Integrated Feedback Control of Urban Freeway Networks via Nonlinear Optimization
The challenging problem of integrated control of urban freeway network traffic using ramp metering and route guidance is addressed. The integrated control problem is formulated as a dynamic, nonlinear, discrete-time optimal control problem with constrained control variables, with the process (network traffic behavior) being modeled by a second-order continuum model based nonlinear network traffic model. The numerical solution is achieved heuristically by use of the evolutionary computation technique "particle swarm optimization (PSO)". Feedback control is realized within a rolling horizon framework, i.e. solving the optimal control problem for each control interval over a sufficiently long future time horizon. Simulation studies are conducted for a hypothetical example network to investigate the performance of the proposed integrated control strategy in terms of control benefits, robustness, and computational effort.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Gan, HongCheng
- Yun, Wei
- BingQuan, Fan
- Yin, Han
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Conference:
- Transportation Research Board 86th Annual Meeting
- Location: Washington DC, United States
- Date: 2007-1-21 to 2007-1-25
- Date: 2007
Language
- English
Media Info
- Media Type: CD-ROM
- Features: Figures; References; Tables;
- Pagination: 24p
- Monograph Title: TRB 86th Annual Meeting Compendium of Papers CD-ROM
Subject/Index Terms
- TRT Terms: Discrete systems; Feedback control; Freeway operations; Freeways; Integrated systems; Operations; Ramp metering; Ramps (Interchanges); Route guidance; Traffic control
- Uncontrolled Terms: Particle swarm optimization; Road networks
- Subject Areas: Administration and Management; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01049537
- Record Type: Publication
- Report/Paper Numbers: 07-0892
- Files: TRIS, TRB
- Created Date: Feb 8 2007 5:28PM