sapiСистемный анализ и прикладная информатика«System analysis and applied information science»2309-49232414-0481Belarusian National Technical University10.21122/2309-4923-2023-4-37-49sapi-644Research ArticleУправление техническими объектамиManagement of technical objectsАнализ алгоритмов обнаружения дорожно-транспортных инцидентов на скоростных автомагистралях, использующих стационарные детекторы транспортаAnalysis of algorithms for detecting traffic incidents on highways using stationary vehicle detectorsНавойД. B.NavoiD. V.

Навой Дмитрий Валерьевич, полковник милиции, начальник отдела дорожного движения главного управления ГАИ МВД, аспирант БНТУ

Минск

Nepei@mail.ruКапскийД. В.KapskiD. V.

Капский Денис Васильевич, доктор технических наук, доцент. Заместитель председателя ВАК Республики Беларусь

Минск

d.kapsky@bntu.byФилипповаН. В.FilippovaN. A.

Филиппова Надежда Анатольевна, доктор технических наук, доцент, профессор кафедры «Автомобильные перевозки»

Москва

umen@bk.ruПугачевИ. Н.PugachevI. N.

Пугачев Игорь Николаевич, доктор технических наук, профессор

Хабарорвск

Белорусский национальный технический университетБеларусьBelarusian National Technical UniversityBelarusВАК Республики БеларусьБеларусьHigher Attestation Commission of the Republic of BelarusBelarusМосковский государственный автомобильно-дорожный технический университетРоссияState Technical University—MADIRussian FederationХабаровский федеральный исследовательский центр Дальневосточного отделения Российской академии наукРоссияKhabarovsk Federal Research Center of the Far Eastern Branch of the Russian Academy of SciencesRussian Federation202311012024043749Copyright © Навой Д.B., Капский Д.В., Филиппова Н.В., Пугачев И.Н., 20242024Навой Д.B., Капский Д.В., Филиппова Н.В., Пугачев И.Н.Navoi D.V., Kapski D.V., Filippova N.A., Pugachev I.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://sapi.bntu.by/jour/article/view/644

Алгоритмы обнаружения инцидентов с точки зрения автоматизации можно разделить на две категории: автоматического и неавтоматического обнаружения инцидентов. Автоматические алгоритмы относятся к тем алгоритмам, которые автоматически определяют инцидент на основании данных о состоянии транспортного потока, полученных от детекторов транспорта. Неавтоматические алгоритмы или процедуры основаны на сообщениях свидетелей-людей. По функциональным признакам алгоритмы обнаружения инцидентов на алгоритмы для автомагистралей и алгоритмы для уличной сети. По методам получения данных алгоритмы обнаружения инцидентов делятся на три группы: алгоритмы, использующие данные от стационарных детекторов транспорта (индуктивные петли, радары, видеокамеры и т.д.); алгоритмы, использующие мобильные датчики (Bluetooth, wi-fi, RFID, GPS, Глонасс-датчики, транспондеры системы оплаты проезда и т.д.). алгоритмы, использующие информацию от водителей (GSM-связь, навигационные сервисы, интернет-приложения и др.). В настоящей статье рассмотрены алгоритмы, использующие данные от стационарных детекторов транспорта. К недостаткам алгоритмов обнаружения инцидентов, использующих стационарные детекторы транспорта, следует в отнести: необходимость установки и эксплуатации детекторов транспорта (индуктивных, видео и т.д.) приводит к помехам для транспортного потока и иногда к временному закрытию движения; место установки детекторов транспорта, частота их установки и количество являются критически важными с точки зрения обнаружения инцидента на том или ином участке магистрали. Однако крайне трудоемко и капиталоемко установить стационарные детекторы по всей длине магистрали. Также индуктивные детекторы транспорта, которые в основном используются для определения параметров транспортных потоков на автомагистралях, являются ненадежными и часто выходят из строя, что делает не эффективным обнаружение инцидентов на том или ином участке дороги. К достоинствам рассматриваемых алгоритмов следует отнести подтвержденная на протяжении десятилетий надежность и точность в определении инцидентов, что является их несомненным преимуществом по сравнению с алгоритмами, использующими мобильные датчики или информацию от водителей.

Incident detection algorithms from an automation point of view can be divided into two categories: automatic and non-automatic incident detection. Automatic algorithms refer to those algorithms that automatically identify an incident based on traffic flow data received from traffic detectors. Manual algorithms or procedures rely on reports from human witnesses. Based on functional characteristics, incident detection algorithms are divided into algorithms for highways and algorithms for street networks. Based on data acquisition methods, incident detection algorithms are divided into three groups: algorithms using data from stationary vehicle detectors (inductive loops, radars, video cameras, etc.); algorithms using mobile sensors (Bluetooth, wi-fi RFID, GPS, Glonass sensors, toll system transponders, etc.). algorithms that use information from drivers (GSM communications, navigation services, Internet applications, etc.). This article discusses algorithms that use data from stationary vehicle detectors. The disadvantages of incident detection algorithms using stationary transport detectors include: the need to install and operate transport detectors (inductive, video, etc.) leads to interference with traffi fl and sometimes to temporary closure of traffic The location of installation of vehicle detectors, the frequency of their installation and the number are critical from the point of view of detecting an incident on a particular section of the highway. However, it is extremely labor and capital-intensive to install stationary detectors along the entire length of the highway. Also, inductive vehicle detectors, which are mainly used to determine the parameters of traffic flow on highways, are unreliable and often fail, which makes it ineffective to detect incidents on a particular section of the road. The advantages of the algorithms under consideration include their proven reliability and accuracy in identifying incidents over decades, which is their undoubted advantage over algorithms that use mobile sensors or information from drivers.

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