Ground Access Mode Research Articles

Multi-criteria decision-making for the selection of best airport ground access mode with a new fuzzy rough-entropy based method

In order to cope with the inherent complexities and uncertainties of settings such as transportation problems, the adoption of proper decision procedures becomes a pivotal issue. Particularly, multi-criteria group decision-making (MCGDM) uses multiple evaluations of the options based on several criteria to select the optimal choice and/or rank the alternatives. In this paper we are interested in the Entropy-REGIME method for MCGDM and its ability to handle uncertainty in the presence of fuzzy rough numbers (that allow the practitioner to represent and process imprecise data). To this end we propose a hybrid framework of the fuzzy rough-entropy based REGIME method that involves two main components: fuzzy rough-entropy for the evaluation of the objective weights of the decision criteria, and fuzzy rough-REGIME to rank the alternatives based on the weighted criteria. Its motivation is a case study that concerns the selection of the best transport mode to access Istanbul newly constructed airport. We consider four alternatives for ground access options, namely, underground metro, bus rapid transit, light rail transit, and premium bus services. Our findings show that an underground metro system is the optimal mode, followed by light rail transit, bus rapid transit, and premium bus services. Additionally, to demonstrate the superiority and effectiveness of the proposed technique, comparisons are made with existing methods.

Optimal planning of urban air mobility systems accounting for ground access trips

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low.

Unraveling passengers’ preferences: investigating airport access mode choice in Tehran, Iran

Airports are imperative elements in fueling the economic growth of a country by reducing the distances between origins and destinations for the movement of both passengers and goods. Managing ground access modes of travel has become essential as the majority of trips are made by private cars, leading to increased environmental issues and congestion. To alleviate these problems, it is vital to identify the factors that shape air passengers' choice of ground access mode. In this work, we utilized inclusive Revealed Preference data (RP) collected through face-to-face interviews and applied a Multinomial Logit (MNL) model to study passengers' ground access mode choice behavior at Mehrabad International Airport, the busiest airport in Iran. The results showed that different factors such as socioeconomic status, travel characteristics, and distance to the airport affect the mode choice behavior of passengers in reaching the airport.

The Impacts of Low-Carbon Incentives and Carbon-Reduction Awareness on Airport Ground Access Mode Choice under Travel Time Uncertainty: A Hybrid CPT-MNL Model

Developing strategies to incentivize travelers towards adopting sustainable mobility options is one of the effective approaches to mitigate carbon emissions. Using Xi’an Xianyang International Airport as a case study, this study aims to explore the effects of low-carbon incentives and carbon-reduction awareness on airport ground access mode choices. In addition, to account for the complex road environment, an innovative stated preference choice experiment was designed, integrating the factor of travel time uncertainty. Then, a hybrid cumulative prospect theory–Multinomial Logit (CPT-MNL) model was also developed. The estimated results revealed that travelers increasingly prioritize emissions reduction and consciously prefer sustainable mobility options to reach the airport. Furthermore, the potential of low-carbon incentives to encourage public transport usage over private vehicles has been highlighted. Notably, travel time uncertainty had a significant impact on the choice of private cars. When the travel time to the airport is uncertain, travelers exhibit a greater inclination towards selecting public transport. The findings of this study offer nuanced insights for transportation authorities, aiding them in fostering the adoption of sustainable mobility options and achieving carbon reduction objectives.

Investigating the recovery of For-Hire-Vehicle, Taxi, and AirTrain at two New York City airports during the COVID-19 pandemic

For operators of large-scale infrastructure systems such as airports, predicting landside access demand amid constant exogenous changes that modify travel volumes and preferences is immensely challenging. COVID-19 disrupted travel behavior and mode preferences, making it even more challenging for airport operators to plan infrastructure and design strategies to accommodate landside access. We use time series regression discontinuity to explore to what extent the volumes of airport ground access modes changed during the pandemic, how the recovery differed across modes, and how the pandemic changed the established relationship among access modes. We do so by examining the change in the three main airport access modes at two large New York City airports, John F. Kennedy International Airport and LaGuardia Airport (traditional taxis; for-hire ride-hailing services such as Uber; AirTrain, the public transit system, at JFK only). We find the mismatch between taxi pick-up and drop-off trips that predates the pandemic widened and persisted during the pandemic; the pandemic diminished taxis’ role and exacerbated the replacement of taxis by for-hire-vehicles. For-hire-vehicle trips recovered at both airports, possibly at the expense of taxis. AirTrain continued to function as an important and resilient airport access mode during the pandemic. Our research helps to inform the airport operators’ resource allocation and traffic management strategies while showcasing the usefulness of time series regression discontinuity in examining changes in travel behavior for future planning in the aftermath of a major shock.

AIMing for Equity in Aviation Accessibility: Development of the Aviation-accessibility Integrated Mobility (AIM) Metric

As federal spending and planning for air transportation infrastructure looks to prioritize access for disadvantaged populations, aviation systems planning metrics that measure accessibility at the individual level are necessary. Existing metrics, from the mobility-driven metrics focused on efficiency and on-time performance to geographic accessibility metrics focused on connectivity, lack the detail of the multiple, interlocking constraints that limit potential travelers (especially lower-income travelers) from executing their agency and accessing the aviation system. We seek to develop a methodology, resulting in new analysis metrics, to quantify accessibility on an origin–destination basis based on individual constraints, time- and cost-based impedance, and aviation travel supply. We develop and apply our Aviation-accessibility Integrated Mobility (AIM) metric to empirically model relative accessibility based on traveler-specific constraints, accounting for individual-level sensitivity to travel costs and propensity to travel by ground access modes. We illustrate how equity-focused variables can change the calculus and geographic distribution of accessibility by applying the AIM to our case study region: Philadelphia, Baltimore, and Newark metropolitan areas, a region with significant socioeconomic disparities, to diverse markets (Austin, Atlanta, Nashville). Our findings indicate that incorporating individual constraints greatly influences the calculation of accessibility; additionally, we find that transportation supply and service characteristics alter the distribution of accessibility. Our model supports a national map of accessibility and potential policy recommendations to expand traditional federal airport infrastructure projects, such as targeted air service enhancement.

A hybrid model to identify the impact of indicators for boarding on passenger's experience

This paper has the objective of mapping the KPIs (Key Performance Indicators) that can be used to measure the level of service and the passenger’ characteristics that mostly impact the boarding procedure in airports. For that, two approaches are made: (i) structured interview with air transport specialists about performance indicators - analyzed through the Analytical Hierarchy Process (AHP) to prioritize the indicators, and (ii) regression analysis of permanent passenger’s satisfaction survey to identify the passenger’s profile. The results show a methodology that can be replicated and its application in three airports from the Multi-Airport System of Terminal Sao Paulo that compete against each other: Guarulhos International Airport (SBGR), Congonhas Airport - Deputy Freitas Nobre (SBSP) and Viracopos - Campinas International Airport (SBKP). Although results might vary for different contexts, for this specific application, it was found that time spent in airport queues can be used as a perceived level of service predictor for boarding. The passenger’ characteristics which mostly impact the perceived levels of service quality were: (i) ground access mode, (ii) family income and (iii) age. Regarding the airport’s level of service, SBSP, which is an airport that is not yet part of the government concessions, have the worst satisfaction rates both in “overall satisfaction” and in “perceived level of service in boarding procedures” when compared to the other two airports of this study. Also, the methodology used to select and evaluate KPIs in this study can help airports around the world that compete against each other to measure the financial return over investments associated with efforts to increase service levels.

Joint Modeling of Access Mode and Parking Choice of Air Travelers Using Revealed Preference Data

Airport ground access mode choice is distinct from everyday mode choice decisions, necessitating context-specific choice model estimation. Understanding airport ground access mode choice decisions is not only important for developing infrastructure planning strategies, but also for assessing the impacts of emerging modes on airport revenues, particularly from parking. However, parking choice is an often-overlooked dimension in airport ground access choice modeling. This paper addresses this gap through the development of a joint model of airport access mode and parking option choice using a passenger survey conducted at Dallas-Fort Worth (DFW) International Airport in 2015. Compared with a traditional conditional logit model that does not consider parking options available at DFW airport, the joint model of mode and parking decisions was found to generate more realistic values of travel time and was shown to have better predictive performance, both of which are critical for obtaining better airport parking revenue estimates and identifying traveler cohorts who may respond more strongly to potential policies targeting curb congestion and parking demand.

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

Abstract The 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.

Quantifying Airport Employee Commuting and Related Energy Use: A Comparison of Six US Airports

There has been growing research interest in understanding the energy signatures of airport access and egress as air travel continues to grow. However, these findings generally focus on airport access from the perspective of air travelers rather than ground access modes by airport employees. Using a data set of employment records, this paper uses residential location data of airport employees to estimate the energy implications of employee commuting at six major US airports based on commuting mode choice information from the US Census. This analysis reveals a high degree of variance in commuting energy usage by employees between different airports.

Tri-reference-point hypothesis development for airport ground access behaviors

Studies have applied single-reference-point or safety margin hypotheses to examine how advanced traveler information affects travel behaviors. However, these theories may fail to fully capture the trade-offs among origin departure time, airport access time, and terminal processing time in terms of airport ground access behaviors. In this study, we developed a tri-reference-point hypothesis and assumed that the rate of change of utility may change at the air passenger’s preferred (PAT), earliest acceptable (EAT), and latest acceptable (LAT) airport arrival times. With an empirical data set collected from 304 passengers at Taipei Songshan Airport, the study examined the tri-reference-point hypothesis by analyzing airport ground access mode choice behaviors with a pooled framework that combined revealed and stated preferences. Moreover, the study developed four alternative specifications for schedule delay variables, assuming that air passengers used different reference points to determine relative gains and losses of the expected airport arrival time. The specifications included selecting both EAT and LAT as the zero-utility points (an indifference-band specification) and either one of PAT, EAT, and LAT as the single zero-utility point. Regardless of which specification was employed for schedule delay variables, the tri-reference-point hypothesis was generally supported. In particular, a significant difference of the rate of change of utility around PAT, EAT, and LAT was identified in the analysis results. When managing increasing road travel times and increasingly congested terminals, air passengers were more willing to retime their origin departure time to an earlier time than to switch their ground access mode. The implications of the analysis results for airport ground access management are discussed in the study.

Selecting an airport ground access mode using novel fuzzy LBWA-WASPAS-H decision making model

Airports are critical in ensuring a fast way of transporting people and goods. Choosing a reliable, fast and comfortable access mode to the airport is vital to ensure a seamless aviation system. The aim of this study is to select the best transport mode for Istanbul’s newly constructed Istanbul Airport. One of the largest airports in the world with 150,000 passenger capacity per year, Istanbul Airport is located in the northern part of Istanbul, outside the city. However, the access to the new airport resulted in many controversies about the selection of the best mode. Underground metro, bus rapid transit (BRT), light rail transit (LRT) and premium bus services are put forward as alternative ground access modes. These alternatives are evaluated based on 4 main decision criteria including financial aspects, operating features, project characteristics and environmental sustainability, which are broken down into 14 sub-criteria. In this paper, the importance weights of the criteria are determined by novel fuzzy Level Based Weight Assessment (LBWA) which is capable of modelling human thinking. Afterwards, the traditional Weighted Aggregated Sum Product Assessment (WASPAS) method is enhanced by the integration of the fuzzy Weighted Heronian Mean (WHM) and fuzzy Weighted Geometric Heronian Mean (WGHM) functions. A hybrid fuzzy multi-criteria decision making method based on LBWA-WASPAS-H model is used to solve this ground access mode selection problem. The results show that an underground metro is the most optimal mode, followed by LRT, BRT, and premium bus services.

Impacts of Automated Vehicles on Airport Landside Terminal Planning, Design, and Operations

Income from parking, rental car facilities, and other ground access modes for most commercial airports in the United States is a significant component in revenue. With the emergence of automated vehicles (AVs), or so-called “self-driving vehicles,” these fundamentals could change. Airport stakeholders need to understand the impacts of the emerging AVs to airport planning, design, and operation. If the impact hurts the operational resilience and financial sustainability of the airports, the stakeholders should come up with countermeasures to alleviate the impacts and to ensure the smooth operation and continuous growth of the airport. To serve these needs, this study quantifies the potential impacts of AV on airport parking and ground access by building a simulation platform and applying scenario analyses. Two airports are selected for case study: Tampa, FL (TPA) and San Francisco, CA (SFO). To fill in the gaps of historical data, statistical methods are used to generate inputs for study airports based on historical information of passenger demand, ground access mode split, and parking categories and durations. Furthermore, future scenarios are developed based on reasonable assumptions of the emergence of AVs. Outcomes of the case study show that the emergence of AVs will significantly affect airport operation if nothing as of now was changed. However, the impacts could be different for airports that are more auto-dependent versus those in metropolitan areas with various ground access options. Moreover, this study discusses possible strategies that can help airports generate revenue in the era of emerging AVs.

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

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.

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

The Hamad International Airport (HIA) in Doha, Qatar is the only international airport operating in the country. The airport was officially opened in 2014 to function as the engine of the country's economic growth. Due to the anticipated high passenger demand of this airport, especially to account for the 2022 FIFA World Cup and in support of Qatar National Vision (2030), it is important to analyze, model, and evaluate the current and future airport ground access and traveler's modal choice. As such, the aim of this study is to analyze the current HIA ground access mode choice characteristics and users, examine the importance of introducing the Doha Metro to airport mode choice, and identify the preference of air travelers for this new mode. A revealed- and stated preference face-to face interview survey was conducted at the airport to collect data related to trip and user characteristics. Quantitative and qualitative methods were used to analyze the data. Binary logit mode was used to the current access mode choice, and a multinomial logit model was used to model the future access mode choice after introducing the Doha Metro. The models results showed that current and future access mode choice is significantly affected by the trip and socioeconomic characteristics of the HIA users.

Intercity travel analysis for a university township with emphasis on air travel

Abstract Intercity travel is crucial to the economic development of any nation with air travel demand modeling being a key component in transportation planning. For this study, the university township of IIT Bombay in Mumbai, India is considered to capture the travel choice behaviour of its residents comprising of students, faculty and staff, when traveling to other cities. The prime areas of analysis are determining trip generations based on socioeconomic characteristics of the travelers using regression analysis, understanding mode choice by looking into travel decisions made as a function of both mode and income characteristics using utility theory, and to decipher the dimension of airport choice when a new airport comes into play along with an existing airport using utility theory, all of these based on responses of the travelers to Revealed Preference (RP) and Stated Preference (SP) questionnaires, with the latter based on the response of 1% of the population. Extensive sample set of 7 % of the university population is considered with two questionnaires, one an RP approach focusing on outgoing trips in 2014 and the other, an SP approach to model airport choice. Interviewees are divided into five categories, three for students based on monthly stipend and the rest for faculty and staff, with trips being differentiated purpose-wise. Number of annual trips by air and other modes is estimated for the sample set with adjusted R2 and p-values giving simplified trip generation equations. Choice of main modes like flight and train and ground access modes of auto and taxi, is deciphered using Multinomial Logit modeling with McFadden R2, p-values and prediction-success tables being evaluation criteria. Conditional probability of airport choice is calculated using a balanced and orthogonal fractional factorial design of 9 options with airfare, flight frequency, airport access time and airport delay, each being three-level indicators. A sound theoretical framework to dissect the intricacies of intercity travel choice with special importance to air travel has been constructed here with the results demonstrating that travel behaviour is not an independent entity but a function of mode and socioeconomic characteristics. Individuals whose households have higher socioeconomic conditions fly more frequently and choose comfortable modes of travel, in general. Airfare and flight frequency are found to be vital attributes for a potential airport choice study. This study provides flexibility in application to any region under similar principles to understand the nuances of travel behaviour better.

Understanding Issues in Airport Ground Access Modeling: Lessons from a Revealed Preference Study of New York Metropolitan Area Airports

Airport ground access mode choice models can provide a great deal of utility for airport facility managers tasked with landside access planning. However, the absence of definitive standards to guide the development of these airport planning tools often results in wide variations in methodological approaches that in turn generate counterintuitive mode choice model parameters and that often leads to improper understanding of the air passenger ground access trip. A new regional airport ground access model was developed in support of the New York City Department of Transportation’s LaGuardia Airport Access Alternatives Analysis Study. The air passenger model developed for the study included a set of market-segmented ground access mode choice models, developed by using revealed preference data from a 2005 survey commissioned by FAA. The model estimation process tested a number of analytical strategies to address some of the challenges typically encountered with revealed preference data and, in the process, uncovered some findings that should both aid future airport ground access mode choice modeling efforts and further illuminate the modeling community’s understanding of the value of time, particularly as it interacts with household income levels and various dimensions of business travel.

Mode choice behavior modeling of ground access to airports: A case study in Istanbul, Turkey

The increase in air transportation mode share, thereby the passenger traffic at airports has made the ground access to the airports more important in recent years. The aim of this study is to analyze the ground access mode choice to airports by using Multinomial Logit (MNL). In particular, our focus is on how transit areas of influence affect the mode choice for travelling to airports. Atatürk International Airport (IST) in Istanbul, Turkey was selected for the analyses and the investigated modes to access IST were automobile, drop-off, public transit, and taxi. The results showed that significant factors and variables included the trip distance to access IST, type of destination, trip cost to IST, automobile ownership status, employment status, travelling group size, location of the trip origin with respect to public transit influence, and time difference between the flight time and departure time to IST. It is also concluded that if the trip origin to IST was inside the influence areas of public transit, then public transit would be more likely to be chosen over other modes for accessing IST.

Airport ground access and private car use: a segmentation analysis

The increasing scale of, and demand for, civil air transport world-wide has resulted in a greater volume of people travelling to and from airports. The vast majority of these journeys are made by private cars, which has led to traffic congestion and raised levels of air pollution in and around airports. Subsequently, airports are re-evaluating their approach to ground access mode choice and considering how to reduce the reliance on private cars. Based on a survey of passengers at Manchester Airport in the UK, attitude statements pertaining to psychological constructs from the Theory of Planned Behaviour and the Norm-Activation Theory, combined with key factors relating to the passenger’s trip, are used to identify eight behaviourally distinct groups of passengers with varying potential to reduce their private car use. Two of these groups, described as the Conflicted Greens and the Pessimistic Lift Seekers, are identified as having the greatest potential to reduce private car use to airports. Analysis reveals the need for decision makers to address the existing attitude–behaviour ‘gap’ that can prevent positive environmental attitudes being translated into the use of more sustainable modes, as well as tackling the perceived difficulty some passengers associate with using these modes.

Domestic airport passenger access mode choice decisions in a multi-airport region of South Africa

The ground access mode used by air passengers to an airport has a vital impact on infrastructural and environmental decisions. An important aspect of a passenger’s mode choice is the sensitivity to factors such as access time and access cost. The objective of this research was to analyse air passenger’s sensitivity to access mode choice attributes, that is,access time, access cost, parking time and parking cost at two airports in Johannesburg, South Africa. A stated choice experiment was used to obtain the information and a latent class model was estimated. In general, discrete choice experiments are designed to reveal respondent(preference) heterogeneity and the latent class model allows for this heterogeneity to be modelled discretely. The estimated results indicated that three latent classes provided the best fit with preference heterogeneity evident from the set of parameter estimates. The access mode used was found to be the only significant covariate in the class assignment model. The respondents’ willingness to pay for a reduction in access time was estimated and it indicated that respondents had the highest access time willingness-to-pay value for the taxi as access mode. In addition, it was estimated that passengers being dropped off at the airport had a higher access time willingness-to-pay than passengers that used their own vehicles to the airport. The research results confirmed the presence of respondent heterogeneity (according to access mode) which resulted in different access time willingness-to-pay values.