The Impact of Perception toward the Built Environment in Airport Access Mode Choice Using Hybrid Choice Modeling

Yazdanpanah, Mahdi; Hosseinlou, Mansour Hadji

doi:10.1155/2017/8268701

Cited by 7 publications

(4 citation statements)

References 70 publications

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“…Most notably, constructs such as general attitudes and personality traits are considered useful for explaining rather than predicting behavior ( 5 ). These relationships and predictive powers were confirmed in more recent studies of travel behavior, with Yazdanpanah and Hosseinlou ( 6 ) considering the impacts of personality trait LVs, and Paulssen et al ( 7 ) the LVs of values (which are comparable to general attitudes and closer to personality traits than any other psychological constructs). Both found that these more abstract psychological LV constructs had no statistically significant effect on behavior, unless mediated by other, more choice-specific psychological constructs.…”

Section: Relevant Concepts and Their Utilizationsupporting

confidence: 55%

Defining Psychometric Variables Related to Use of Autonomous Vehicles

Ranjbari

Lewis

et al. 2019

Transportation Research Record: Journal of the Transportation R

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With the goal of understanding autonomous vehicle (AV) adoption and use behavior, numerous behavioral studies and surveys have included variables intended to capture individuals’ perceptions of and attitudes toward AVs. However, the selection of questions to measure these psychometric variables appears to be ad hoc and, in many cases, arbitrary. In contrast, this study defines psychometric latent variables (LVs) that are related to the adoption and use of AVs and develops a set of questions to reliably measure them. By considering three psychological concepts (norms, perceptions, and attitudes) and nine qualitative utility constructs that influence individuals’ travel behavior, this study defines a comprehensive list of LVs and identifier questions to support their construction. A factor analysis of a nationwide n = 347 sample was used to obtain a minimum set of relevant LVs and questions to measure them. Ultimately, the factor analysis resulted in a final set of nine LVs specified by 44 questions (four or five questions for each LV). The final set of questions may be used by researchers or survey organizations interested in studying future trends of demand and adoption for AVs or other emerging transportation modes. The approach used in this study may also be employed in other contexts to define psychometric variables of interest and the questions needed to reliably measure them.

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Section: Relevant Concepts and Their Utilizationsupporting

confidence: 55%

Defining Psychometric Variables Related to Use of Autonomous Vehicles

Ranjbari

Lewis

et al. 2019

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…The typical Type 1 model is a model of access mode choice to a specific airport. This type of model has been developed for several airports in the US [1,2,4,24], Europe [5,9,10,23,43], and Asia [3,14,29,36,38,39,44,45]. Also Type 1-models when rail is the main mode exist [42], even though it is not at all as common.…”

Section: Stand-alone Models Of Connection Trips (Type 1 2 and 3)mentioning

confidence: 99%

Modelling connection trips to long-distance travel

Kristoffersson

Berglund²

2022

Eur. Transp. Res. Rev.

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Connection trips is often an important part of long-distance travel, especially for air travel. Models of long-distance travel would therefore benefit from a more detailed representation of the connection part. In this paper it is however shown that most models of connection trips are stand-alone models not integrated with the model for main mode. Only a handful models that integrate connection trip modelling into a large-scale transport model for long-distance travel are found. The connection trip models are classified into different types using a typology developed within the paper. The typology identifies nine model types that differ in how access/egress mode choice and terminal choice are handled. The scarce literature on connection trip modelling within large-scale transport modelling systems call for more research regarding detailed representation of access/egress mode choice and terminal choice, especially regarding the trade-off between model complexity and detailed representation, as well as whether the detailed representation of connection trips should primarily be conducted within the public transport network assignment or on the demand modelling side.

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“…Land use planning contributes to alleviating the influence of transportation on environment by determining the human activities' basic spatial settings. Therefore, various researches have focused on analyzing the connection of built environment and travel behavior at the time of the current study [34][35][36]. Based on New York Metropolitan Region dataset, Chen et al [13] assessed the impact of density on travel mode choice decisions for commuting trips under the condition of controlling for confounding factors.…”

Section: Influence Of Built Environment Features On Travel Behaviormentioning

confidence: 99%

Joint Analysis of the Commuting Departure Time and Travel Mode Choice: Role of the Built Environment

Yang

Ding

et al. 2018

Journal of Advanced Transportation

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This paper aims to conduct an empirical study to evaluate the influence of built environment features and socioeconomic factors on commuters’ simultaneous choice of departure time and travel mode. Using Kunming, China, as the study region, the 2015 Regional Household Travel Survey and 2016 Point of Interest data are used in the analysis. The results show that, in addition to socioeconomic factors, built environment, such as the density of residential building, employment, and service facility are correlated with joint choice behavior. Moreover, there exist differences regarding the influence of built environment and socioeconomic factors on departure time and travel mode choice. The dissimilarity parameters show that commuters prefer to shift travel mode than departure time generally when travel condition alters. In order to examine the policy measures’ potential performance, the paper conducts simulation tests based on the Monte Carlo method. The simulation results show that congestion pricing of car travel during peak hours can reduce the number of commuting trips, and reducing travel time of public transit would be a better strategy to attract more passengers during peak hours. Moreover, reasonable land use planning, such as building more bus stops around commuters’ home location, would be a long term and fundamental approach to reduce mobile-source emissions and attract more public transit passengers.

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The Impact of Perception toward the Built Environment in Airport Access Mode Choice Using Hybrid Choice Modeling

Cited by 7 publications

References 70 publications

Defining Psychometric Variables Related to Use of Autonomous Vehicles

Defining Psychometric Variables Related to Use of Autonomous Vehicles

Modelling connection trips to long-distance travel

Joint Analysis of the Commuting Departure Time and Travel Mode Choice: Role of the Built Environment

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