Optimization of User and Operator Cost for Large-Scale Transit Network
A methodology for optimizing transit networks based on both passenger and operator costs is presented. Given information on transit demand, street network, and a set of feasibility constraints of a transit service area, the methodology searches for the transit network that best fits design goals through the minimization of a total cost objective function. The goal is to provide an effective mathematical solution procedure with minimal reliance on heuristics to solve large-scale transit network optimization problems. The methodology consists of a representation of transient route network and headway search spaces; a normalized, dimensionless total cost function; and a stochastic global search scheme that combines simulated annealing, tabu, greedy, and bisection search methods. The methodology has been tested with published benchmark problems and applied to a large-scale realistic network optimization problem. The results show that the methodology is capable of producing improved solutions to large-scale transit network design problems with reasonable computing resources.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/8674831
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Authors:
- Zhao, Fang
- Zeng, Xiaogang
- Publication Date: 2007-4
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 240-251
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Serial:
- Journal of Transportation Engineering
- Volume: 133
- Issue Number: 4
- Publisher: American Society of Civil Engineers
- ISSN: 0733-947X
- Serial URL: https://ascelibrary.org/journal/jtepbs
Subject/Index Terms
- TRT Terms: Design; Methodology; Networks; Operating costs; Optimization; Passengers; Public transit; Transit operators; Travel costs
- Subject Areas: Design; Operations and Traffic Management; Passenger Transportation; Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01046540
- Record Type: Publication
- Files: TRIS, ATRI
- Created Date: Apr 7 2007 10:27PM