This research focuses on establishing a systematic non-survey-based method for developing an origin-destination (OD) matrix of freight flows based on secondary data sources. The estimated freight flows and concurrent passenger volumes are loaded onto the regional highway network in the greater Los Angeles area of Southern California. Economic analyses, modeling, and GIS technologies are integrated into building a GIS-based OD matrix for freight flow. In order to load the freight flows onto the regional highway network, a three-step feedback transportation model is developed. It includes trip generation, trip distribution, and traffic assignment. A doubly-constrained gravity model is used to co-distribute and calibrate personal trips and freight trips in the trip distribution step. A version of User-Optimal-Strict On Network Assignment (UO-S-NA) is used to assign all of the vehicle trips to the regional highway network.
As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoreti
The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive national picture of freight movements among states and major metropolitan areas by all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns the flows to the transportation network, and projects freight flow patterns into the future. The FAF4 is the fourth database of its kind, FAF1 provided estimates for truck, rail, and water tonnage for calendar year 1998, FAF2 provided a more complete picture based on the 2002 Commodity Flow Survey (CFS) and FAF3 made further improvements building on the 2007 CFS. Since the first FAF effort, a number of changes in both data sources and products have taken place. The FAF4 flow matrix described in this report is used as the base-year data to forecast future freight activities, projecting shipment weights and values from year 2020 to 2045 in five-year intervals. It also provides the basis for annual estimates to the FAF4 flow matrix, aiming to provide users with the timeliest data. Furthermore, FAF4 truck freight is routed on the national highway network to produce the FAF4 network database and flow assignments for truck. This report details the data sources and methodologies applied to develop the base year 2012 FAF4 database. An overview of the FAF4 components is briefly discussed in Section 2. Effects on FAF4 from the changes in the 2012 CFS are highlighted in Section 3. Section 4 provides a general discussion on the process used in filling data gaps within the domestic CFS matrix, specifically on the estimation of CFS suppressed/unpublished cells. Over a dozen CFS OOS components of FAF4 are then addressed in Section 5 through Section 11 of this report. This includes discussions of farm-based agricultural shipments in Section 5, shipments from fishery and logging sectors in Section 6. Shipments of municipal solid wastes and debris from construction and demolition activities are covered in Section 7. Movements involving OOS industry sectors on Retail, Services, and Household/Business Moves are addressed in Section 8. Flows of OOS commodity on crude petroleum and natural gas are presented in Sections 9 and 10, respectively. Discussions regarding shipments of foreign trade, including trade with Canada/Mexico, international airfreight, and waterborne foreign trade, are then discussed in Section 11. Several appendices are also provided at the end of this report to offer additional information.
The new edition of the classic bestseller that launched thedata warehousing industry covers new approaches and technologies,many of which have been pioneered by Inmon himself In addition to explaining the fundamentals of data warehousesystems, the book covers new topics such as methods for handlingunstructured data in a data warehouse and storing data acrossmultiple storage media Discusses the pros and cons of relational versusmultidimensional design and how to measure return on investment inplanning data warehouse projects Covers advanced topics, including data monitoring andtesting Although the book includes an extra 100 pages worth of valuablecontent, the price has actually been reduced from $65 to $55
Calls upon the Department of Transportation and its Bureau of Transportation Statistics to take the lead in coordinating freight data collection in the US. This work focuses on increasing the linkages between sources of data and filling data gaps to develop a source of timely and reliable data on freight flows. A national framework is needed.
"Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.
