An airline itinerary choice model that includes the option to delay the decision

Freund-Feinstein, Uzi; Bekhor, Shlomo

doi:10.1016/j.tra.2016.12.004

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Besides total travel time, Srinivasan et al [4] also investigated the efect of airline inspection and boarding time on passengers' choice toward air mode. Regarding air mode, transfer time (sometimes is refected by number of transfer) of an itinerary is an attribute that commonly adopted in the literature (e.g., [8][9][10]).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Impacts of Stay-in-Place Policy of COVID-19 Pandemic during the Chinese Spring Festival: A Stated Preference Approach

Pan

Feng

Chen

2023

Journal of Advanced Transportation

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This paper aims to investigate Chinese people’s willingness to stay in the city where they work when the Spring Festival meets the COVID-19 pandemic. Specifically, a stated choice experiment about intercity travel including three homecoming trips (i.e., trips carried by conventional railway, high-speed railways, and private car) and the option “stay in place” was designed. Respondents were requested to choose the most preferred alternative in the context of the current situation of the COVID-19 pandemic and relevant policies. Based on the data collected from 800 respondents, a latent class mixed logit model was developed and estimated to capture the potential correlations within alternatives and respondents and the preference heterogeneity between respondents. Two latent classes were identified, one of which paid more attention to epidemic prevention policies while the other cared more about the characteristics of homecoming trips. Results show that people’s willingness to stay in the city of work is largely dependent on epidemic prevention policies in their hometowns and decisions of social network members.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the Impacts of Stay-in-Place Policy of COVID-19 Pandemic during the Chinese Spring Festival: A Stated Preference Approach

Pan

Feng

Chen

2023

Journal of Advanced Transportation

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“…However, the logit model has been criticized for its underlying assumption that should be independently and identically distributed (IID) (Sheffi, 1985), and nested logit models have been suggested to deal with the correlation. Hess and Polak (2006) and Drabas and Wu (2013) included nesting structures in their airline itinerary-choice models, and Freund-Feinstein and Bekhor (2017) considered both heterogeneity and correlation in their mixed cross-nested logit model. However, the characteristics of airline itineraries differ from those of train itineraries, so we cannot directly apply these findings to our study.…”

Section: Stochastic Passenger Assignmentmentioning

confidence: 99%

Train schedule optimization based on schedule-based stochastic passenger assignment

Xie

Wong

Shou-yi

et al. 2020

Transportation Research Part E: Logistics and Transportation Re

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“…e most widely used bottom-up method for itinerary demand forecasting is the discrete choice model, which is based on the simulation of passengers' itinerary choice making [12][13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

Air Transportation Direct Share Analysis and Forecast

Zheng

Liu

Wei

2020

Journal of Advanced Transportation

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Air transportation direct share is the ratio of direct passengers to total passengers on a directional origin and destination (O&D) pair. Direct share is an essential factor of passenger flow distribution and shows passengers’ general preference for direct flight services on a certain O&D. A better understanding and a more accurate forecast of direct share can benefit air transportation planners, airlines, and airports in multiple ways. In most of the previous research and applications, it is commonly assumed that direct share is a fixed ratio, which contradicts the air transportation practice. In the Federal Aviation Administration (FAA) Terminal Area Forecast (TAF), the O&D direct share is forecasted as a constant based on the latest observation of direct share on the O&D. To find factors which have significant impacts on O&D direct share and to build an accurate model for O&D direct share forecasting, both parametric and nonparametric machine learning models are investigated in this research. We propose a novel category-based learning method which can provide better forecasting performance compared to employing the single modeling method for O&D direct share forecasting. Based on the comparison, the developed category-based learning model is a promising replacement for the model used for O&D direct share forecasting by the FAA TAF.

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An airline itinerary choice model that includes the option to delay the decision

Cited by 7 publications

References 18 publications

Assessing the Impacts of Stay-in-Place Policy of COVID-19 Pandemic during the Chinese Spring Festival: A Stated Preference Approach

Assessing the Impacts of Stay-in-Place Policy of COVID-19 Pandemic during the Chinese Spring Festival: A Stated Preference Approach

Train schedule optimization based on schedule-based stochastic passenger assignment

Air Transportation Direct Share Analysis and Forecast

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