Neural-network-based cycle length design for real-time traffic control
- Title
- Neural-network-based cycle length design for real-time traffic control
- Author
- 장명순
- Keywords
- adaptive traffic controls; target volume-to-capacity (v/c) ratio; cycle; neural network; simulation
- Issue Date
- 2008-04
- Publisher
- NATL RESEARCH COUNCIL CANADA-N R C RESEARCH PRESS
- Citation
- CANADIAN JOURNAL OF CIVIL ENGINEERING, v. 35, No. 4, Page. 370-378
- Abstract
- Adaptive traffic control systems (ATCS) are designed to calculate traffic signal timings in real time to accommodate current traffic demand changes. A conventional off-line computer-based design procedure that uses iterative evaluations to select alternatives may not be appropriate for ATCS due to its unstable searching time. Search-free analytical procedures that directly find solutions have been noted for ATCS for this reason. This paper demonstrates (i) the shortcomings of an analytical cycle-length design model, specifically COSMOS, in its ability to generate satisfactory solutions at various saturation levels and (ii) an artificial neural network (ANN) based model that can overcome these shortcomings. The ANN-based model consistently yielded cycle lengths that ensure a proper operational target volume to capacity (v/c) ratio, whereas the use of the analytical model resulted in unstable target v/c ratios that might promote congestion.
- URI
- http://www.nrcresearchpress.com/doi/abs/10.1139/l07-123#.W-OA-htReUkhttps://repository.hanyang.ac.kr/handle/20.500.11754/80286
- ISSN
- 0315-1468
- DOI
- 10.1139/L07-123
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > TRANSPORTATION AND LOGISTICS ENGINEERING(교통·물류공학과) > Articles
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