Моделювання логістичної системи управління швидкісними залізничними перевезеннями

Abstract

UA: Нами розглянуто технології функціонування логістичної ситуаційної моделі транспортного обслуговування населення за умови участі швидкісного залізничного транспорту. Запропонований основний принцип ефективного логістичного управління – необхідної різноманітності. Пропонуємо логістичні моделі освоєння пасажиропотоків швидкісним залізничним транспортом та корегування плану формування пасажирських поїздів. Вивчення логістики прогнозування пасажиропотоків швидкісних залізничних перевезень вказало на той факт, що у логістичній моделі вибору пасажирами варіантів реалізації запланованих поїздок бажано використовувати одночасно соціально-економічні фактори і рівень тарифів на залізничному транспорті.

EN: The research of the technology of functioning of the logistic situation model of transport service of the population is conducted, provided that the participation of high-speed rail transport is involved. The basic principle of effective logistic control is established - the necessary diversity. The logistic models of development of passenger traffic by high-speed railway and the correction of the plan for the formation of passenger trains were constructed. The study of logistics of the forecasting of passenger traffic of high-speed rail transportation pointed to the fact that in the logistic model of choice of passengers for the implementation of planned trips it is necessary to use simultaneously socio-economic factors and the level of tariffs on rail transport. It is established that the real choice of a passenger for a variant of a trip is influenced not so much by the level of tariffs for transport services, as the ratio between passenger tariffs and incomes. Since the average passenger travels only 1-2 trips in a year, the trip planning is determined by the income level not in the particular month during which the trip was made, but by the average income over the long previous period (for example, one year). Thus, in the task of logistic modeling, instead of the indicators of average per capita cash income, the minimum consumer budget and the initial tariff levels, it is necessary to use factors calculated on their basis, such as the real level of monetary incomes and the real level of tariffs in different categories of high-speed train cars. For checking the correctness of the choice of factors and assessing their impact on the quality of the logistic model, preliminary studies of the results of calculation based on the long-distance passenger traffic from the above factors were carried out. The accuracy of the simulation was determined by the degree of approaching values of passenger traffic, calculated using the model, to real passenger traffic data. факторы и уровень тарифов на железнодорожном транспорте. EN: simultaneously socio-economic factors and the level of tariffs on rail transport. It is established that the real choice of a passenger for a variant of a trip is influenced not so much by the level of tariffs for transport services, as the ratio between passenger tariffs and incomes. Since the average passenger travels only 1-2 trips in a year, the trip planning is determined by the income level not in the particular month during which the trip was made, but by the average income over the long previous period (for example, one year). Thus, in the task of logistic modeling, instead of the indicators of average per capita cash income, the minimum consumer budget and the initial tariff levels, it is necessary to use factors calculated on their basis, such as the real level of monetary incomes and the real level of tariffs in different categories of high-speed train cars. For checking the correctness of the choice of factors and assessing their impact on the quality of the logistic model, preliminary studies of the results of calculation based on the long-distance passenger traffic from the above factors were carried out. The accuracy of the simulation was determined by the degree of approaching values of passenger traffic, calculated using the model, to real passenger traffic data.