An analysis of activity scheduling behavior of airport travelers |
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| Affiliation: | 1. School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou 221116, China;2. College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;1. Emergency and Trauma Intensive Care Unit, Azienda Ospedaliero Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;2. Emergency Department, Azienda Ospedaliero Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;3. Intensive Care Unit, Azienda Ospedaliera di Perugia, Piazzale Menghini, 1 – 06156 Sant''Andrea delle Fratte, Perugia, Italy;4. Emergency Department, Ospedale Santa Maria Nuova, ASF, piazza Santa Maria Nuova n.1 50122, Florence, Italy;5. Emergency Department, Stabilimento Ospedaliero Nottola – AUSL 7 di Siena, piazzale Rosselli, 26 – 53100, Siena, Italy;6. Emergency Department, Ospedale Santa Maria Annunziata, ASF, Ponte a Niccheri, 58, Via dell''Antella, 50012 – Bagno a Ripoli, Florence, Italy;7. Emergency Department, Ospedale Unico della Versilia, USL 12 Viareggio, via Aurelia sud 312 55041 – Lido di Camaiore, Lucca, Italy;8. Emergency Department, Ospedale della Misericordia, ASL 9 Via Cimabue 152, 58100 Grosseto, Italy;9. Emergency Department, Azienda Universitaria Ospedaliera Senese Le Scotte, viale Bracci 14, 53100 Siena, Italy;10. Emergency Department, Presidio Ospedaliero S. Luca, AUSL 2 Lucca, Via Guglielmo Lippi Francesconi, 55100 Lucca, Italy;11. Emergency Department, Ospedale San Jacopo, AUSL 3 di Pistoia, via Ciliegiole 97, 51100 Pistoia, Italy;12. Emergency Department, Alta Valdelsa, AUSL 7 Poggibonsi, Siena, 53036 Siena, Italy;13. Emergency Department, Ospedale Nuovo del Mugello, ASF, Viale Resistenza, 60 – 50032 Borgo San Lorenzo, Florence, Italy |
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| Abstract: | A nested logit model is presented that can be used to predict the activity pattern of travelers inside an airport based on their socio-demographical characteristics (e.g. gender, age), group size, and travel related information (e.g. number of bags, airport size, and total available time). The availability of such a model enhances representation of the behavior dynamics when simulating airport pedestrian traffic. An internet-based revealed preference survey was used to collect data from persons that visit airports including both travelers and non-travelers. The survey focused on the agenda of subjects’ most recent airport trip, the frequency and attitude concerning certain types of activities they performed inside the airport, and the socio-demographic characteristics of each respondent. Three possible nested logit model structures are analyzed and one has been identified as a plausible and statistically acceptable nested structure. Also, the empirical results demonstrate the applicability of a nested logit model for use in identifying travelers’ activity patterns in an airport. |
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| Keywords: | Behavior simulation Nested logit model Transportation modeling Behavioral modeling Discrete choice modeling Air passenger study |
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