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A short-duration vehicle classification count program provides essential data for developing a system-wide understanding of truck traffic volume. In responding to increasingly urgent truck traffic data needs, transportation agencies face the challenge of implementing improvements with constrained resources. Within this context, this paper develops and applies a decision support tool that reveals trade-offs amongst program design parameters. The Tool enables decision-makers to simultaneously consider two broad and interrelated program objectives, namely, to achieve a target level of classification count coverage and to minimize changes in resource requirements. The Tool incorporates five decision input parameters (coverage, technology type, count duration, frequency, and counting cycle) and produces information concerning count accuracy, the number of equipment units required to implement the program, the classification counting season duration, costs, and considerations such as count redundancy and data timeliness. The paper presents Manitoba's short-duration count program as a case study and evaluates three program options. Key words: short-duration classification count, traffic monitoring program, truck traffic, vehicle classification. Un programme de comptage de vehicules selon leur classification sur une courte duree fournit des donnees essentielles pour developper une comprehension a l'echelle du systeme, et ce, du volume de circulation de camions. Pour repondre aux besoins de plus en plus urgents en matiere de donnees sur la circulation des camions, les organismes de transport doivent relever le defi de mettre en oeuvre des ameliorations compte tenu de ressources limitees. Dans ce contexte, le present document elabore et applique un outil d'aide a la decision qui revele les compromis entre les parametres de conception du programme. L'outil permet aux decideurs de tenir compte simultanement de deux objectifs generaux et interrelies du programme, a savoir atteindre un niveau cible de couverture du comptage de la classification et reduire au minimum les changements dans les besoins en ressources. L'outil integre cinq parametres d'entree de decision (couverture, type de technologie, duree du comptage, frequence et cycle de comptage) et produit des donnees concernant l'exactitude du comptage, le nombre d'unites d'equipement necessaires a la mise en oeuvre du programme, la duree de la saison de comptage, les couts et les considerations comme la redondance du comptage et l'actualite des donnees produites. Le document presente le programme de comptage sur un courte duree comme une etude de cas et evalue trois options de programme. Mots-cles : comptage de classification de courte duree, programme de surveillance de la circulation, circulation des camions, classification des vehicules., Introduction This paper designs, develops, and applies a decision-support tool to evaluate options for implementing a short-duration classification count (SDCC) program. SDCC programs provide spatially-representative vehicle classification data and enable [...]

Developing and disseminating system‐wide traffic volume data are critical objectives of traffic monitoring programs. Jurisdictions commonly use maps to disseminate traffic volume data and visualize spatial traffic patterns throughout a highway network. A linear referencing system is essential in this process, yet limited research is available on the methods of sequencing (segmenting) a highway network and attributing traffic volume data to those sequences. This paper aims to fill this knowledge gap by describing the development and application of a generic three‐phase methodology: (1) to subdivide a rural highway network into sequences assumed to have uniform or homogeneous traffic volume by applying objective criteria; (2) to attribute traffic volume data to the sequenced highway network based on a set of attribution principles; and (3) to evaluate and refine the results. Application of the methodology in Manitoba resulted in the development and attribution of a new linear referencing system comprising 2,164 sequences. This provided the foundation for mapping spatial fluctuations in traffic volume at the network level. More generally, it revealed the importance of periodically evaluating the data visualization and dissemination impacts that arise when changes are made to the sampling procedure within a traffic monitoring program and when physical modifications occur on the highway network. Key Messages: Methods to subdivide a highway network and attribute traffic volume data to those subdivisions (sequences) are not well‐documented.This paper describes the development and application of a transferrable methodology to sequence and attribute traffic volume to a linear‐referenced highway network.Application of the methodology improves practitioners' ability to accurately represent spatial fluctuations in traffic volume at a network scale. [ABSTRACT FROM AUTHOR]

Abstract Background False discovery rate (FDR) control is commonly accepted as the most appropriate error control in multiple hypothesis testing problems. The accuracy of FDR estimation depends on the accuracy of the estimation of p-values from each test and validity of the underlying assumptions of the distribution. However, in many practical testing problems such as in genomics, the p-values could be under-estimated or over-estimated for many known or unknown reasons. Consequently, FDR estimation would then be influenced and lose its veracity. Results We propose a new extrapolative method called Constrained Regression Recalibration (ConReg-R) to recalibrate the empirical p-values by modeling their distribution to improve the FDR estimates. Our ConReg-R method is based on the observation that accurately estimated p-values from true null hypotheses follow uniform distribution and the observed distribution of p-values is indeed a mixture of distributions of p-values from true null hypotheses and true alternative hypotheses. Hence, ConReg-R recalibrates the observed p-values so that they exhibit the properties of an ideal empirical p-value distribution. The proportion of true null hypotheses (π0) and FDR are estimated after the recalibration. Conclusions ConReg-R provides an efficient way to improve the FDR estimates. It only requires the p-values from the tests and avoids permutation of the original test data. We demonstrate that the proposed method significantly improves FDR estimation on several gene expression datasets obtained from microarray and RNA-seq experiments. Reviewers The manuscript was reviewed by Prof. Vladimir Kuznetsov, Prof. Philippe Broet, and Prof. Hongfang Liu (nominated by Prof. Yuriy Gusev).