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Title:

Agent-Based Traffic Management and Reinforcement Learning in Congested Intersections

Accession Number:

01463558

Record Type:

Project

Abstract:

The loss of time and resources due to congestion, especially in urban areas, is significant. Appropriately operated traffic signals help to smooth the flow of traffic, leading to a reduction in commute time and fuel consumption. This study seeks to develop an agent-based traffic management technique with reinforcement learning principles. Agents, working independently within the same network, will learn from their environments to minimize travel time and reduce stoppage. The information produced by this innovative research will be applicable to improvements in mobility and reliability in the region.

Language:

English

Sponsor Organizations:

Research and Innovative Technology Administration

University Transportation Centers Program
Washington, DC 20590 USA

Performing Organizations:

NEXTRANS

3000 Kent Avenue
Lafayette, IN 47906 USA

Principal Investigators:

Benekohal, Rahim F

Project Status:

Active

Funding:

117786.00

Start Date:

20101001

Uncontrolled Terms:

Subject Areas:

Highways; Operations and Traffic Management

Source Agency:

Purdue University, West Lafayette

NEXTRANS
3000 Kent Avenue
West Lafayette, IN 47906 USA

Source Data:

RiP Project 28417

Files:

UTC, RiP, USDOT

Last Modified:

Aug 22 2013 10:10AM