Modeling ground access mode choice behavior for Hamad International Airport in the 2022 FIFA World Cup city, Doha, Qatar

2018 ◽  
Vol 73 ◽  
pp. 32-45 ◽  
Author(s):  
Esmat Zaidan ◽  
Ammar Abulibdeh
Keyword(s):  
Choice Behavior ◽  
Mode Choice ◽  
World Cup ◽  
Fifa World Cup ◽  
International Airport ◽  
Access Mode

scholarly journals Modelling of Ground Access Mode Choice: Empirical Evidence from Mehrabad International Airport, Tehran, Iran

2020 ◽  
Vol 16 (2) ◽  
pp. 299-307
Author(s):  
Ali Edrisi ◽  
Rambod Vakilian ◽  
Houmaan Ganjipour ◽  
Ali Rezaei Jafari
Keyword(s):  
Empirical Evidence ◽  
Mode Choice ◽  
Multinomial Logit ◽  
Age Group ◽  
Questionnaire Data ◽  
Land Access ◽  
International Airport ◽  
Access Mode ◽  
Private Car

AbstractThe aim of this study is to investigate air travellers’ behaviour with regard to choices for ground access to this airport. Questionnaire data were collected from 684 respondents. Multinomial logit modelling was employed for this purpose. These findings indicate that in families with at least one car of their own, the tendency for using bus and carpooling was decreased. In addition, it was found that those in the 36 - 40 age group were more inclined to take the taxi. Finally, the results of this study suggest that with the growing price of the private car, the tendency to use it was diminished. Therefore, if transportation authorities seek to reduce the use of personal cars, it is possible to make the use of private cars expensive with increasing the parking cost in the airport. This may lead to the reduction of using private cars in land access to Mehrabad airport.

scholarly journals MODELLING TRAVELLER’S GROUND ACCESS MODE CHOICE OF DUBAI INTERNATIONAL AIRPORT, UNITED ARAB EMIRATES

2019 ◽  
Vol 10 (1) ◽  
pp. 87-109
Author(s):  
Khaula Alkaabi
Keyword(s):  
United Arab Emirates ◽  
Public Transportation ◽  
Mode Choice ◽  
Binary Logistic Regression ◽  
Vehicle Ownership ◽  
Main Mode ◽  
Logistic Regression Models ◽  
International Airport ◽  
Access Mode ◽  
Using Data

With increased demand for air travel, airports have become economic engines of the regional development that connect aviation systems with other modes of transportation facilitating the movement of people and cargo. Originating air passengers account for the majority of passenger trips to and from an airport and have different ground access needs. Since the air transportation is interrelated to ground transportation, studying ground access mode choice of airport users is a crucial part of airport management and system planning. The purpose of this study is to identify Dubai International Airport (DXB) ground access mode characteristics and users located in the United Arab Emirates (UAE), which has not been studied previously. Although there are many different modes of transportation serving the airport, yet personal vehicles are the main mode used to arrive to the airport. Binary logistic regression models are developed to evaluate access mode choice for originating air travelers focusing on the mix between private vehicle and public transportation system (taxi, limousine, bus network, and Dubai metro) using data collected specifically for this study. A total of 1012 air travelers were interviewed and completed the questionnaire in December 2014. Models result showed that access mode choice is significantly affected by different socio-economic characteristics of travelers including income, nationality, household size, vehicle ownership; and different trip characteristics that include number of travelers and how often air travelers use public ransportation in their community.

JTRF Volume 56 No. 3, Fall 2017On-Demand, App-Based Ride Services: A Studyof Emerging Ground Transportation ModesServing Los Angeles International Airport (LAX)

2017 ◽  
Author(s):  
Karina Hermawan ◽  
Amelia C. Regan
Keyword(s):  
Los Angeles ◽  
Transportation Network ◽  
Mode Choice ◽  
Nested Logit ◽  
Travel Times ◽  
Google Maps ◽  
International Airport ◽  
Access Mode ◽  
Transportation Network Companies

This research estimates and compares travel times and costs of transportation by Uber and Lyft (the latter are referred to here as transportation network companies or TNCs) against other forms of ground transportation to Los Angeles International Airport (LAX). Using estimated travel times and costs derived from Google Maps and other sources as well as the 2015 LAX air passenger survey, we develop estimates of airport ground transportation access mode choice decisions. Among other fi ndings, our preferred nested logit specifi cation implies that if TNC fares were to be raised to match the current cost of taking a taxi to the airport, demand for TNC’s would fall by 20.9% and 23.3% (relative to initial TNC shares) for business and leisure passengers, respectively.

Selecting the best team from the players in the 2018 FIFA World Cup using performance analysis

2020 ◽  
Vol 42 (1) ◽  
pp. 15-32
Author(s):  
Tugay Karadag ◽  
Coskun Parim ◽  
Erhan Cene
Keyword(s):  
Performance Analysis ◽  
Topsis Method ◽  
World Cup ◽  
Fifa World Cup ◽  
Performance Variables

This study aims to determine the best player in each position from among the footballers who played in the 2018 World Cup in Russia. Player statistics for those who played over 200 minutes were obtained from the FIFA official and transfermarkt.com websites. Selected performance variables were then calculated per 100 minutes and the results were normalised. Kruskal Wallis H and Bonferroni Tests were used to determine the weights of the variables before the analysis. As the variables will have different values according to the players’ positions, the weights for each position were calculated separately. Finally, the performances of the players on the basis of the variables used were ranked for each position using the TOPSIS method. A second analysis was undertaken including only those players whose ages were under 28 and goalkeepers whose ages were under 32. The purpose of this analysis was to identify players with potential that had been largely unrecognised up until the tournament. It was found that both the teams selected in this way were dominated by players from European clubs. Ninety-two percent of the top sixty players in the analysis were playing in European leagues with 85% playing in Spain, England, Italy, Germany, France or Russia.

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