•Elevator dispatching and routing problems are modelled mathematically.•Control systems affect passenger waiting times and elevator performance which is determined by simulation.•Elevator solutions ...and zoning affect elevator core space demand.•Multicar elevator system takes least of the building space.
In the middle of the 19th century, the invention of a safety device that prevented elevators from falling enabled the construction of tall buildings and skyscrapers. In the middle of the 20th century, control systems started to serve the given calls automatically by relay technology, and later by electro-mechanical systems. In the 1970s-80 s, software-based control systems invaded elevator technology. Passenger service levels improved with the application of mathematical methods such as artificial intelligence. When the old relay boards of the skyscrapers in New York were modernized by software-based group controls, passenger waiting times dropped to less than half. In this millennium, the need to reduce elevator core space has further increased, since a significant number of buildings already exceed 300 m. The challenge in constructing tall buildings is that elevator groups can occupy the rentable area of a building. At the elevator planning stage, elevator core space can be decreased by zoning the building. The latest trends include systems with several elevator cars running in the same shaft. With modern control systems, passenger journey times can be decreased and handling capacity increased. This article deals with mathematical methods used in elevator dispatching problems. Building traffic simulation is utilized to search for an elevator arrangement that saves the most space in an example building. The design criteria of the ISO 8100–32 standard are used in selecting the elevator arrangements.
In recent years, airline practitioners and academics have started to explore new ways to model airline passenger demand using discrete choice methods. This book provides an introduction to discrete ...choice models and uses extensive examples to illustrate how these models have been used in the airline industry. These examples span network planning, revenue management, and pricing applications. Numerous examples of fundamental logit modeling concepts are covered in the text, including probability calculations, value of time calculations, elasticity calculations, nested and non-nested likelihood ratio tests, etc. The core chapters of the book are written at a level appropriate for airline practitioners and graduate students with operations research or travel demand modeling backgrounds. Given the majority of discrete choice modeling advancements in transportation evolved from urban travel demand studies, the introduction first orients readers from different backgrounds by highlighting major distinctions between aviation and urban travel demand studies. This is followed by an in-depth treatment of two of the most common discrete choice models, namely the multinomial and nested logit models. More advanced discrete choice models are covered, including mixed logit models and generalized extreme value models that belong to the generalized nested logit class and/or the network generalized extreme value class. An emphasis is placed on highlighting open research questions associated with these models that will be of particular interest to operations research students. Practical modeling issues related to data and estimation software are also addressed, and an extensive modeling exercise focused on the interpretation and application of statistical tests used to guide the selection of a preferred model specification is included; the modeling exercise uses itinerary choice data from a major airline. The text concludes with a discussion of on-going customer modeling research i
The COVID-19 pandemic has changed many aspects of people's lives including travel since early 2020. Specifically, it has adversely affected people traveling by air and has hit the air transport ...industry significantly. But, how big is the COVID-19 impact? In order to answer such a question, we collected air passenger traffic data from the US, European countries, and China which accounted for over 75% of the world's total air passenger traffic. Air passenger traffic data in these three regions during the period January 2010 to December 2019 were modeled using seasonal autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models were used to predict air passenger traffic from January 2011 to December 2019 (just before the spread of COVID-19) and the accuracy of the models was evaluated. The models were then used to predict air passenger traffic from January 2020 to December 2022 for the case without COVID-19. The COVID-19 impacts on air passenger traffic were estimated by calculating the differences in predicted and actual air passenger numbers in monthly basis. Results showed that air passenger traffic was significantly recovered in the US and European countries but it encountered significant falls in 2021 and 2022 in China due to spikes in COVID-19 variant cases in many provinces and the implementation of zero-tolerance COVID-19 policy. Implications of the study are given.
•The COVID-19 pandemic adversely affects the air transportation industry.•Seasonal ARIMA models can model air passenger traffic accurately before COVID-19.•Seasonal ARIMA models show how COVID-19 impacts air traffic in the US, EU and China.•Rate of recovery are different among air passenger traffic in different regions.
The paper aims to evidence the impact of passenger traffic in the airports of major Brazilian tourist destinations and its relation to the spread of COVID-19 pandemic. To achieve this, the study ...performed a qualitative analysis based on secondary data obtained from official websites of regulatory authorities and a quantitative analysis through the use of multiple regression, cluster and discriminant analysis in order to measure a cause-and-effect relation between the variables observed. The tourist destinations addressed are the capitals of Brazilian federal states, the national capital (Brasília), and the cities of Campinas, Foz do Iguaçu, and Balneário Camboriú - the choice was made based on the cities with the highest number of airport passenger traffic. The results indicate a strong correlation between passenger traffic in Brazilian capitals and the spread of COVID-19 cases.
The main task of sensors in public transport is to collect information about the transportation of passengers. One of the features of passenger traffic in Russian settlements is the low level of ...their organization. This is due, among other things, to the lack of professional analysis of traffic flows when designing route networks. The relevance of the work lies in increasing the efficiency of detecting urban passenger traffic using modern methods and algorithms. The article analyzes the current state of passenger traffic in the settlement, describes the role of technical and programs for monitoring passenger traffic, classifies their types and areas of application.
Several possible options for the location of Pyatiletka transport interchange hub in the Samara city district are considered. In order to determine the optimal option, the hub location is compared by ...several parameters. Such values as passenger traffic, existing routes of urban public transport, priority directions of passenger traffic, and capital investments in construction are selected as optimization parameters. To determine the values of passenger traffic, an analysis of the existing passenger traffic was performed, with its allocation by capacity and routing. The unevenness of passenger traffic by days of the week and periods of the day is determined, the minimum and maximum values of passenger traffic are revealed, as well as its fluctuations over the considered periods. The construction of public urban transport routes allowed to identify the busiest routes and the availability of transport for different variants of the transport interchange hub location. The options of organizing the possible arrival/departure of urban public transport to/from the transport interchange hub are considered. Using the obtained data, a SWOT analysis was performed to determine the strengths and weaknesses of each hub placement option and the optimal variant was selected.
Most of the regional airports in India are financially unsustainable because of low and fluctuating passenger traffic. Despite double digit passenger traffic growth since past four years, most ...regional Indian airports are yet to achieve financial sustainability due to high fixed operating costs and low non-aeronautical revenue. The Indian government is putting special emphasis on regional air connectivity through UDAN (Ude Desh ka Aam Nagrik) scheme. However, it is difficult to ascertain whether such schemes can ensure airport profitability. This paper attempts to find the operating breakeven point in terms of annual passenger traffic for 27 regional airports over a period of three years from 2014-15 to 2016-17. The method used is simple linear regression of operating revenue and operating cost with passenger traffic. The method as well as findings have been corroborated with relevant literature. The breakeven point changes 0.8 million passengers in 2014-15 to 0.6 million passengers in 2016-17. Most regional airports in the sample have more than 0.5 million passengers per annum and this paves way for smaller and upcoming airports looking for incentive schemes to attract airlines.
This paper provides an overview of the development of the low-cost carrier (LCC) sector in China, Japan, and South Korea. It is the first paper that documents LCC contributions to the passenger ...traffic and cheaper fares in Northeast Asia (NEA)'s intra-markets. We argue that a single aviation market can facilitate the growth of the LCC sector, which in turn will make a significant contribution to the NEA connectivity, mobility, and integration. In addition, with a single aviation market, NEA countries can adopt a proactive, unified approach in negotiating air transport agreements with the major aviation partners to maximize the interests of this region as a whole, which will further provide valuable growth opportunities for the LCCs.
•This paper provides an overview of the development of the low-cost carrier (LCC) sector in China, Japan, and South Korea.•The LCCs contribute to the increase in passenger traffic and reduction in airfares in Northeast Asia's intra-markets.•A single aviation market in Northeast Asia can facilitate the growth of the LCC sector.•This region can adopt a proactive, unified approach in negotiating air transport agreements with the major aviation partners.
Passenger traffic at airports is characterised by fluctuations resulting from the influence of several factors. The influence of each factor is different, leading to unpredictable passenger traffic ...patterns that make planning difficult. Previous studies used geographic, demographic, and economic variables as exploratory factors to examine air travel demand. The study explores several variables to confirm airports and airlines’ characteristics as demand factors for domestic air travel in Nigeria. Data for the study were collected by administering a questionnaire to respondents at major domestic airports in Nigeria. The variables were presented in the 5-point Likert Scale for respondents to rank in order of significance. Exploratory and confirmatory factor analyses (EFA and CFA) were employed to identify the significant factors affecting passenger traffic at domestic airports in Nigeria. EFA reduced fifteen variables to four orthogonal factors influencing passenger traffic at domestic airports. CFA validates airport and airline services, demographics, economic factors, and airport size and facilities as significant factors affecting passenger traffic at domestic airports in Nigeria. The model fit test shows CMIN/DF = 2.263; CFI = 0.940; GFI = 0.929; NFI = 0.901; and RMSEA = 0.078. The result identifies airport and airline characteristics as factors influencing passenger traffic at the domestic airport in any country. It implies that airport and airline characteristics significantly influence domestic air traffic and needs to be included in modelling. Identifying airport and airline characteristics as air travel determinants make this study unique for policy decisions to forecast domestic passenger traffic in a country.
PDF
