Читать книгу People Flow in Buildings - Marja-Liisa Siikonen - Страница 37

Early theoretical studies have already recognized that elevators give limitations to passenger traffic. Passenger arrivals in bulks in a single‐server elevator system were studied by Bailey (1954). The passenger capacity, c, of an elevator limits the number of passengers that can be transported with one server. Later, Miller (1959) studied the single‐elevator system where passengers arrived in batches rather than individually. According to Miller, the theory based on individual passenger arrivals cannot be applied to an elevator system.

Kuusinen (2008) revised the study of Alexandris for passenger arrival process in a multi‐storey office building using digital video recordings. He used video footage together with the data that were simultaneously logged by the elevator monitoring system. The passenger data included time stamps of passenger arrival times, and their arrival and destination floors. The video recordings were synchronized with the monitored data. Additional data of the passengers’ social behaviour were collected from the staff with a questionnaire. The study was made in an office building with two entrances where video cameras were placed on both elevator entrance floors (Figure 3.6).

Figure 3.6 A snapshot of a video recording from the first entrance floor

(Source: Kuusinen et al. (2012). © SAGE Publications, Inc.).

People arriving in and departing from the elevator lobby were categorized into groups of one, two, three, etc. persons. In the study, a passenger batch is defined as people travelling together with their friends. Passenger batch consists of people who

1 arrive in the elevator lobby at the same time

2 arrive in the lobby from the same direction

3 enter the same elevator

4 travel together with the same elevator

5 exit the elevator on the same destination floor.

The measurement data were analysed especially for the morning up‐peak, beginning and end of lunch hour and for the evening down‐peak. The test period was partitioned into minute intervals to guarantee a sufficient number of observations. According to the questionnaires, the average batch size was 1.5, 3.3, 3.7 and 1.3 passengers for morning up‐peak, lunch down‐peak, lunch up‐peak and evening down‐peak, respectively. The batch size distributions from the video recordings showed that in the morning, about 80% of passengers arrived individually with a batch size of 1.1 persons (Kuusinen et al. 2012). Figure 3.7 shows the cumulative number of individual and batch arrivals at the entrance floor during lunch hour. When returning from lunch time, about 50% of the arrivals were batches of two or more persons with the average size of 1.5 persons.

Figure 3.7 Cumulative number of batch and individual arrivals at the entrance floor during lunch hour

(Source: Kuusinen et al. (2012). © 2012, SAGE Publications).

Kuusinen applied the test of Brown et al. (2005) to find out whether the batch arrivals could be modelled with a Poisson process (Kuusinen 2015). The conclusion of the test was that in the morning up‐peak, passenger batch arrivals follow a Poisson process better than the individual passenger arrivals. Batch arrivals follow a Poisson process also during lunch hour, which was not the case for individual passenger arrivals.