When it comes to travel behavior research, commuting behavior tends to take the spotlight. Travel surveys tend to provide insight on travel on a regular day, and at times refrain from collecting data on people’s travel on holidays. Is there something that makes travel during the holidays different?
Since it’s right around the corner, let’s look at Thanksgiving as an example. Historically, the days surrounding Thanksgiving are some of the busiest when it comes to holiday travel. Last year, AAA estimated more than 55 million travelers for Thanksgiving, of which 89% were estimated to hit the roads. The skies are no exception for holiday travel congestion either, where last year Airlines for America estimated 31.6 million people to fly during the 12-day Thanksgiving travel period (November 22 to December 3 last year). Thanksgiving may be a little different during COVID-19, given the current CDC travel guidelines and differing state guidelines. To get a sense of what Thanksgiving may look like this year, summer travel estimates were down 15% compared to the same period last year, with AAA estimating 700 million trips over the summer months.
Traffic congestion, travel mode choice, peak travel hours… sounds a lot like the issues surrounding commuting. There are a lot of studies concerning commutes and travel on regular days, but is research about holiday travel any different?
What kind of research is out there about holiday travel?
A look at the academic literature about holiday travel yielded interesting research studies from different parts of the world.
In China, Li, Weng, Shao, Guo (2016) conducted a study to find out travel preferences of households in China for one of the major holidays in China, when people tend to travel home to visit family. To gather data, the researchers conducted a survey to find out what factors influence the choice to drive during the holidays (i.e., number of cars owned, gender, driver’s license ownership, and number of travel companions). Results showed regardless of number of cars owned, households with high household income and those who have a driver’s license have a higher probability to drive during the holidays. For households that own only one car, they are unlikely to choose to drive for the holidays if the time they will stay is long (i.e., longer than 4 hours). Researchers attribute this aversion to avoiding expensive parking fees. Households who own more than one car are more likely to choose to travel by car when traveling with children and elder members of the family.
In the UK, Dickinson, et al (2017) conducted a study about a collaborative travel app aimed at encouraging carsharing among vacationers heading to camping sites. The research provided insights on the veracity of having a successful carsharing culture for trips and identified what social and cultural barriers are faced by a carsharing campaign. The findings show that factors underpinning social exchange are more indicative of motivations for use: strength of social ties, trust, and reciprocity.
What kinds of data and research methodology are used to study holiday travel?
These examples of holiday travel research used data and research methodology not very different from other travel behavior studies.
Trip card data from transit users was used by Chu (2015) to conduct research about understanding travel behavior in Canada. The study utilized two years’ worth of smart card transaction data in Montreal, Canada, reporting observable decreases in fare card usage around the holidays.
For researchers in China seeking to understand travel preference for holiday travel, a survey was conducted specifically about travel preferences for the tomb-sweeping festival, a holiday in China where people tend to travel home to visit family (Li, et al, 2016). The researchers acknowledged the difference in travel mode choice behavior between routine travel (commute or work travel) and for holidays, the latter characterized by long distances, which led the researchers to conduct a survey specifically about holiday travel preferences.
Similarly, Han, Zhang and Wang (2020) conducted a survey to determine whether using the Integrated Travel Reservation Information (ITRI) affected holiday travel behavior in China. The researchers define ITRI as a set of information which includes tickets sold ratio, traffic condition, real-time number of tourists, and route planning, that is used by travelers in China as a reference. The research showed that the higher the tickets sold ratio, the higher the probability of travel instead on non-holiday periods. The study’s findings also revealed that the following factors affect departure time choice and destination: age, level of education, number of visits, content and query method of ITRI.
What are some examples of insights about holiday travel research?
Much like routine travel like commuting or traveling to work, information obtained before a trip helps with trip planning. Research about ITRI’s impact on how people travel during holidays in China by Han, Zhang and Wang (2020) showed how some travelers adjust their departure time choice and destination based on information they obtained before going on a trip, such as information on tickets sold ratio and traffic condition.
Similarly, research conducted by Wang, Shao, Li, Weng & Ji (2015) to analyze the relationship between travel behavior during the holidays and Integrated Multimodal Travel Information (IMTI) obtained before and during the trip. The researchers define IMTI as information for navigation, real time information alerts, and route planning. The research conducted by Wang, et al. (2015) found that information obtained before the trip has a stronger effect on holiday travel than information obtained during the trip; that is, there is a higher possibility of trip mode or travel route changes, the more information a traveler obtains before the trip.
The environmental impact of holiday travel was also an avenue for holiday travel research. A study by Christensen (2016) estimated greenhouse gas (GHG) emissions of long-distance travel for Denmark and surrounding regions by air, train, and car travel. Aside from estimating and comparing GHG emissions of different modes of travel, the objective of the study was to add more insight in the policy sphere by providing evidence for Denmark and surrounding regions that can help shift balance of travel demand to more sustainable modes of travel that have less emissions per person.
This shortlist of holiday travel research provides a slice of the newest insights and methods for understanding travel behavior of people during the holidays. The motivations, data, and methods are not very different from research about work commutes or daily travel. Holiday travel is an area of research in travel behavior and transportation research that can give us valuable insights about another facet of mobility.
Christensen, L. (2016). Environmental impact of long distance travel. Transportation Research Procedia, 14, 850-859. doi: 10.1016/j.trpro.2016.05.033
Chu, K. K. A. (2015). Two-year worth of smart card transaction data – extracting longitudinal observations for the understanding of travel behaviour. Transportation Research Procedia, 11, 365-380. doi: 10.1016/j.trpro.2015.12.031
Dickinson, J., Hibbert, J., Filimonau, V., Cherrett, T., Davies, N., Norgate, S., Speed, C., & Wistanley, C. (2017). Implementing smartphone enabled collaborative travel: Routes to success in the tourism domain. Journal of Transport Geography, 59, 100-110. http://dx.doi.org/10.1016/j.jtrangeo.2017.01.011
Han, Y., Zhang, T., Wang, M. (2020). Holiday travel behavior analysis and empirical study with Integrated Travel Reservation Information usage. Transportation Research Part A, 134, 130-151. https://doi.org/10.1016/j.tra.2020.02.005
Li, Ju., Weng, J., Shao, C., & Guo, H. (2016). Cluster-based logistic regression model for holiday travel mode choice. Procedia Engineering, 137, 729-737. doi: 10.1016/j.proeng.2016.01.310
Wang, B., Shao, C., Li, J., Weng, J., & Ji, X. (2015). Holiday travel behavior analysis and empirical study under integrated multimodal travel information service. Transport Policy, 39, 21-36. http://dx.doi.org/10.1016/j.tranpol.2014.12.005