kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университетkaz29-230Research ArticleФИЗИКО-МАТЕМАТИЧЕСКИЕ И ТЕХНИЧЕСКИЕ НАУКИPHYSICAL, MATHEMATICAL AND TECHNICAL SCIENCESВЫЯВЛЕНИЕ СКРЫТЫХ ХАРАКТЕРИСТИК МОБИЛЬНОСТИ ЛЮДЕЙ И ПРОГНОЗИРОВАНИЕ ПРИТОКА/ОТТОКА ВЕЛОСИПЕДОВ НА СТАНЦИЯХ СОВМЕСТНОГО ИСПОЛЬЗОВАНИЯEXTRACTING HIDDEN FEATURES OF HUMAN MOBILITY AND PREDICTING INFLOW AND OUTFLOW OF BIKE SHARING STATIONSСейтбековаЕ. С.SeitbekovaE. S.

лектор

АсилбековБ. К.AsilbekovB. K.

ВНС

КульджабековА. Б.KuljabekovA. B.

ВНС

БейсембетовИ. К.BeisembetovI. K.

ректор,  PhD

АО «КБТУ»КазахстанКазахский Национальный исследовательский технический университет им. К. И. СатпаеваКазахстан201912112021164171176Copyright © Сейтбекова Е.С., Асилбеков Б.К., Кульджабеков А.Б., Бейсембетов И.К., 20212021Сейтбекова Е.С., Асилбеков Б.К., Кульджабеков А.Б., Бейсембетов И.К.Seitbekova E.S., Asilbekov B.K., Kuljabekov A.B., Beisembetov I.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/230

Огромное количество пространственно-временных данных генерируется из всех типов городской инфраструктуры. Точное понимание и прогнозирование такого большого объема данных может принести пользу многим реальным приложениям. В этой статье представлен анализ данных о мобильности людей в городских районах с использованием данных со станции совместного использования велосипедов. На основе данных, взятых с веб-сайта оператора, можно определить временную и географическую мобильность в пределах города. Эти схемы используются для прогнозирования количества доступных велосипедов для любой станции на несколько часов вперед. Наша методология сначала идентифицирует и количественно определяет скрытые характеристики различных пространственных сред и временных факторов посредством тензорной факторизации. Гипотеза авторов состоит в том, что закономерности пространственно-временнойактивности сильно зависят от этих скрытых пространственно-временных особенностей. Мы моделируем это как зависимые отношения, как гауссовский процесс, который можно рассматривать как распределение.

Huge amounts of spatial-temporal data are generated daily from all kinds of citywide infrastructures. Understanding and predicting accurately such a large amount of data could benefit many real world applications. This paper provides an analysis of human mobility data in an urban area using the amount ofavailable bikes in the stations of the bicycle sharing program. Based on data sampled from the operator's website, it is possible to detect temporal and geographic mobility patterns within the city. These patterns are applied to predict the number ofavailable bikes for any station some hours ahead. Our methodology first identifies and quantifies the latent characteristics of different spatial environments and temporal factors through tensor factorization. Our hypothesis is that the patterns of spatial-temporal activities are highly dependent on or caused by these latent spatial-temporal features. We model this hidden dependent relationship as a Gaussian process, which can be viewed as a distribution over the possible functions to predict human mobility.

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The authors declare that there are no conflicts of interest present.