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Analysis and literature review have indicated that transportation agencies often report significant percentage of missing values of traffic volumes collected at permanent data collection stations (PDCS). These missing values can be as high as 60% in some cases. Although invested significantly in legacy systems that rely primarily on inductive loop detectors to collect traffic volumes, transportation agencies are faced with a challenge of relying only on a small set of the traffic data. Despite the fact that transportation agencies are often required to report on various annual traffic statistics such as average annual daily traffic (AADT) and vehicles miles travelled, little research is available on how transportation practitioners handled missing values in their traffic data collection efforts. In this paper, a simple iterative approach is introduced--based on the integration of an imputation algorithm and a time series model--to impute missing PDCS data. The approach is deigned such that the imputation algorithm is implemented first to impute relatively small to medium gaps forming a library of complete datasets; then a time series model is fitted to these stations to form the base for imputing large gaps. The data imputation algorithm is applied on a case study on Ministry of Transportation of Ontario PDCS stations, Canada. The average errors for imputing as high as 90% of missing data--with maximum continuous gaps of 76 h--were approximately 16% with an average median error of 11 veh/h. After all traffic data are imputed, a time series model is estimated to capture the multiple seasonalities and trends in the traffic data across multiple years. The estimated 'model' is then used to calculate and produce seasonal variation factors and graphs that are typically required for planning, design, control, operation, and management of traffic and highway facilities. By estimating the model parameters using existing raw data, the model was then tested to assess its accuracy to forecast future years. Key words: missing traffic data, data imputation, trends and seasonality, time series, seasonal graphs. Les donnees publiees dans la litterature specialisee et des analyses ont montre que les agences gouvernementales en charge des transports signalent souvent un pourcentage eleve de perte de donnees relatives au debit de circulation et obtenues a l'aide de stations de collecte permanente de donnees (SCPD). Cette perte de donnees peut parfois atteindre 60 %. Bien qu'elles aient beaucoup investi dans des dispositifs d'ancienne generation, dont le fonctionnement repose principalement sur des detecteurs a boucle inductive permettant d'enregistrer les debits de circulation, les agences des transports ne peuvent se fier qu'a une petite partie des donnees collectees, ce qui constitue un probleme. En outre, meme s'il est souvent demande a ces agences de publier diverses statistiques annuelles sur la circulation, comme le debit journalier moyen annuel ou le nombre de kilometres parcourus par les vehicules, peu d'etudes ont ete menees sur la maniere dont les specialistes des transports gerent les lacunes de donnees lorsqu'ils collectent des donnees sur la circulation. Dans le present papier, une approche iterative simple est decrite, basee sur l'utilisation d'association d'un algorithme d'imputation et d'un modele de serie temporelle et permettant d'imputer les donnees des SCPD manquantes. L'approche est concue de sorte que, dans un premier temps, l'algorithme d'imputation est mis en reuvre pour imputer des intervalles de longueur assez petite a moyenne qui forment une bibliotheque d'ensembles de donnees complets; puis, une serie temporelle est adaptee aux SCPD de facon a constituer la base de l'imputation des intervalles longs. L'algorithme d'imputation de donnees est applique a une etude de cas portant sur les SCPD du ministere des Transports de l'Ontario, au Canada. Le taux d'erreur, lors d'une imputation de 90 % de donnees manquantes et pour des intervalles continus maximums de 76 heures, avoisinait 16 %, l'erreur mediane etant de 11 vehicules/heure. Apres que toutes les donnees sur la circulation ont ete imputees, un modele de serie temporelle est evalue de maniere a integrer les cyclicites et tendances variees qui transparaissent dans les donnees sur la circulation sur de nombreuses annees. Le <> evalue est ensuite utilise pour calculer et generer des facteurs de variation cyclique et des graphiques, lesquels sont generalement necessaries a la planification, l'organisation, le controle, la supervision et la gestion de la circulation et des infrastructures routieres. En determinant les parametres du modele a l'aide de donnees brutes existantes, on a pu ensuite mettre a l'essai le modele afin d'evaluer la precision des previsions qu'il a permis d'etablir pour les annees a venir. [Traduit par la Redaction] Mots-cles: donnees manquantes sur la circulation, imputation de donnees, tendance et cyclicite, serie temporelle, graphiques cycliques., Introduction Transportation agencies commit a significant portion of their resources to conduct primary traffic volume data collection efforts on the highway system such as permanent data collection station (PDCS) volumes. [...]

The online silicon vertex tracker (SVT) is the new trigger processor dedicated to the two-dimensional (2-D) reconstruction of charged particle trajectories at the Level 2 of the collider detector at Fermilab (CDF) trigger. The SVT links the digitized pulse heights found within the silicon vertex detector to the tracks reconstructed in the central outer tracker by the Level 1 fast-track finder. Preliminary tests of the system took place during the October 2000 commissioning run of the Tevatron Collider. During the April-October 2001 data taking, it was possible to evaluate the performance of the system. In this paper, we review the tracking algorithms implemented in the SVT and we report on the performance achieved during the early phase of run II. Index Terms--Collider Detector at Fermilab (CDF), data acquisition, online tracking, trigger.

Backplane buses are becoming a legacy for high-rate, high-volume data processing applications. Higher efficiency at lower cost is offered by the PCI bus technology, compared to crate-embedded processors. Becoming part of the plug&play domain of the host's operating system, no additional data transfer protocols are needed. We have combined the PCI technology with high-density field-programmable gate array (FPGA) logic and common mezzanine standards on a flexible PCI card. First applications cover readout controllers for legacy bus protocols, high-speed link I/O and fast analog input data conversion. An FPGA with embedded PCI master/target core serves as programmable interface between the PCI bus, mezzanine cards, and a local SDRAM. Adapter mezzanine cards, implemented according to the IEEE P1386 or similar common standards, are used for voltage level conversion, trigger interfacing or preprocessing. The application-dependent controller functions as well as SDRAM and PCI interfacing are handled by FPGA logic. A Linux driver was developed to achieve high bandwidth via CPU-initiated transfers. Control software for Windows and an interface for LabView target control and monitoring applications via graphical interfaces. First experience and applications will be reported. Index Terms--Data acquisition buses, detector readout architectures, DMA transactions, PCI bus.

The collectors of the data analyzed primary data, while secondary data was analyzed by persons who were not involved in the collection of the data. Secondary data was based on original data, which was found in the archive of animal cognition and was considered to be very feasible and desirable.

Analyses of life-history, ecological, and geographic trait differences among species, their causes, correlates, and likely consequences are increasingly important for understanding and conserving biodiversity in the face of rapid global change. Assembling multispecies trait data from diverse literature sources into a single comprehensive data set requires detailed consideration of methods to reliably compile data for particular species, and to derive single estimates from multiple sources based on different techniques and definitions. Here we describe PanTHERIA, a species-level data set compiled for analysis of life history, ecology, and geography of all known extant and recently extinct mammals. PanTHERIA is derived from a database capable of holding multiple geo-referenced values for variables within a species containing 100 740 lines of biological data for extant and recently extinct mammalian species, collected over a period of three years by 20 individuals. PanTHERIA also includes spatial databases of mammalian geographic ranges and global climatic and anthropogenic variables. Here we detail how the data fields are extracted and defined for PanTHERIA using a customized data input format (MammalForm); how data were collected from the literature, species names and sources tracked, error-checking and validation procedures applied, and how data were consolidated into species-level values for each variable. Tables of the consolidated species-level values are made available for each of two recent species-level taxonomic classifications of mammals, as well as associated taxonomic synonymy conversion and data-input files. This study provides a useful guide to prospective researchers on how to structure and codify life-history, ecological, geographic, and taxonomic data and methods to extract meaningful species-level traits. It also provides comprehensive information on traits like size, diet, environmental conditions, and ecology to permit macroecological and macroevolutionary analyses of this important clade. Key words: behavior; biogeography; body size; climatic variables; comparative analyses; conservation; ecology; geographic range; life history; mammals; PanTHERIA; population density.