Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) utilize real-time information to apply measures improve the transportation system performance. Two key inputs for ATMS and ATIS are dynamic travel times and dynamic OD matrices. Bluetooth devices detection technology has been increasingly used to track vehicle movements on the network. This possibility naturally raises the question of whether this information can be used to improve the dynamic estimation of OD matrices. Previous research efforts rely entirely on the Bluetooth OD counts for estimation, which is why they require high penetration rates. In our study, we use Bluetooth data to supplement loop detector data while estimating dynamic OD matrices using Kalman filter. We use OD proportions as state variables and travel times, link counts, Bluetooth OD matrix and input and exit volumes as measurements. A simulation experiment is conducted in VISSIM and is designed such that the traffic network emulates the observed traffic patterns. Two case studies are performed for comparison. One uses Bluetooth OD matrices as input for estimation while the other does not. The Bluetooth ODs used in the Kalman filter estimation was found to improve the OD flow estimates. The developed methods were compared with synthetic OD estimation software (QueensOD) and were found to be more effective in obtaining dynamic OD flow estimates. A case of study with fewer detectors was also studied. When it was compared with a similar method developed by Gharat(2011), the errors were lower. / Master of Science

Most current methods used for the estimation of vehicular delay at intersections involve some form of manual data collection. These methods rely on statistical correlation to improve the accuracy of the delay estimates. In addition, most require significant data collection and reduction efforts. This work presents the theory, design, and operation of a microprocessor-based system for the collection of vehicular delay data at intersections. The hardware design is described in detail including schematic diagrams of the microprocessor system and the associated interface circuitry. Documented software listings and flowcharts are provided as well as a description of the data collection and reduction processes. A benefit/cost analysis was made based on the construction and operation of a prototype system. The system performance was evaluated both in the lab and through analysis of data collected in the field. Recommendations for further development of the device are presented as well as applications of the microprocessor to other forms of transportation and traffic engineering data. / M.S.

There are many different types of automatic data collection technologies that have been used in transportation system applications such as pneumatic tubes, radar, video cameras, inductive loops detectors, wireless toll tags, and global positioning systems (GPS). Nevertheless, there are still multiple examples of important and helpful transportation system data that still require manual data collection. In this research, the automatic transportation system data collection capabilities are expanded by enhancements in the use of wireless communications technology. In recent years, smartphones and electronic peripherals with wireless communication capabilities have become very popular. Many of these electronic devices include a Bluetooth or Wi-Fi wireless radio, whose presence in a vehicle can be used as a vehicle identifier. With wireless on-board devices available now and in the future, this research explores how roadside data collection units (DCUs) communicating with on-board devices can be used for the automated data collection of important road system data such as intersection performance data. To this end, two approaches for wirelessly collecting vehicle movement over a short road segment were explored. One approach utilized the collection and triangulation of wireless signal strength data, and demonstrated the capabilities and limitations of this approach. The second approach focused on developing methods for utilizing wireless signal strength data for vehicle point detection and identification. The vehicle point detection methods developed were applied to collect travel time data over signalized arterial roads, and to collect intersection delay data for a three way stop controlled intersection. The results from these case studies indicate a significant advantage in the proposed data collection system over the existing data collection approaches presented in the literature. / Graduation date: 2013

This thesis describes environmental data collection and transmission from the field to a server using Wi-Fi. Also discussed are components, radio wave propagation, received power calculations, and throughput tests. Measured receive power resulted close to calculated and simulated values. Throughput tests resulted satisfactory. The thesis provides detailed systematic procedures for Wi-Fi radio link setup and techniques to optimize the quality of a radio link.

Thesis: S.M., Joint Program in Applied Ocean Physics and Engineering (Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 97-107). / Mobile robots and autonomous sensors have seen increasing use in scientific applications, from planetary rovers surveying for signs of life on Mars, to environmental buoys measuring and logging oceanographic conditions in coastal regions. This thesis makes contributions in both planning algorithms and model design for autonomous scientific information gathering, demonstrating how theory from machine learning, decision theory, theory of optimal experimental design, and statistical inference can be used to develop online algorithms for robotic information gathering that are robust to modeling errors, account for spatiotemporal structure in scientific data, and have probabilistic performance guarantees. This thesis first introduces a novel sample selection algorithm for online, irrevocable sampling in data streams that have spatiotemporal structure, such as those that commonly arise in robotics and environmental monitoring. Given a limited sampling capacity, the proposed periodic secretary algorithm uses an information-theoretic reward function to select samples in real-time that maximally reduce posterior uncertainty in a given scientific model. Additionally, we provide a lower bound on the quality of samples selected by the periodic secretary algorithm by leveraging the submodularity of the information-theoretic reward function. Finally, we demonstrate the robustness of the proposed approach by employing the periodic secretary algorithm to select samples irrevocably from a seven-year oceanographic data stream collected at the Martha's Vineyard Coastal Observatory off the coast of Cape Cod, USA. Secondly, we consider how scientific models can be specified in environments - such as the deep sea or deep space - where domain scientists may not have enough a priori knowledge to formulate a formal scientific model and hypothesis. These domains require scientific models that start with very little prior information and construct a model of the environment online as observations are gathered. We propose unsupervised machine learning as a technique for science model-learning in these environments. To this end, we introduce a hybrid Bayesian-deep learning model that learns a nonparametric topic model of a visual environment. We use this semantic visual model to identify observations that are poorly explained in the current model, and show experimentally that these highly perplexing observations often correspond to scientifically interesting phenomena. On a marine dataset collected by the SeaBED AUV on the Hannibal Sea Mount, images of high perplexity in the learned model corresponded, for example, to a scientifically novel crab congregation in the deep sea. The approaches presented in this thesis capture the depth and breadth of the problems facing the field of autonomous science. Developing robust autonomous systems that enhance our ability to perform exploratory science in environments such as the oceans, deep space, agricultural and disaster-relief zones will require insight and techniques from classical areas of robotics, such as motion and path planning, mapping, and localization, and from other domains, including machine learning, spatial statistics, optimization, and theory of experimental design. This thesis demonstrates how theory and practice from these diverse disciplines can be unified to address problems in autonomous scientific information gathering. / by Genevieve Elaine Flaspohler. / S.M.

Typescript (photocopy). / Digitized by Kansas State University Libraries / Department: Electrical Engineering.

Call number: LD2668 .T4 1979 B32 / Master of Science

The purpose of the current study was to determine the efficiency of an electronic data collection method compared to a traditional paper-based method in the educational field, in terms of the accuracy of data collected and the time required to do it. In addition, data were collected to assess users' preference and system usability. The study included a sample of 20 preschool special educators from the Mailman Segal Center's Baudhuin Preschool and Autism Institute located in Davie County, Florida, who conduct daily data collection and analysis. The study used both quantitative and qualitative methods to determine answers to five research questions. These were, (1) to what extent is electronic data collection faster than traditional paper-based data collection, (2) to what extent does electronic data collection aid special education teachers and paraprofessionals to collect more accurate data than traditional paper-based data collection, (3) to what extent is the use of electronic data collection result in significant time savings relative to traditional paper-based data collection during data graphing, (4) to what extent do specialists prefer either data collection method and for what reasons, and (5) to what extent do specialists rate the usability of the handheld device used for electronic data collection. Results suggested that both formats are comparable in terms of data collection time F(1, 18) = 3.53, p = .077 and accuracy, F(1, 18) = .928, p = .348 but that electronic data graphing is faster (M = 40.4, SD = 2.17) than paper-based graphing (M = 80.4, SD = 52.61). A higher percentage of participants (60%) preferred the electronic-based data collection method due to its graphing capability and better organization of data. The electronic data collection system used in this study was found to be more usable than 86.8% of all products tested using the System Usability Scale (SUS) and, therefore, could be considered a "C" or at an "acceptable" level or "good" relative to the other 200+ systems tested using the SUS by Bangor, Kortum, and Miller (2009). The electronic-based data collection system could also be considered an "A-" based on Sauro and Lewis' (2012) scale.

Automated technologies can be beneficial for collecting data on the condition of pavements. As opposed to a traditional manual survey of the road, automated data collection can provide a safer alternative that is objective, repeatable, and consistent, while traveling at highway speeds. Though the automated method is preferred, it still needs to be reliable enough to accurately model the current pavement performance. The Texas Department of Transportation (TxDOT) initiated a project to allow an independent assessment of the accuracy and repeatability of new automated distress data measurements. In this study, 20 550-ft. pavement sections were tested with automated data collection technologies. The sections were located in Austin and Waco Districts. The accuracy and repeatability was evaluated for cracking and other distress measurements, cross slope measurements, and texture measurements. Known manual methods were used as a reference, and a 3D system developed by TxDOT was compared with three systems of other vendors (Dynatest, Fugro, and Waylink-OSU). With the data provided for the texture and cross slope, an additional investigation was done to evaluate hydroplaning potential. This thesis reports in the latter investigation. / text

The growing popularity of various Web 2.0 media has created massive amounts of user-generated content such as online reviews, blog articles, shared videos, forums threads, and wiki pages. Such content provides insights into web users' preferences and opinions, online communities, knowledge generation, etc., and presents opportunities for many knowledge discovery problems. However, several challenges need to be addressed: data collection procedure has to deal with unique characteristics and structures of various Web 2.0 media; advanced data selection methods are required to identify data relevant to specific knowledge discovery problems; interactions between Web 2.0 users which are often embedded in user-generated content also need effective methods to identify, model, and analyze. In this dissertation, I intend to address the above challenges and aim at three types of knowledge discovery tasks: (data) collection, selection, and investigation. Organized in this "CSI" framework, five studies which explore and propose solutions to these tasks for particular Web 2.0 media are presented. In Chapter 2, I study focused and hidden Web crawlers and propose a novel crawling system for Dark Web forums by addressing several unique issues to hidden web data collection. In Chapter 3 I explore the usage of both topical and sentiment information in web crawling. This information is also used to label nodes in web graphs that are employed by a graph-based tunneling mechanism to improve collection recall. Chapter 4 further extends the work in Chapter 3 by exploring the possibilities for other graph comparison techniques to be used in tunneling for focused crawlers. A subtree-based tunneling method which can scale up to large graphs is proposed and evaluated. Chapter 5 examines the usefulness of user-generated content in online video classification. Three types of text features are extracted from the collected user-generated content and utilized by several feature-based classification techniques to demonstrate the effectiveness of the proposed text-based video classification framework. Chapter 6 presents an algorithm to identify forum user interactions and shows how they can be used for knowledge discovery. The algorithm utilizes a bevy of system and linguistic features and adopts several similarity-based methods to account for interactional idiosyncrasies.