The application of genetic algorithms and knowledge-based systems to dynamic traffic signal control

Environmental effects of road usage are becoming increasingly important and these concerns have catalysed interest in promoting the use of public transport, encouragement of bicycle and pedestrian traffic and the search for methods to reduce apparent needless delays experienced by motorists at signalised intersections. Dynamic traffic signal control systems offer greater flexibility by using contemporary computer software tools such as genetic algorithms (GAs) and knowledge-based systems (KBSs). Dynamic control using knowledge-based systems and genetic algorithm technology can consider current flow demand information, site-dependent information, accident occurrence data, flow predictions and meteorological conditions, and use a wide range of weight-adjustable performance parameters as goals of the optimisation process. Genetic algorithms are used for the optimisation. This approach is not goal-oriented towards 'the optimum' for any given situation. The advantage of the method is that the most optimal arrangement need not be found due to the stochastic nature of the input parameters. The inter-relationships between the measures of intersection performance are not linear therefore, depending on sensed or perceived conditions, each is assigned a weight that is varied by a knowledge-based system.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/869106406
• Authors:
  • Clement, S J
  • Taylor, M A
• Conference:
  • International Symposium on Highway Capacity, 2nd, 1994, Sydney, New South Wales, Australia
• Publication Date: 1994

Language

• English

Media Info

• Pagination: 193-202
• Serial:
  • Volume: 1

Subject/Index Terms

• TRT Terms: Computer programs; Expert systems; Traffic control; Traffic flow
• ATRI Terms: Area traffic control; Computer program; Knowledge based system; Traffic flow
• Subject Areas: Operations and Traffic Management;

Filing Info

• Accession Number: 01434824
• Record Type: Publication
• Source Agency: ARRB
• ISBN: 869106406
• Files: ATRI
• Created Date: Aug 24 2012 6:37PM