Technical Papers
Mar 2, 2016

Classification of Tours in the U.S. National Household Travel Survey through Clustering Techniques

Publication: Journal of Transportation Engineering
Volume 142, Issue 6

Abstract

Tours are increasingly being considered as an appropriate unit of observation of mobility behaviors and are one of the key ideas underpinning contemporary activity-based modeling approaches. Identifying typologies of tours would benefit both modelers and decision makers, striving to set up more tailored actions aimed at promoting environmentally benign travel choices. Different a priori classifications based on activity kinds have been proposed, none of which seems clearly preferable on empirical grounds. This paper takes a complementary approach and defines a data-driven segmentation through a cluster analysis of tours that were derived from the trip records from a United States national survey. The socioeconomic characterization of each cluster is finally carried out to link travelers’ profiles with specific kinds of tours. Four main tour clusters have thus been identified: nonwork tours for compulsory activities done by young individuals, tours done by elder or retired persons, short and secondary tours within the travel day, and tours dominated by the working activity. Their relevance on a modeling and policy viewpoint is discussed.

Get full access to this article

View all available purchase options and get full access to this article.

References

Anable, J. (2005). ““Complacent car addicts” or “aspiring environmentalists”? Identifying travel behaviour segments using attitude theory.” Transp. Policy, 12(1), 65–78.
Arentze, T., Timmermans, H., Hofman, F., and Kalfs, N. (2000). “Data needs, data collection, and data quality requirements of activity-based transport demand models.”, Transportation Research Board, Washington, DC.
Axhausen, K. W. (2007). “Definition of movement and activity for transport modelling.” Handbook of transport modelling (handbooks in transport), 2nd Ed., D. A. Hensher and K. J. Button, eds., Emerald, Bingley, U.K.
Bayart, C., and Bonnel, P. (2012). “Combining web and face-to-face in travel surveys: Comparability challenges?” Transportation, 39(6), 1147–1171.
Biagioni, J. P., Szczurek, P. M., Nelson, P. C., and Mohammadian, A. (2009). “Tour-based mode choice modeling: Using an ensemble of conditional and unconditional data mining classifiers.” Proc., Transportation Research Board 88th Annual Meeting, Transportation Research Board, Washington, DC.
Bowman, J. L., and Ben-Akiva, M. E. (2001). “Activity-based disaggregate travel demand model system with activity schedules.” Transp. Res. Part A: Policy Pract., 35(1), 1–28.
Buliung, R. N., and Kanaroglou, P. S. (2007). “Activity-travel behaviour research: Conceptual issues, state of the art, and emerging perspectives on behavioural analysis and simulation modelling.” Transp. Rev., 27(2), 151–187.
Currie, G., and Delbosc, A. (2011). “Exploring the trip chaining behaviour of public transport users in Melbourne.” Transp. Policy, 18(1), 204–210.
Cyganski, R., Justen, A., Schulz, A., and Köhler, K. (2013). “Generation of coherent trip chains for travel behavior analysis and modeling.” Proc., European Transport Conf., Association for European Transport, U.K.
Diana, M., and Mokhtarian, P. L. (2009). “Grouping travelers on the basis of their different car and transit levels of use.” Transportation, 36(4), 455–467.
Diana, M., and Pronello, C. (2010). “Traveler segmentation strategy with nominal variables through correspondence analysis.” Transp. Policy, 17(3), 183–190.
Doherty, S. T. (2006). “Should we abandon activity type analysis? Redefining activities by their salient attributes.” Transportation, 33(6), 517–536.
Doherty, S. T., and Mohammadian, A. (2011). “The validity of using activity type to structure tour-based scheduling models.” Transportation, 38(1), 45–63.
Elgar, A., and Bekhor, S. (2004). “Car-rider segmentation according to riding status and investment in car mobility.” Transp. Res. Rec., 1894, 109–116.
Fowkes, A. S. (2000). “Recent developments in stated preference techniques in transport research.” Stated preference modelling techniques, J. de D. Ortuzar, ed., PRTC, Westminster, U.K., 37–52.
Han, J., Kamber, M., and Pei, J. (2012). Data mining: Concepts and techniques, 3rd Ed., Morgan Kaufmann, Waltham, MA.
Hensher, D. A., and Reyes, A. J. (2000). “Trip chaining as a barrier to the propensity to use public transport.” Transportation, 27(4), 341–361.
Ho, C. Q., and Mulley, C. (2013). “Multiple purposes at single destination: A key to a better understanding of the relationship between tour complexity and mode choice.” Transp. Res. Part A: Policy Pract., 49, 206–219.
Jensen, M. (1999). “Passion and heart in transport—A sociological analysis on transport behavior.” Transp. Policy, 6(1), 19–33.
Jin, X., Wu, J., Asgari, H., and Argote, J. A. (2013). “Examining trip misreporting behavior using gps-assisted household travel surveys.” Proc., Transportation Research Board 92th Annual Meeting, Transportation Research Board, Washington, DC.
Joh, C. H., Arentze, T., Hofman, F., and Timmermans, H. (2002). “Activity pattern similarity: A multidimensional sequence alignment method.” Transp. Res. Part B: Methodol., 36(5), 385–403.
Krizek, K. J. (2003). “Neighborhood services, trip purpose, and tour-based travel.” Transportation, 30(4), 387–410.
Louviere, J. J., Hensher, D. A., and Swait, J. D. (2000). “Cross validity and external validity of SP models.” Stated choice methods—Analysis and application, Cambridge University Press, Cambridge, U.K., 354–381.
Lyons, G., Jain, J., Susilo, Y., and Atkins, S. (2013). “Comparing rail passengers’ travel time use in Great Britain between 2004 and 2010.” Mobilities, 8(4), 560–579.
McGuckin, N., Zmud, J., and Nakamoto, Y. (2005). “Trip-chaining trends in the United States: Understanding travel behavior for policy making.” Transp. Res. Rec., 1917, 199–204.
Mokhtarian, P. L., Papon, F., Goulard, M., and Diana, M. (2015). “What makes travel pleasant and/or tiring? An investigation based on the French national travel survey.” Transportation, 42(6), 1103–1128.
NHTS (National Household Travel Survey). (2009). “2009 national household travel survey.” Federal Highway Administration, U.S. Dept. of Transportation 〈http://nhts.ornl.gov〉 (Jan. 22, 2016).
Nishii, K., Kondo, K., and Kitamura, R. (1988). “Empirical analysis of trip chaining behavior.” Transp. Res. Rec., 1203, 48–59.
Nowrouzian, R., and Srinivasan, S. (2012). “Empirical analysis of spatial transferability of tour-generation models.” Transp. Res. Rec., 2302, 14–22.
O’Fallon, C., and Sullivan, C. (2005). “Trip chains and tours: Definitional issues associated with household travel surveys.” Proc., 28th Australasian Transport Research Forum, Sydney, Australia.
Primerano, F., Taylor, M. A., Pitaksringkarn, L., and Tisato, P. (2008). “Defining and understanding trip chaining behaviour.” Transportation, 35(1), 55–72.
Rasouli, S., and Timmermans, H. (2014). “Judgments of travel experiences, activity envelopes, trip features and multi-tasking: A panel effects regression model specification.” Transp. Res. Part A: Policy Pract., 63, 67–75.
Shiftan, Y., et al. (2003). “Activity-based modeling as a tool for better understanding travel behaviour.” Proc., 10th Conf. of the Int. Association of Travel Behavior Research, Lucerne, Switzerland.
Steinbach, M., Karypis, G., and Kumar, V. (2000). “A comparison of document clustering techniques.”, Univ. of Minnesota, Minneapolis.
Stopher, P., Jiang, Q., and Zhang, Y. (2010). “Tour-based analysis of multi-day GPS data.” Proc., 12th World Conf. for Transport Research, Lisbon, Portugal.
Strathman, J. G., and Dueker, K. J. (1995). “Understanding trip chaining. Special reports on trip and vehicle attributes.”, U.S. Dept. of Transportation, Washington DC.
Tan, P. T., Steinbach, M., and Kumar, V. (2006). Introduction to data mining, Pearson Addison Wesley, Boston.
Timmermans, H., et al. (2003). “Spatial context and the complexity of daily travel patterns: An international comparison.” J. Transp. Geogr., 11(1), 37–46.
Valiquette, F., and Morency, C. (2010). “Trip chaining and its impact on travel behaviour.” Proc., 12th World Conf. on Transport Research, Lisbon, Portugal.
Vande Walle, S., and Steenberghen, T. (2006). “Space and time related determinants of public transport use in trip chains.” Transp. Res. Part A: Policy Pract., 40(2), 151–162.
Wolf, J., Lechl, M., Thompson, M., and Arce, C. (2003). “Trip rate analysis in GPS-enhanced personal travel surveys.” Transport survey quality and innovation, P. R. Stopher and P. M. Jones, eds., Elsevier, London, 483–498.
Yagi, S., and Mohammadian, A. K. (2008). “Modeling daily activity-travel tour patterns incorporating activity scheduling decision rules.” Transp. Res. Rec., 2076(1), 123–131.
Ye, X., Pendyala, R. M., and Gottardi, G. (2007). “An exploration of the relationship between mode choice and complexity of trip chaining patterns.” Transp. Res. Part B: Methodol., 41(1), 96–113.

Information & Authors

Information

Published In

Go to Journal of Transportation Engineering
Journal of Transportation Engineering
Volume 142Issue 6June 2016

History

Received: Jul 15, 2015
Accepted: Dec 22, 2015
Published online: Mar 2, 2016
Published in print: Jun 1, 2016
Discussion open until: Aug 2, 2016

Permissions

Request permissions for this article.

Authors

Affiliations

Miriam Pirra, Ph.D. [email protected]
Research Fellow, Dept. of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. E-mail: [email protected]
Marco Diana, Ph.D. [email protected]
Associate Professor, Dept. of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy (corresponding author). E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share