TACC’s Rustler and XSEDE ECSS Support Assist With Analyzing Data for Transportation Systems
Feb. 13 — In the next 10 years you are going to see some form of autonomous or connected vehicles on the streets. Natalia Ruiz-Juri, a research associate with The University of Texas at Austin’s Center for Transportation Research (CTR) is fairly certain of this. She is one of many researchers at CTR and The University of Texas at Austin (UT Austin) who are studying the wide range of technical, social and policy aspects of connected and autonomous vehicle (CAV) technologies.
Fully autonomous vehicles or driverless cars are capable of sensing their environment and navigating without human input. They can detect surroundings using a variety of techniques such as radar, lidar, GPS, odometry, and computer vision. Similarly, connected vehicles (CVs) are vehicles that can exchange messages containing location and other safety-related information with other vehicles, and with devices affixed to roadside infrastructure.
CVs share information in the form of Basic Safety Messages (BSMs) with other vehicles and the infrastructure; these include vehicle position, speed and breaking status. Such real-time feedback and information exchange between vehicles is expected to greatly enhance safety, and it opens the door to several possibilities in traffic management.
For example, vehicles could talk to other vehicles that are much further ahead and get warned about congestion or dangerous conditions, thereby allowing a driver to make strategic decisions and take a different path.
Additionally, vehicles could also talk to infrastructure, such as an intersection light, which might be capable of tracking the number of vehicles passing through and potentially adjusting the signal timing plan accordingly. The advent of CVs would therefore have huge promise in improving traffic management and the overall utilization of transportation infrastructure, particularly if vehicle connectivity is considered along with automation.
While the basic goal of CVs, in particular, is safety — experts hypothesize up to 80 percent less accidents in the future — the data generated by CVs has an enormous potential to support transportation planning and operations.
The Big Data Problem
At this point researchers are still exploring diverse datasets. A number of connected vehicle test beds and autonomous vehicles test sites have been planned, or are already in place. Texas is part of one of the 10 US-Department of Transportation-designated autonomous vehicle proving grounds, and research sponsored by other agencies, such as TxDOT and the North Central Texas Council of Governments is also happening at UT Austin.
“The volume and complexity of CV data are tremendous and present a big data challenge for the transportation research community,” Ruiz-Juri said. While there is uncertainty in the characteristics of the data that will eventually be available, the ability to efficiently explore existing datasets is paramount.
Ruiz-Juri and her colleagues, including Chandra Bhat, James Kuhr and Jackson Archer, were interested in exploring the most comprehensive data set released to date — the Safety Pilot Model Deployment (SPMD) data, produced by a study conducted by The University of Michigan Transportation Research Institute and the National Highway Traffic Safety Administration.
The entire article can be found here.
Source: Faith Singer-Villalobos, TACC