Анализ больших данных в геоинформационной задаче краткосрочного прогнозирования параметров транспортного потока на базе метода k ближайших соседей
Автор: Агафонов Антон Александрович, Юмаганов Александр Сергеевич, Мясников Владислав Валерьевич
Журнал: Компьютерная оптика @computer-optics
Рубрика: Численные методы и анализ данных
Статья в выпуске: 6 т.42, 2018 года.
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Точная и своевременная информация о текущем и прогнозном распределении транспортных потоков является важным фактором функционирования интеллектуальных транспортных систем. Использование этих данных позволит транспортным агентствам эффективнее решать задачу управления трафиком, участникам дорожного движения точнее планировать маршрут поездки и снизить время движения, и в целом повысит эффективность использования транспортной инфраструктуры. В данной статье представлена модель краткосрочного прогнозирования трафика, основанная на методе k ближайших соседей, которая учитывает пространственное и временное распределение транспортных потоков. Разработанная модель реализована с помощью фреймворка Apache Spark на основе модели распределённых вычислений MapReduce. Экспериментальные исследования представленной модели по данным о распределении транспортных потоков в транспортной сети города Самары позволяет сделать вывод, что предлагаемая модель обладает высокой точностью прогнозирования и временем работы, достаточным для прогнозирования в режиме реального времени.
Транспортный поток, краткосрочное прогнозирование, k ближайших соседей
Короткий адрес: https://sciup.org/140238483
IDR: 140238483 | DOI: 10.18287/2412-6179-2018-42-6-1101-1111
Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a k nearest neighbors method
Accurate and timely information on the current and predicted traffic flows is important for the successful deployment of intelligent transport systems. These data play an essential role in traffic management and control. Using traffic flow information, travelers could plan their routes to avoid traffic congestion, reduce travel time and environmental pollution, as well as improving traffic operation efficiency in general. In this paper, we propose a distributed model for short-term traffic flow prediction based on a k nearest neighbors method, that takes into account spatial and temporal traffic flow distributions. The proposed model is implemented as a MapReduce based algorithm in an Apache Spark framework. An experimental study of the proposed model is carried out on a traffic flow data in the transportation network of Samara, Russia. The results demonstrate that the proposed model has high predictive accuracy and an execution time sufficient for real-time prediction.
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