Within these pages, readers will find information about the current practices of freight data, challenges and needs associated with freight data, and products to support freight data sharing, integration, and management.

Timely, comprehensive, and high-quality freight data support agency planning and operational efforts by identifying and aligning critical needs with infrastructure investments. Growing trends such as e-commerce, curbside pickups, and demand for warehousing and consolidation centers directly influence public sector decision-making relating to roadway operations, roadway design, maintenance activities, corridor and urban planning, and land use. Studies over the past decades have focused on improving freight data availability, sharing, and use by agencies to address specific needs.

Despite these studies, challenges to collect, share, organize, and analyze freight data within and between organizations persist. When organizations, whether public or private, collect data, it is done for a variety of reasons resulting in disparities of geographical scale, temporal granularity, data processing methods, modeling assumptions, and file sharing formats. Scarce or limited funding compounds these challenges for public sector organizations when pursuing partnerships with the private sector. 

Freight Data Needs

Based on an assessment of the various freight data challenges drawn from the literature, a set of broad freight data needs were developed. These needs cut across multiple planning and operational functions such as, long-range freight planning, system performance monitoring, modal-shift analysis, last-mile deliveries, e-commerce, truck idling, truck parking, and land-use. 

Leadership regarding freight data collection, organization, analysis, and standards

08-119 Research Products:

A Data Decision Tree for Big Data in Freight Transportation Planning and Operations

State and other government planning agencies explore big data sources to facilitate better freight transportation planning and operations decision-making. However, limited funds, differing planning horizons, proprietary information, insufficient human resources, and misinformation about available data have resulted in challenges related to the acquisition, integration, and use of freight-related big data. The freight data decision tree is an interactive tool developed to illustrate how agencies can integrate and use big data sources to address freight planning and operational use cases.

Freight Data Interoperability Framework

Previous research has highlighted the need for an interoperable freight data architecture to help strengthen multimodal freight data collection efforts; enable interoperability between multiple systems; and aid with data accuracy verification, validation, gap identification, and integration for seamless exchange of information. While attempts have been made to address differences in freight data sources, as well as data collection, querying, and fusion methodologies, most studies address specific data sources or aim to address a specific use case. The Freight Data Interoperability Framework is a simplified, generic, and robust freight data querying methodology that data enthusiasts can leverage to encourage the implementation of an interoperable freight data architecture.

Additional Resources:

Special Report 276: A Concept for a National Freight Data Program 

Proposed in 2003, this framework intends to guide the development of a national freight database, related data collection, and synthesis activities. This conceptual framework focused on increasing the linkages between different sources of data and fulfilling the major needs of a wide variety of users by capturing the important characteristics of freight movements (shipment

NCFRP Report 29: Making Trucks Count: Innovative Strategies for Obtaining Comprehensive Truck Activity Data

This report explored innovative approaches to obtaining and making comprehensive truck activity data publicly available. The study identified a number of challenges with using truck activity data, including users integrating data from multiple sources to answer critical policy questions, lack of temporal coverage, excluded commodity types in the national surveys, and the level of investment required to use and visualize the data.

Freight Data Architecture Business Process, Logical Data Model, and Physical Data Model

In 2014, TxDOT sponsored a study to integrate data from multiple sources to optimize freight transportation planning efforts in the state. The study team sought to establish data-sharing partnerships from private stakeholders and relay the lessons learned. In addition, the team developed a prototype freight data architecture with supporting descriptions and specifications.

NCFRP Report 49: Understanding and Using New Data Sources to Address Urban and Metropolitan Freight Challenges

This project developed guidance for local agencies, MPOs, and state DOTs to utilize the rapidly emerging data being collected and processed by the private sector in urban and metropolitan areas. The study defined and described new data sources; examined approaches, methods, and analytical techniques that enable agencies to better carry out their planning, programming, and operations responsibilities.

Retrieving Information from Heterogeneous Freight Data Sources to Answer Natural Language Queries

There are a multitude of freight databases, both public and private, that do not conform to any uniform standard or ontology. This makes the creation of one single authoritative ontology challenging, as the individual data sources do not and will not share a common vocabulary.

TRB Standing Committee on Freight Transportation Data

The purposes of the committee are to identify and publicize sources of and needs for data on commodity movements, international trade, freight transportation activity, and the economics and organization of establishments engaged in freight transportation; to advise data collection agencies on cost-effective means of fulfilling essential data needs; and to assist analysts and decision makers in the effective use of freight transportation data.