Evolution and Usage of the Portal Data Archive: 10-Year Retrospective

The Portal transportation data archive (http://portal.its.pdx.edu/) was begun in June 2004 in collaboration with the Oregon Department of Transportation, with a single data source: freeway loop detector data. In 10 years, Portal has grown to contain approximately 3 TB of transportation-related data from a wide variety of systems and sources, including freeway data, arterial signal data, travel times from Bluetooth detection systems, transit data, and bicycle count data. Over its 10-year existence, Portal has expanded both in the type of data that it receives and in the geographic regions from which it gets data. This paper discusses the evolution of Portal. The paper describes the new data, new regions, and new systems that have been added and how those changes have affected the archive. The paper concludes with a section on the uses of Portal that provides several examples of how Portal data have been used by regional partners, with a focus on measuring the performance of the multimodal transportation system, but also including educational elements and research

Multimodal Data at Signalized Intersections: Strategies for Archiving Existing and New Data Streams to Support Operations and Planning & Fusion and Integration of Arterial Performance Data

There is a growing interest in arterial system management due to the increasing amount of travel on arterials and a growing emphasis on multimodal transportation. The benefits of archiving arterial-related data are numerous. This research report describes our efforts to assemble and develop a multimodal archive for the Portland-Vancouver region. There is coverage of data sources from all modes in the metropolitan region; however, the preliminary nature of the archiving process means that some of the data are incomplete and samples. The arterial data sources available in the Portland-Vancouver region and that are covered in this report include data for various local agencies (City of Portland, Clark County, WA, TriMet and C-TRAN) covering vehicle, transit, pedestrian, and bicycle modes. We provide detailed descriptions of each data source and a spatial and temporal classification. The report describes the conceptual framework for an archive and the data collection and archival process, including the process for extracting the data from the agency systems and transferring these data to our multimodal database. Data can be made more useful though the use of improved visualization techniques. Thus as part of the project, a number of novel, online visualizations were created and implemented. These graphs and displays are summarized in this report and example visualizations are shown. As with any automated sensor system, data quality and completeness is an important issue and the challenge of automating data quality is large. Preliminary efforts to validate and monitor data quality and automate data quality processing are explored. Finally, the report presents efforts to combine transit and travel time data and signal timing and vehicle count data to generate some sample congestion measures

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees

Effect of Ending the Nationwide Free School Fruit Scheme on the Intake of Fruits, Vegetables, and Unhealthy Snacks in Norwegian School Children Aged 10–12 Years

The Norwegian authorities started a nationwide free school fruit program in 2007, implemented in all secondary schools (grades 8–10) and combined schools (grades 1–10) in Norway. The program ended in 2014. This study evaluates the effect of ending the nationwide free school fruit program on the consumption of fruit, vegetables, and unhealthy snacks among Norwegian sixth and seventh graders. The study sample consists of pupils at 18 schools that participated in all data collections in the Fruits and Vegetables Make the Marks project (FVMM), initiated in 2001, with new data collections in 2008 and 2018. Four of the schools were combined schools, therefore children in sixth and seventh grade at these schools received free fruit in 2008 (intervention schools), and fourteen schools did not (control schools). Between 2008 and 2018, pupils at the intervention schools ate a lower proportion of fruits and vegetables per school week, and the consumption of unhealthy snacks increased compared to the control schools. Completion of the free fruit program was not significantly different for boys and girls, or low and high parental education. The results indicate that the end of the free school fruit program resulted in less healthy eating habits among children.publishedVersio

S-Store: a streaming NewSQL system for big velocity applications

First-generation streaming systems did not pay much attention to state management via ACID transactions (e.g., [3, 4]). S-Store is a data management system that combines OLTP transactions with stream processing. To create S-Store, we begin with H-Store, a main-memory transaction processing engine, and add primitives to support streaming. This includes triggers and transaction workflows to implement push-based processing, windows to provide a way to bound the computation, and tables with hidden state to implement scoping for proper isolation. This demo explores the benefits of this approach by showing how a naïve implementation of our benchmarks using only H-Store can yield incorrect results. We also show that by exploiting push-based semantics and our implementation of triggers, we can achieve significant improvement in transaction throughput. We demo two modern applications: (i) leaderboard maintenance for a version of "American Idol", and (ii) a city-scale bicycle rental scenario

Phase 2 Comprehensive Acquisition Plan (CAP)- University of Washington ITS4US Deployment Project

693JJ321C000004This Comprehensive Acquisition Plan (CAP) provides an overview of the proposed acquisition approach for the University of Washington (UW) ITS4US project, the Transportation Data Equity Initiative (TDEI). The TDEI project is a software-focused project which will primarily deployed on the cloud. The TDEI project will acquire mobile phones that will be used to run applications that demonstrate the value of the TDEI, computers and storage to be used by TDEI staff for data processing and cloud resources for the development, deployment, and operation of the TDEI system. This document identifies the acquisition needs and requirements for the mobile phones, computers, storage, and cloud resources that will be acquired by the project

Phase 1 Integrated Complete Trip Deployment Plan - University of Washington ITS4US Deployment Project

693JJ321C000004This document presents the Integrated Complete Trip Deployment Plan (ICTDP) for the University of Washington (UW) ITS4US Deployment Project, which is developing and deploying the Transportation Data Equity Initiative (TDEI). This ITS4US Deployment Project currently is in Phase 1, planning and systems engineering development, in which the preliminary idea is developed into a structured concept that is suitable for further design, building, testing, and operation. This ICTDP summarizes the refined deployment concept developed in Phase 1 and sets forth the high-level Phase 2 (Design/Build/Test) and Phase 3 (Operate/Maintain/Evaluate) Schedule. It provides a refined Phase 1 deployment concept and the technical approach the UW ITS4US team will use for phases 2 and 3. It also provides the current deployment schedule for phases 2 and 3, along with a cost estimate for those phase