Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Doing Business As Name:Iowa State University
  • Anuj Sharma
  • (515) 294-3624
Award Date:11/17/2017
Estimated Total Award Amount: $ 50,000
Funds Obligated to Date: $ 50,000
  • FY 2018=$50,000
Start Date:01/01/2018
End Date:06/30/2018
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:I-Corps: Intelligent Traffic Management System
Federal Award ID Number:1800452
DUNS ID:005309844
Parent DUNS ID:005309844
Program Officer:
  • Pamular Mccauley
  • (703) 292-8950

Awardee Location

Street:1138 Pearson
Awardee Cong. District:04

Primary Place of Performance

Organization Name:Iowa State University
Street:2711 S. Loop Drive, Suite 4700
Cong. District:04

Abstract at Time of Award

The broader impact/commercial potential of this I-Corps project will be significant reductions in traffic congestion, vehicle crash risk, and fuel consumption. This will potentially have large economic benefits as traffic congestion causes significant costs to the economy. It is anticipated that intelligent traffic incident management system will be used by state departments of transportation (DOTs) to reduce the duration and impacts of incidents and improve the safety of motorists, crash victims, and emergency responders. Additional benefits to the DOTs will be: reduced personnel training needs, improved workload conditions, and increased worker retention rates. State, municipal and city agencies managing traffic will use this solution as a smart and reliable decision-assist system to monitor traffic conditions in real time, proactively control risk using advisory control, quickly detect traffic incidents, identify the location and potential cause of incidents, suggest traffic control alternatives, and minimize cognitive bottlenecks for traffic incident management operators. This I-Corps project is focused on understanding the product-market fit for intelligent traffic management systems. The proposed system uses novel machine learning techniques and graph-based trend filtering approaches for anomaly detection and state estimation for massive, spatially correlated, multi-dimensional time series data obtained from sensors that monitor the traffic networks. These approaches have been shown to be superior to the state-of-the-art approaches for detecting faulty sensors and quickly reporting traffic incidents. An advanced human-machine interface will also be provided for the Intelligent Traffic Management system with the aim to reduce the Visual, Auditory, Cognitive and Psychomotor (VACP) workload of the Traffic Incident Managers. The system data architecture uses state-of-the-art data pipelines for data ingestion, massively parallel methods for stream and batch analytics, distributed databases for scalable data storage, and GPU-augmented methods for fast data visualization of large volumes of data.

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.