Abstract
In recent years, the strong pace of construction is increasing in big cities. With their growth becomes a question of the deployment of firefighters and the number of fire stations. The most effective solution is the problem of finding the optimum route of fire departments, taking into account the information transport logistics systems within the city that will allow us to arrive at the scene at any time, regardless of the degree of congestion of city roads. Prompt arrival of fire units provides the most successful fire fighting. The main objective of the study is to develop a preliminary route and the route in case of unforeseen factors affecting the time fire engine arrived. To construct the routes used to develop actively in the current methods of machine learning artificial neural networks. To construct the optimal route requires a correct prediction of the future behavior of a complex system of urban traffic based on its past behavior. Within the framework of statistical machine learning theory considered the problem of classification and regression. The learning process is to select a classification or a regression function of a predetermined broad class of such functions. After determining the prediction scheme, it is necessary to evaluate the quality of its forecasts, which are measured not on the basis of observations, and on the basis of an improved stochastic process, the result of the construction of the prediction rules. The model is verified on the basis of data collected in real departures real fire brigades, which made it possible to obtain a minimum time of arrival of fire units.
Highlights
SaikoM athematical model o f opti mizing the ar rival of fire u nits with the us e of in for mation s ys tems for mon itorin g trans port logis tics of Voro nezh city
Что мы заранее не знаем какойиз методов классификации или регрессии будет построен по наблюдаемойчасти данных в процессе обучения; нам задан целыйкласс таких методов – например, это может быть класс разделяющих гиперповерхностей в многомерном пространстве
The model is verified on the basis of data collected in real departures real fire brigades, which made it possible to obtain a minimum time of arrival of fire units
Summary
Для повышения оперативности реагирования пожарных подразделений наиболее эффективным методом является использование оптимального пути следования пожарного подразделения, с учетом прогнозирования поведения систем транспортной логистики в пределах города. Для построения маршрутов использовались активно развиваемые в настоящее время методы машинного обучения искусственных нейронных сетей. Для построения маршрутов мы используем активно развиваемые в настоящее время методы машинного обучения искусственных нейронных сетей. При котором прошлые данные или примеры используются для первоначального формирования и совершенствования схемы предсказания, называется методом машинного обучения (Machine Learning).
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