Journal of Transport and Land Use

Intrapersonal day-to-day travel variability and duration of household travel surveys: Moving beyond the one-day convention

Wei Li

Texas A&M University

Douglas Houston

University of California, Irvine

Marlon G. Boarnet

University of Southern California

Han Park

Texas A&M University

DOI: https://doi.org/10.5198/jtlu.2018.984

Keywords: Intrapersonal day-to-day travel variability, household travel survey, survey duration, Monte Carlo experiments

---

Abstract

By analyzing seven-day travel logs from Los Angeles during 2011–2013, we contribute to the understanding of intrapersonal day-to-day travel variability (IDTV) in relation to socio-demographic and land-use characteristics and the implication of travel survey duration for travel parameter estimates. Our main sample included 2,395 person-days from 352 individual participants in 219 households. Our analytical methods included linear regressions and random sampling experiments. Our Feasible Generalized Least Squares (FGLS) regression models revealed that many factors significantly influenced IDTV, such as gender, age, income, and household type. However, the observed socio-demographic and land-use characteristics could only explain a small portion of IDTV. The random sampling experiments enabled us to contrast travel variables measured from the seven-day master sample with those from subsamples of a shorter period (one to six days). The “optimal” duration for a travel survey may depend on the specific travel variables measured, and we provide evidence that studies of transit and non-motorized travel will require longer surveys than studies of car travel. In conclusion, the conventional one-day approach is likely to produce imprecise parameter estimates due to the intrapersonal day-to-day travel variability. We recommend that transportation professionals and policy makers consider shifting from the conventional one-day approach toward a multi-day approach. Surveys that focus on the modes of walking, biking, and transit should consider data collection for at least seven days.

---

References

Ampt, E. S. (2003). Respondent burden. In P. Jones & P. R. Stopher (Eds.), Transport survey quality and innovation (pp. 507–521). Bingley, UK: Emerald Group Publishing Limited.

Axhausen, K. W., Zimmermann, A., Schönfelder, S., Rindsfüser, G., & Haupt, T. (2002). Observing the rhythms of daily life: A six-week travel diary. Transportation, 29(2), 95–124.

Barnard, P. O. (1984). Use of an activity diary survey to examine travel and activity reporting in a home interview survey. Paper presented at the 9th Australian Transport Research Forum, Adelaide, May 15–16.

Bayarma, A., Kitamura, R., & Susilo, Y. O. (2007). Recurrence of daily travel patterns: Stochastic process approach to multiday travel behavior. Transportation Research Record: Journal of the Transportation Research Board, 2021(1), 55–63.

Bhat, C. R., Frusti, T., Zhao, H., Schönfelder, S., & Axhausen, K. W. (2004). Intershopping duration: An analysis using multiweek data. Transportation Research Part B: Methodological, 38(1), 39–60.

Bohte, W., & Maat, K. (2009). Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands. Transportation Research Part C: Emerging Technologies, 17(3), 285–297.

Buliung, R. N., Roorda, M. J., & Remmel, T. K. (2008). Exploring spatial variety in patterns of activity-travel behavior: Initial results from the Toronto Travel-Activity Panel Survey (TTAPS). Transportation, 35(6), 697–722.

California Department of Transportation. (2013). 2010-2012 California household travel survey final report. Retrieved from http://www.dot.ca.gov/hq/tsip/FinalReport.pdf

Carmel, Y., Paz, S., Jahashan, F., & Shoshany, M. (2009). Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management, 257(1), 370–377.

Chang, N.-B., Parvathinathan, G., & Breeden, J. B. (2008). Combining GIS with fuzzy multicriteria decision-making for landfill siting in a fast-growing urban region. Journal of Environmental Management, 87(1), 139–153.

Chen, A., Yang, H., Lo, H. K., & Tang, W. H. (1999). A capacity related reliability for transportation networks. Journal of Advanced Transportation, 33(2), 183–200.

Dill, J., & Broach, J. (2014). Travel to common destinations: An exploration using multiday GPS data. Transportation Research Record: Journal of the Transportation Research Board, 2413(-1), 84–91. doi:10.3141/2413-09.

Elango, V. V., Guensler, R., & Ogle, J. (2007). Day-to-day travel variability in the Commute Atlanta, Georgia, study. Transportation Research Record: Journal of the Transportation Research Board, 2014(-1), 39–49.

Golob, T. F., & Meurs, H. (1986). Biases in response over time in a seven-day travel diary. Transportation, 13(2), 163–181.

Goodwin, P. B. (1978). Intensity of car use in Oxford. Traffic engineering and control, 19(11), 514–517.

Hanson, S. (1982). The determinants of daily travel-activity patterns: Relative location and sociodemographic factors. Urban Geography, 3(3), 179–202.

Hanson, S., & Hanson, P. (1981). The impact of married women’s employment on household travel patterns: A Swedish example. Transportation, 10(2), 165–183.

Hanson, S., & Huff, J. O. (1981). Assessing day-to-day variability in complex travel patterns. Transportation Research Record, 891, 18–24.

Harrison, B. (1986). Electronic road pricing in Hong Kong III: Estimating and evaluating the effects. Traffic Engineering & Control, 27(1), 13–18.

Harvey, A. S. (1993). Guidelines for time use data collection. Social Indicators Research, 30(2-3), 197–228.

Hong, A., Boarnet, M. G., & Houston, D. (2016). New light rail transit and active travel: A longitudinal study. Transportation Research Part A: Policy and Practice, 92, 131–144.

Houston, D., Boarnet, M. G., Ferguson, G., & Spears, S. (2015). Can compact rail transit corridors transform the automobile city? Planning for more sustainable travel in Los Angeles. Urban Studies, 52(5), 938–959.

Houston, D., Luong, T. T., & Boarnet, M. G. (2014). Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns. Transportation Research Part C: Emerging Technologies, 48, 97–108.

Jara-Díaz, S., & Rosales-Salas, J. (2015). Understanding time use: Daily or weekly data? Transportation Research Part A: Policy and Practice, 76, 38–57.

Jones, P., & Clarke, M. (1988). The significance and measurement of variability in travel behavior. Transportation, 15(1), 65–87.

Karlsruhe Institute of Technology. (2015). MOP—The German mobility plan. Karlsruhe, Germany: Karlsruhe Institute of Technology.

Kennedy, P. (2008). A guide to econometrics (6th ed.). Hoboken, NJ: Blackwell Publishing Ltd.

Li, W. (1994). Reliability assessment of electrical power systems using Monte Carlo methods. New York: Springer.

Löchl, M., Axhausen, K., & Schönfelder, S. (2005). Analyzing Swiss longitudinal travel data. Paper presented at the 5th Swiss Transport Research Conference, Monte Verità, March.

Marble, D. F., Hanson, P. O., & Hanson, S. (1972). Household travel behavior study: Field operations and questionnaires. Evanston, IL: Transportation Center, Northwestern University.

Michigan Department of Transportation. (2005). 2004-2005 Comprehensive household travel data collection program: MI travel counts. Retrieved from http://www.michigan.gov/documents/MDOT_travelcounts_results_Final_Report__142283_7.pdf

Pas, E. I. (1983). A flexible and integrated methodology for analytical classification of daily travel-activity behavior. Transportation Science, 17(4), 405–429.

Pas, E. I. (1986). Multiday samples, parameter estimation precision, and data collection costs for least squares regression trip-generation models. Environment and Planning A, 18(1), 73–87.

Pas, E. I. (1987). Intrapersonal variability and model goodness-of-fit. Transportation Research Part A: General, 21(6), 431–438.

Pas, E. I., & Harvey, A. S. (1997). Time use research and travel demand analysis and modelling. In P. R. Stopher & M. E. H. Lee-Gosselin (Eds.), Understanding travel behavior in an era of change. New York: Pergamon.

Pas, E. I., & Koppelman, F. S. (1987). An examination of the determinants of day-to-day variability in individuals’ urban travel behavior. Transportation, 14(1), 3–20. doi:10.1007/BF00172463

Pas, E. I., & Sundar, S. (1995). Intrapersonal variability in daily urban travel behavior: Some additional evidence. Transportation, 22(2), 135–150.

Schlich, R., & Axhausen, K. W. (2003). Habitual travel behavior: Evidence from a six-week travel diary. Transportation, 30(1), 13–36.

Shapcott, M. (1978). Comparison of the use of time in Reading, England, with time use in other countries. Transactions of the Martin Centre for Architectural and Urban Studies, 3, 231–257.

Shen, L., & Stopher, P. R. (2014). Review of GPS travel survey and GPS data-processing methods. Transport Reviews, 34(3), 316–334.

Spears, S., Houston, D., & Boarnet, M. G. (2013). Illuminating the unseen in transit use: A framework for examining the effect of attitudes and perceptions on travel behavior. Transportation Research Part A: Policy and Practice, 58, 40–53.

STATA. (2017). Fit panel-data models by using GLS. Retrieved from https://www.stata.com/manuals13/xtxtgls.pdf

Stopher, P. R. (2012). Collecting, managing, and assessing data using sample surveys. Cambridge, UK: Cambridge University Press.

Stopher, P. R., Clifford, E., & Montes, M. (2008). Variability of travel over multiple days: Analysis of three panel waves. Transportation Research Record: Journal of the Transportation Research Board, 2054(1), 56–63.

Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367–381.

Stopher, P. R., Kockelman, K., Greaves, S. P., & Clifford, E. (2008). Reducing burden and sample sizes in multiday household travel surveys. Transportation Research Record: Journal of the Transportation Research Board, 2064(-1), 12–18.

Stopher, P. R., & Zhang, Y. (2011). Repetitiveness of daily travel. Transportation Research Record: Journal of the Transportation Research Board, 2230(1), 75–84.

Stopher, P. R., Zhang, Y., Armoogum, J., & Madre, J.-L. (2011). National household travel surveys: The case for Australia. Paper presented at the 34th Australasian Transport Research Forum (ATRF), Adelaide, South Australia.

Tran, N. L., Chikaraishi, M., Zhang, J., & Fujiwara, A. (2012). Exploring day-to-day variations in the bus usage behavior of motorcycle owners in hanoi. Procedia-Social and Behavioral Sciences, 43, 265–276.

UK Department of Transport. (2016). National travel survey statistics. Retrieved from https://www.gov.uk/government/collections/national-travel-survey-statistics

Venter, C. J., & Joubert, J. W. (2013). Use of multisource global positioning system data to characterize multiday driving patterns and fuel usage in a large urban region. Transportation Research Record: Journal of the Transportation Research Board, 2338(1), 1–10.

Wooldridge, J. M. (2006; p 424–426). Introductory econometrics: A modern approach (3rd ed). Masson, OH: Thomson South-Western.

Xian, G., & Crane, M. (2005). Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 97(2), 203–215.