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urban-transportation-systems
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Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems
, Article SPEEDAM 2010 - International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 14 June 2010 through 16 June 2010 ; June , 2010 , Pages 1218-1221 ; 9781424449873 (ISBN) ; Fekri Moghadam, M ; Mokhtari, H ; Sharif University of Technology
2010
Abstract
Research projects in urban transportation systems require proper simulation softwares. Such softwares are the basic requirements of any kind of study in electrical transportation systems. Due to high energy consumption of these systems, studies for its optimization are challenging and necessary. In this paper an optimized timetable of trains? movement is proposed in order to completely utilize the regenerative energy of trains? braking in stations. A sample subway system is simulated and Genetic algorithm is used as an optimization method. The effect of headway and reserve times on the amount of energy consumption is studied and an optimized reserve time set is found for the sample system....
Disaggregate mode choice analysis for work trips using genetic-fuzzy and neuro-fuzzy systems
, Article 10th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2006, Palma de Mallorca, 28 August 2006 through 30 August 2006 ; 2006 , Pages 250-255 ; 0889866104 (ISBN); 9780889866102 (ISBN) ; Nazari, S ; Sharif University of Technology
2006
Abstract
This paper applies the genetic-fuzzy and neuro-fuzzy systems to analyze a disaggregate mode choice problem for work trips. These two artificial intelligence methods have been widely used in many complex engineering problems recently. Some key points in developing genetic-fuzzy and neuro-fuzzy systems to analyze the mode choice problem for a large city are explained. Shiraz urban trips database is applied to prepare the data needed for the models. Results show that the developed models have good ability to estimate the existing situation and also to present reliable predictions for the future conditions of an urban transportation system