Optimizing Urban Public Transportation with Ant Colony Algorithm / E. A. Kochegurova, E. S. Gorokhova

Уровень набора: Lecture Notes in Computer ScienceОсновной Автор-лицо: Kochegurova, E. A., specialist in the field of Informatics and computer engineering, associate Professor of Tomsk Polytechnic University, candidate of technical Sciences, 1958-, Elena AlekseevnaАльтернативный автор-лицо: Gorokhova, E. S., Ekaterina SergeevnaКоллективный автор (вторичный): Национальный исследовательский Томский политехнический университет (ТПУ), Институт кибернетики (ИК), Кафедра автоматики и компьютерных систем (АИКС)Язык: английский.Страна: .Резюме или реферат: Transport system in most cities has some problems and should be optimized. In particular, timetable of the city public transportation needs to be changed. Metaheuristic methods for timetabling were considered the most efficient. Ant algorithm was chosen as one of these methods. It was adapted for optimization of an urban public transport timetable. A timetable for one bus route in the city of Tomsk, Russia was created on the basis of the developed software. Different combinations of parameters in ant algorithm allow obtaining new variants of the timetable that better fit passengers’ needs..Примечания о наличии в документе библиографии/указателя: [References: 1 tit.].Аудитория: .Тематика: электронный ресурс | труды учёных ТПУ | алгоритм муравья | расписание | городской общественный транспорт | оптимизация Ресурсы он-лайн:Щелкните здесь для доступа в онлайн
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Transport system in most cities has some problems and should be optimized. In particular, timetable of the city public transportation needs to be changed. Metaheuristic methods for timetabling were considered the most efficient. Ant algorithm was chosen as one of these methods. It was adapted for optimization of an urban public transport timetable. A timetable for one bus route in the city of Tomsk, Russia was created on the basis of the developed software. Different combinations of parameters in ant algorithm allow obtaining new variants of the timetable that better fit passengers’ needs.

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