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An Artificial Intelligence (AI) Traffic Data Analysis Tool for Advanced Freeway Traffic Management

Award Information
Agency: Department of Transportation
Branch: N/A
Contract: 6913G618P800111
Agency Tracking Number: 180FH4013
Amount: $149,853.82
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 180FH4
Solicitation Number: 6913G618QSBIR1
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-10
Award End Date (Contract End Date): 2019-03-09
Small Business Information
15400 Calhoun Drive, suite 190 suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 George Zhao
 Associate Director, NDE & Intelligent Transportation
 (301) 294-5232
Business Contact
 Mark James
Title: Sr. Director, Contracts and Proposals
Phone: (301) 294-5221
Research Institution

An Artificial Intelligence (AI) Traffic Data Analysis Tool for Advanced Freeway Traffic Management 3/20/2018

Planned or unplanned traffic events cause various magnitude of traffic congestion and safety impact to road users. It was estimated that 60% of congestions are non-recurrent and 15% of incidents are secondary to the primal accidents. Thus local or regional Traffic Management Centers (TMCs) have spent tremendous labor and budget resources detecting and responding to these events. In this project, we propose to develop, deploy, and test an Artificial Intelligence (AI) based Traffic Data Analysis Tool (AI-TDA) for Advanced Traffic Management decision support. This tool, which will be built upon our existing Traffic Signal Operation Analysis Expert System (SOES) software, leverages most recent advances in AI and ML such as Convolutional Neural network (CNN), Long Short-Term Memory (LSTM) network, video/imagery intelligence processing and exploitation, multi-source “big data” analytics, and decision recommendations/support for freeway and arterial traffic incident management applications. This tool, when fully developed, will be able to collect real-time field data and make recommendations to TMC operators/engineers in response to the traffic variations in the road network.

* Information listed above is at the time of submission. *

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