut, Lebanon is presented. The results demonstrate the potential of the developed approach in incorporating new evaluation criteria at the route layout design stage and in automating the route layout assessment procedure.

Generalised bilinear regression

This paper discusses the application of generalised linear methods to bilinear models by criss-cross regression. It proposes an extension to segmented bilinear models in which the expectation matrix is linked to a sum in which each segment has specified row and column covariance matrices as well as a coefficient parameter matrix that is specified only by its rank. This extension includes a variety of biadditive models including the generalised Tukey degree of freedom for non-additivity model that consists of two bilinear segments, one of which is constant. The extension also covers a variety of other models for which least squares fits had not hitherto been available, such as higher-way layouts combined into the rows and columns of a matrix, and a harmonic model which can be reparameterised so a lower rank fit is equivalent to a constant phase parameter. A number of practical applications are provided, including displaying fits by biplots and using them to diagnose models.

Nonparametric Hypotheses and Rank Statistics for Unbalanced Factorial Designs

Factorial designs are studied with independent observations, fixed number of levels, and possibly unequal number of observations per factor level combination. In this context, the nonparametric null hypotheses introduced by Akritas and Arnold are considered. New rank statistics are derived for testing the nonparametric hypotheses of no main effects, no interaction, and no factor effects in unbalanced crossed classifications. The formulation of all results includes tied observations. Extensions of these procedures to higher-way layouts are given, and the efficacies of the test statistics against nonparametric alternatives are derived. A modification of the test statistics and approximations to their finite-sample distributions are also given. The small-sample performance of the procedures for two factors is examined in a simulation study. As an illustration, a real dataset with ordinal data is analyzed.

Nonparametric Hypotheses and Rank Statistics for Unbalanced Factorial Designs

Abstract Factorial designs are studied with independent observations, fixed number of levels, and possibly unequal number of observations per factor level combination. In this context, the nonparametric null hypotheses introduced by Akritas and Arnold are considered. New rank statistics are derived for testing the nonparametric hypotheses of no main effects, no interaction, and no factor effects in unbalanced crossed classifications. The formulation of all results includes tied observations. Extensions of these procedures to higher-way layouts are given, and the efficacies of the test statistics against nonparametric alternatives are derived. A modification of the test statistics and approximations to their finite-sample distributions are also given. The small-sample performance of the procedures for two factors is examined in a simulation study. As an illustration, a real dataset with ordinal data is analyzed.

A Unified Approach to Rank Tests for Multivariate and Repeated Measures Designs

Abstract A unified approach to asymptotic rank tests is presented for a wide class of univariate and multivariate models, including repeated measures designs without compound symmetry. The models are assumed to be balanced and complete with more than one replication per cell. The proposed rank tests are constructed by ranking all of the observations together regardless of row, column, or component membership. This method of ranking offers increased power over the method of n-rankings. The resulting test statistic is a quadratic form in linear rank statistics. Asymptotic distributions are determined under Pitman alternatives that allow for both scale and location alternatives. The resulting statistics include tests for factor effects (both scale and location differences) in one-, two-, and higher-way layouts with repeated measures on one or several factors without assuming equicorrelation. Also included are tests for the multivariate two- and k-sample problem, as well as multivariate versions of the tests for multiway layouts and repeated measures designs. For many of the repeated measures designs without equicorrelation, no other rank based statistics have been previously studied. The results also include the asymptotic distributions for many possible rank transform tests for univariate and multivariate models, as well as a rich class of aligned rank tests.

A Unified Approach to Rank Tests for Multivariate and Repeated Measures Designs

Abstract A unified approach to asymptotic rank tests is presented for a wide class of univariate and multivariate models, including repeated measures designs without compound symmetry. The models are assumed to be balanced and complete with more than one replication per cell. The proposed rank tests are constructed by ranking all of the observations together regardless of row, column, or component membership. This method of ranking offers increased power over the method of n-rankings. The resulting test statistic is a quadratic form in linear rank statistics. Asymptotic distributions are determined under Pitman alternatives that allow for both scale and location alternatives. The resulting statistics include tests for factor effects (both scale and location differences) in one-, two-, and higher-way layouts with repeated measures on one or several factors without assuming equicorrelation. Also included are tests for the multivariate two- and k-sample problem, as well as multivariate versions of the tests for multiway layouts and repeated measures designs. For many of the repeated measures designs without equicorrelation, no other rank based statistics have been previously studied. The results also include the asymptotic distributions for many possible rank transform tests for univariate and multivariate models, as well as a rich class of aligned rank tests.

On Tests of Main Effects and Interactions in Higher-Way Layouts in the Analysis of Variance Random Effects Model

It is known that in the random effect model, exact F-tests are not obtainable from the analysis of variance table for testing the main effects in a three-way layout. Similarly exact F-tests are not obtainable for the main effects and certain interactions in higher-way layouts. In this paper level-α: tests obtainable from the analysis of variance table are given for testing these effects. Tables are provided to illustrate the size function and the power function of the tests.