Queueing Theory 2. Nikolaos Limnios

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from it for U(t). The random variables
and
are independent and
has an exponential distribution. Moreover,
therefore, condition 1.1 holds. Let q(t) be the total number of servers that are already busy or will be busy by service of the Q(t) customers, which are present at the system S at time t. Then q(t) as well as Q(t) is a regenerative process with a sequence
of points of regeneration that is a subsequence of
such that
Let us recall that
is a sequence of points of regeneration for X. Therefore, because of theorem 1 from (Afanasyeva and Tkachenko 2014), condition 1.4 is fulfilled. Now for any fix
we define the common points of regeneration
for the input flow X and auxiliary flow Z by the relation

      Let

      where

is the process Z for the system S that is the number of service completions by all m servers up to time t. Now we formulate the main result of this section.

      COROLLARY 1.4.– For the system S, the process Q is a stable process if and only if ρ < 1.

      takes place. Therefore condition 1.3 is fulfilled. Based on theorem 1.1, we obtain the convergence

image

      The proof is based on the approach described in Afanasyeva and Tkachenko (2014). Thus, conditions 1.1, 1.4 and 1.5 of theorem 1.2 are fulfilled and Q is a stochastically bounded process when ρ < 1. Since Q is a regenerative process, this means that it is stable.

      We see that for the model under consideration, the stability condition does not depend on the structure of the input flow. ■

      The study of traffic flows has a long history (Gideon and Pyke 1999; Grinbeerg 1959; Greenshields 1935; Inose and Hamada 1975; and references therein). Various methods such as cellular automate (Maerivoet and de Moor 2005), statistical mechanics and mathematical physics (Blank 2003; Chowdhury 1999; Fuks and Boccara 2001; Helbing 2001; Schadschneider 2000) or queueing theory (Afanasyeva and Bulinskaya 2009, 2010, 2011, 2013; Afanasyeva and Mihaylova 2015; Afanasyeva and Rudenko 2012; Baycal-Gursoy and Xiao 2004; Baycal-Gursoy et al. 2009; Caceres and Ferrari 2007) were used.

      The purpose of the proposed study is an estimation of the carrying capacity of the automobile road, intersected by a crosswalk. Under the capacity, we mean the upper bound of the intensity of the flow of cars, when the queue of cars does not tend to infinity. This means that the stability condition for the process determining the number of these cars is satisfied, so our analysis will be based on the results obtained in section 1.6.

      Let us move to the description of the models.

      First, we assume that there is no traffic light at the crossing and pedestrians have an absolute priority over cars. In this case the number of pedestrians at the crosswalk is the number of customers in the infinite-channel service system of M |G|∞ type.

      Let us assume that 2b is an average time of crossing the road by a pedestrian. Then the probability P0 that there is no pedestrian at the crosswalk in a stationary regime is defined by the expression

      [1.22]image

      Saaty (1961).

      First assume that a car can cross a pedestrian crossing only if there are no pedestrians on it. Let us assume that the cars in the lanes 1, 2, ..., m are going in one direction and the cars in the lanes m + 1,..., 2m – in another. We consider the process Q1(t) – the number of cars in the lanes 1,2,...,m at time t (the consideration of lanes m + 1,m + 2,..., 2m is analogous).

      Denote Hj(t) the mathematical expectation of the number of cars that pass through the crosswalk at the lane j during time t under the condition that there are always cars at this lane and the crosswalk is free. Also denote images In relation to the process Q1(t), we have a single-channel service system with

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