No abstract

This article presents a study on the process of how passengers arrive at lift lobbies to travel to their destinations. Earlier studies suggest that passengers arrive at the lift lobbies individually with exponentially distributed inter-arrival times, that is, according to a Poisson process. This study was carried out in a multi-storey office building. The data was collected using a questionnaire, digital video recordings and the lift monitoring system. The results show that, in the studied building, passengers arrive in batches whose size varies with the time of day and the floor utilization. In addition, the batch arrivals follow a time-inhomogeneous Poisson process with piecewise constant arrival rates. Practical applications: This article contributes to the basic understanding of passenger behaviour, and how people move around in buildings and arrive at the lift lobbies. It is proposed that the model for the passenger arrival process should take into account that passengers do not always arrive individually but also in batches. The passenger arrival process affects the design of elevators. It will also affect the passenger generation in building traffic simulations.

This dissertation studies people flow in buildings, especially the process of how passengers arrive at elevator lobbies, estimation of elevator passenger traffic, and human behaviour and decision making in evacuations. The arrival process is studied by taking into account, for the first time, that passengers do not always arrive and use elevators individually but rather in batches. The results suggest that the common assumption that individual arrivals follow a Poisson distribution may not hold when the proportion of batch arrivals is large.To estimate the elevator passenger traffic in a building, new mathematical models and algorithms are developed. The new methods are based on mathematical optimization, namely, linear programming, integer least squares and constraint programming. The results from numerical experiments show that the new approaches satisfy real-time elevator control requirements. In addition, randomized algorithms result in better quality passenger traffic statistics than traditional deterministic algorithms. The dissertation presents also an experimental evacuation study. The results show that people may not be able to select the fastest exit route and that cooperation may slow down the evacuation.The new estimation models and algorithms presented in this dissertation enable better elevator control and some of them are already being implemented by KONE Corporation. The results also give new insights into the process of how passengers arrive at the elevator lobbies and use elevators and into human behaviour in evacuation situations, which affect elevator and building safety planning. Tiivistelmä Tässä väitöskirjassa tutkitaan rakennusten henkilöliikennettä, erityisesti henkilöiden saapumisprosessia hissiauloihin, hisseillä tapahtuvan henkilöliikenteen estimointia sekä henkilöiden käyttäytymistä ja päätöksentekoa rakennuksen evakuoinnissa. Saapumisprosessia tutkitaan ottaen ensimmäistä kertaa huomioon, että henkilöt eivät aina saavu ja käytä hissiä yksin vaan isommissa joukoissa. Tulokset osoittavat, että yleinen olettamus yksittäisten saapumisten Poisson jakautuneisuudesta ei välttämättä päde, kun joukkosaapumisten osuus on suuri.Rakennuksen henkilöliikenteen estimoimiseksi väitöskirjassa kehitetään matemaattisia malleja ja algoritmeja, jotka perustuvat lineaariseen optimointiin, pienimmän neliösumman kokonaislukuoptimointiin ja rajoiteoptimointiin. Numeeristen testien tulokset osoittavat, että menetelmät toteuttavat todellisen hissiohjauksen reaaliaikavaatimukset. Lisäksi satunnaistetut algoritmit tuottavat laadultaan parempia henkilöliikennetilastoja kuin perinteiset deterministiset algoritmit. Väitöskirjassa esitetään myös kokeellinen evakuointitutkimus, jonka tulokset osoittavat, että ihmiset eivät välttämättä kykene valitsemaan nopeinta poistumisreittiä ja että evakuoitavien pyrkimys yhteistyöhön voi hidastaa poistumista.Väitöskirjassa kehitetyt henkilöliikenteen estimointimallit ja -algoritmit mahdollistavat hissien tehokkaamman ohjaamisen ja osa menetelmistä on jo tuotannossa KONE...

This field study was made to study the size of social groups among passengers travelling with lifts. The group size was observed in three types of buildings located in four countries, totally in nine multi-storey buildings. The observations were carried out manually for 12 hours on a normal weekday. Analysis results show that the daily mean group size was generally low: 1.2–1.3 persons in the offices, 1.3–2.0 persons in the hotels and 1.1–1.4 persons in the residential buildings. Hourly means differed significantly from the daily means for each building. This suggests that group size should be considered on hourly or shorter basis. In addition, goodness-of-fit tests were conducted to determine a statistical model for the group size. A geometric distribution was found to fit the data the best across all building types which simplifies modeling of passenger group arrivals in buildings. Practical application: This article contributes to the understanding of lift passenger tendency to travel in socially connected groups in multi-storey buildings. A unique data set of group size observations around the world is reported on an hourly basis for office, hotel and residential buildings. A geometric distribution is proposed to model the group size statistically, which simplifies the use of socially connected groups when generating passengers in lift traffic simulations. The real-world data and the statistical model of the group size enable more realistic lift traffic simulations that are routinely conducted during the design phase of multi-storey buildings.

We present a constraint programming formulation for the elevator trip origin-destination matrix estimation problem using a lexicographic bi-criteria optimization method where least squares minimization is applied to the measured counts and the minimum information or the maximum entropy approach to the whole matrix. An elevator trip consists of successive stops in one direction of travel with passengers inside the elevator. It can be defined as a directed network, where the nodes correspond to the stops on the trip and the arcs to the possible origins and destinations of the passengers. The goal is to estimate the most likely counts of passengers for the origin-destination pairs of every elevator trip occurring in a building that are consistent with the measured boarding and alighting counts and any prior information about the trip matrix. These counts are used to make passenger traffic forecasts which, in turn, are used in elevator dispatching to reduce uncertainties related to future passengers and therefore to improve passenger service level. Artificial test data was obtained by simulations of lunch hour traffic in a typical multi-story office building. This resulted in complex problem instances that enable robust performance and quality testing. The results show that the proposed approach outperforms previous alternatives in terms of quality, and can take an advantage of prior information. In addition, the proposed approach satisfies real time elevator group control requirements for estimating elevator trip origin-destination matrices. Practical application: The elevator trip origin-destination matrix estimation problem is important since it makes it possible to obtain complete information and statistics about the elevator passenger traffic. The statistics can be used to model future passengers which, when taken into account in the elevator group control, helps to improve passenger service level. Simulation experiments show that most of the problems occurring in reality can be solved within a reasonable time considering a real application, and the solving algorithms are relatively easy to implement using available constraint programming tools. Hence, this work is undoubtedly of interest to the building and elevator industry.