| People | Locations | Statistics |
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| Sourd, Romain Crastes Dit |
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| Marton, Peter |
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| Toaza, Bladimir |
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| Lubashevsky, Katrin |
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| Ambros, Jiří |
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| Niederdränk, Simon |
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| Khoshkha, Kaveh |
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| Brenner, Thomas |
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| Badea, Andrei | Delft |
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| Michálek, Tomáš | Pardubice |
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| Jensen, Anders Fjendbo | Kongens Lyngby |
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| Le Goff, A. |
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| Greer, Ross |
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| Gutiérrez, Javier | Madrid |
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| Sagues, Mikel |
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| Eggermond, Michael Van |
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| Milica Milovanović, M. |
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| Carrasco, Juan-Antonio |
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| Groen, Eric L. |
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| Tzenos, Panagiotis |
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| Mesas, Juan-José |
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| Oikonomou, Maria G. |
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| Messiou, Chrysovalanto |
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| Giuliani, Felice |
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| Roussou, Julia |
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Circella, Giovanni
in Cooperation with on an Cooperation-Score of 37%
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Publications (15/15 displayed)
- 2025Teleworkers and physical commuters during the COVID-19 pandemic: the change in mobility related attitudes and the intention to telecommute in the future
- 2024Investigating Objective and Subjective Factors Influencing the Frequency and Purpose of E-Scooter Tripscitations
- 2023Conclusion: Reflections and Lessons from the Pandemic
- 2023Changes in Active Travel During the COVID-19 Pandemiccitations
- 2023Combining disparate surveys across time to study satisfaction with life: the effects of study context, sampling method, and transport attributescitations
- 2023Adoption of Telecommuting and Changes in Travel Behavior in Southern California During the COVID-19 Pandemiccitations
- 2021Do millennials value travel time differently because of productive multitasking? A revealed-preference study of Northern California commuterscitations
- 2021Who doesn’t mind waiting? Examining the relationships between waiting attitudes and person- and travel-related attributescitations
- 2021What drives the gap? Applying the Blinder–Oaxaca decomposition method to examine generational differences in transportation-related attitudescitations
- 2021ICT, Virtual and In-Person Activity Participation, and Travel Choice Analysiscitations
- 2020Information and Communication Technologies(ICT), Activity Decisions,and Travel Choices: 20 years into the Second Millennium and where do we go next?
- 2020Are millennials more multimodal? A latent-class cluster analysis with attitudes and preferences among millennial and Generation X commuters in Californiacitations
- 2019Millennials in cities: Comparing travel behaviour trends across six case study regionscitations
- 2018Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experimentcitations
- 2015The estimation of changes in rail ridership through an onboard survey: did free Wi-Fi make a difference to Amtrak’s Capitol Corridor service?citations
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document
The estimation of changes in rail ridership through an onboard survey: did free Wi-Fi make a difference to Amtrak’s Capitol Corridor service?
Abstract
Amtrak launched free Wi-Fi internet service (“AmtrakConnect”) on all trains of the California Capitol Corridor route (CC) on November 28, 2011. In March 2012, an onboard survey was conducted to evaluate the impact of the Wi-Fi service on ridership. We develop descriptive statistics and estimate a linear regression model of the impact of Wi-Fi on passengers’ trip frequency. As higher-frequency riders are overrepresented in the original sample, we weight cases to reflect the distribution of passengers , rather than person - trips , more accurately. We segment the linear regression model for three groups of travelers, based on their ridership frequency, to better understand the impact of selected variables on the expected number of trips in 2012. Several conventional factors (trip frequency in 2011, trip purpose, station-to-station distance and employment) as well as Wi-Fi have some impact on the self-reported projected trip frequency in 2012. Using the estimated parameters from the model, the expected number of trips on CC trains for 2012 is 2.7 % higher than it would have been without free Wi-Fi. In particular, new riders expect to make 8.6 % more trips than if Wi-Fi were not available, while the expected number of trips made by lower-frequency continuing riders (those using CC less than once a week in 2011) and higher-frequency continuing riders (those using CC once a week or more in 2011) increase by 6.2 and 1.0 %, respectively.
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