This report summarizes the state of practice in freight data interoperability in the areas of data processing, data fusion, and data querying; presents a proposed freight data querying methodology; and demonstrates the querying methodology for various use cases. The paper also presents a suggested approach for reconciling differences in location and vehicle classification, as well as an approach to addressing vehicle routes, inferred from the start and end locations.
The proposed querying methodology assumes four base parameters for most freight data sources: location, time, vehicle classification, and use case. The tool infers other parameters, such as route and geographical extent, from the start and end locations specified as part of the data query. The proposed methodology was tested using the following use cases:
- Origin-Destination Analysis – traffic volumes, carloads.
- Congestion Analysis – traffic delay, delay per mile, travel time index, travel time reliability, congestion cost.
- Safety Analysis – crash counts, manner of collision, weather conditions, time of day, surface conditions, light conditions, contributing factors.
- Bridge Condition and Vertical Clearance Analysis – count of bridges that are structurally deficient, functionally obsolete, or do not meet vertical clearance requirements.
- Pavement Condition Assessment – International Roughness Index, distress score
- Socio-Economic Analysis: population, median household income, gross domestic product (GDP), labor force trends, employment information
The goal was to identify commonalities and differences in data elements when working with multiple freight data sources.
A web-based geographical analysis and data querying tool was developed and employed to query multiple freight-related databases using the base parameters. The validity of the proposed methodology was evaluated by assessing the outcomes of the queries.
Multiple public and private databases were used in the analysis, including origin-destination, point location datasets such as crashes and bridges; route-based datasets such as the National Performance Measures Research Data Set (NPMRDS); and geographic area datasets such as population data from the U.S. Census Bureau or gross domestic product (GDP) data from the U.S. Bureau of Economic Analysis.
The use cases illustrate the feasibility of resolving some of the differences in data element definitions and the shift towards interoperability in freight data sources.
Potential next steps include developing a complete web-based application using the methodologies developed from this study and enhancing the recommendations provided through testing of additional use cases.
- Log in to post comments