D-STOP Research Spotlight: The Transportation Data Discovery Environment
Initiated in January 2015, the Transportation Data Discovery Environment (also known as the Data Rodeo) project leverages the computing resources and expertise at UT to develop a “data discovery environment” (DDE) for transportation data to aid in agency decision-making and research. Read GCN Many efforts focus on leveraging transportation data to help travelers make decisions, but less thought has gone into a framework for using big data to help transportation agency staff and decision makers. The research team began by building the DDE for the Central Texas region, in collaboration with the local MPO, the City of Austin, and the local transit agency. Initially, the project focused on creating more meaning from existing data sources; the project has grown to include more novel data sources and methods. With a web-based data platform, part of the research includes not only building the tool but developing appropriate protocols for access and governance. The research team features the diverse skill sets of D-STOP’s on-staff modelers. The DDE archives a variety of data sources, and consolidates data across multiple agencies. It also provides easy access for people to use the data to make better decisions for the welfare of the traveling public.
Led by Dr. Natalia Ruiz Juri, this project has developed/refined a number of web-based tools that facilitate the analysis of large data sets. For example, D-STOP has supported the City of Austin in the analysis of fine-grained GPS point data provided by a commercial vendor by providing a tool that allows users to draw polygons on a map and request a summary of all the trips that have points in all selected polygons. One potential application is the analysis of travel times between polygons across time periods. While this tool is not currently operating on open source data, the framework may be used to support the analysis of other GPS data sources.
This project has also partially supported the development of a web-based tool to aggregate corridor performance data from multiple sources. The later includes speed/volume data from radar-based sensors, travel time data from Bluetooth sensors, and traffic volume/turning movement counts from manual field data collection. The framework allows users to analyze data across time periods, and to understand the current availability of manually collected data. Future development will incorporate additional data sources, and develop meaningful performance indicators based on the available data. The analysis of corridor-level travel time data obtained from a commercial data provider also lead to a published paper and a magazine article. Similar data is being analyzed in a collaborative effort with the City of Austin to support the development of a systematic approach to prioritize corridors for the re-timing of traffic signals.
This project also partially support work towards developing a catalog of relevant weather data to support transportation system operations during adverse conditions, such as wildfires, flash flooding, and icing. The catalog provides a framework to which new data sources, and potentially data analysis tools, may be added.
Previously conducted work on the analysis of video data collected through traffic monitoring cameras also garnered a Smart 50 award. (The Smart 50 Awards annually recognize global smart-cities projects, honoring the most innovative and influential work.) A demonstration website is currently online to facilitate the dissemination of findings and testing of prototype analyses.
For more information on this project, please contact Dr. Natalia Ruiz Juri.