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Труды Института системного программирования РАН

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Анализ загруженности трафика на главных улицах электронного города c применением индекса перегрузки и искусственной нейронной сети (на примере города Хамедан)

https://doi.org/10.15514/ISPRAS-2020-32(3)-12

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Аннотация

Заторы на дорогах являются серьезной проблемой для электронных городов, и для решения этой проблемы необходимо анализировать пробки в городской дорожной сети. В этой статье изучается показатель эффективности транспортных средств для оценки условий дорожных сетей. В нашем исследовании исследуется плотность трафика главной дорожной сети города Хамедан на основе данных о скорости, собранных системой управления движением Хамедана. На основе этого анализа были определены индекс трафика и пиковые часы трафика. Кроме того, с использованием нейронной сети и генетического алгоритма была определена предсказуемая связь между скоростью транспортных средств и загруженностью трафика. В работе использовались данные Центра управления движением Хамедана о скорости движения транспортных средств в густонаселенных районах.

Об авторах

Мехди ШИРМОХАММАДИ
Хамеданский филиал Исламского университета Азад
Иран
Аспирант в области компьютерных программных систем в Аракском филиале Исламского университета Азада, преподаватель на факультете компьютерной инженерии Хамеданского филиала Исламского университета Азад


Мансур ЭСМАИЛПУР
Аракский филиал Исламского университета Азад
Иран
Доцент кафедры вычислительной техники и разработки программного обеспечения отделения Хамеданского филиала Исламского университета Азад


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Для цитирования:


ШИРМОХАММАДИ М., ЭСМАИЛПУР М. Анализ загруженности трафика на главных улицах электронного города c применением индекса перегрузки и искусственной нейронной сети (на примере города Хамедан). Труды Института системного программирования РАН. 2020;32(3):131-146. https://doi.org/10.15514/ISPRAS-2020-32(3)-12

For citation:


SHIRMOHAMMADI M., ESMAEILPOUR M. Analysis of Traffic Congestion in Main Streets of Electronic city using Traffic Congestion Index and Artificial Neural Network (Case Study: Hamedan City). Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2020;32(3):131-146. (In Russ.) https://doi.org/10.15514/ISPRAS-2020-32(3)-12

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ISSN 2079-8156 (Print)
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