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Abstract
Data collection activities related to travel require large amounts of financial and human resources to be conducted successfully. When available resources are scarce, the information hidden in these data sets needs to be exploited, both to increase their added value and to gain support among decision makers not to discontinue such efforts. This study assessed the use of a data mining technique, association analysis, to understand better the patterns of mode use from the 2009 U.S. National Household Travel Survey. Only variables related to self-reported levels of use of the different transportation means are considered, along with those useful to the socioeconomic characterization of the respondents. Association rules potentially showed a substitution effect between cars and public transportation, in economic terms but such an effect was not observed between public transportation and nonmotorized modes (e.g., bicycling and walking). This effect was a policy-relevant finding, because transit marketing should be targeted to car drivers rather than to bikers or walkers for real improvement in the environmental performance of any transportation system. Given the competitive advantage of private modes extensively discussed in the literature, modal diversion from car to transit is seldom observed in practice. However, after such a factor was controlled, the results suggest that modal diversion should mainly occur from cars to transit rather than from nonmotorized modes to transit.
References
| 1. | Agrawal, R. , Imielinski, T. , and Swami, A. Mining Association Rules Between Sets of Items in Large Databases. Proc., 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., 1993, pp. 207–216. http://doi.acm.org/10.1145/170035.170072. Accessed Feb. 29, 2012. Google Scholar |
| 2. | Han, J. , Cheng, H. , Xin, D. , and Yan, X. . Frequent Pattern Mining: Current Status and Future Directions. Data Mining and Knowledge Discovery, Vol. 15, No. 1, 2007, pp. 55–86. Google Scholar |
| 3. | Keuleers, B. , Wets, G. , Arentze, T. , and Timmermans, H. . Association Rules in Identification of Spatial–Temporal Patterns in Multiday Activity Diary Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1752, TRB, National Research Council, Washington, D.C., 2001, pp. 32–37. Google Scholar |
| 4. | Keuleers, B. , Wets, G. , Timmermans, H. , Arentze, T. , and Vanhoof, K. Stationary and Time-Varying Patterns in Activity Diary Panel Data: Explorative Analysis with Association Rules. In Transportation Research Record: Journal of the Transportation Research Board, No. 1807, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 9–15. Google Scholar |
| 5. | Gong, X. , and Liu, X. A Data Mining–Based Algorithm for Traffic Network Flow Forecasting. Proc., IEEE International Conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS), Boston, Mass., 2003, pp. 243–248. http://dx.doi.org/10.1109/KIMAS.2003.1245052. Accessed Feb. 29, 2012. Google Scholar |
| 6. | Geurts, K. , Thomas, I. , and Wets, G. Understanding Spatial Concentrations of Road Accidents Using Frequent Item Sets. Accident Analysis and Prevention, Vol. 37, No. 4, 2005, pp. 787–799. Google Scholar |
| 7. | Pande, A. , and Abdel-Aty, M.A . Discovering Indirect Associations in Crash Data Through Probe Attributes. In Transportation Research Record: Journal of the Transportation Research Board, No. 2083, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 170–179. Google Scholar | SAGE Journals |
| 8. | Jensen, M. Passion and Heart in Transport—A Sociological Analysis on Transport Behavior. Transport Policy, Vol. 6, No. 1, 1999, pp. 19–33. Google Scholar |
| 9. | Anable, J. “Complacent Car Addicts” or “Aspiring Environmentalists”? Identifying Travel Behaviour Segments Using Attitude Theory. Transport Policy, Vol. 12, No. 1, 2005, pp. 65–78. Google Scholar |
| 10. | Diana, M. , and Mokhtarian, P.L. Grouping Travelers on the Basis of Their Different Car and Transit Levels of Use. Transportation, Vol. 36, No. 4, 2009, pp. 455–467. Google Scholar |
| 11. | Vredin Johansson, M. , Heldt, T. , and Johansson, P. The Effects of Attitudes and Personality Traits on Mode Choice. Transportation Research Part A, Vol. 40, No. 6, 2006, pp. 507–525. Google Scholar |
| 12. | Scheiner, J. , and Holz-Rau, C. . Travel Mode Choice: Affected by Objective or Subjective Determinants? Transportation, Vol. 34, No. 4, 2007, pp. 487–511. Google Scholar |
| 13. | Domarchi, C. , Tudela, A. , and Gonzales, A. Effect of Attitudes, Habit and Affective Appraisal on Mode Choice: An Application to University Workers. Transportation, Vol. 35, No. 3, 2008, pp. 585–599. Google Scholar |
| 14. | Diana, M. From Mode Choice to Modal Diversion: A New Behavioural Paradigm and an Application to the Study of the Demand for Innovative Transport Services. Technological Forecasting and Social Change, Vol. 77, No. 3, 2010, pp. 429–441. Google Scholar |
| 15. | Tan, P.-T. , Steinbach, M. , and Kumar, V. Introduction to Data Mining. Pearson Addison Wesley, Boston, Mass., 2006. Google Scholar |
| 16. | Nisbet, R. , Elder, J. , and Miner, G. Handbook of Statistical Analysis and Data Mining Applications. Academic Press, Burlington, Mass., 2009. Google Scholar |
| 17. | Hahsler, M. , Grün, B. , Hornik, K. , and Buchta, C. Introduction to Arules—A Computational Environment for Mining Association Rules and Frequent Item Sets. 2010. http://cran.r-project.org/web/packages/arules/vignettes/arules.pdf. Accessed Feb. 29, 2012. Google Scholar |
| 18. | Golob, T.F. , and Hensher, D.A. The Trip Chaining Activity of Sydney Residents: A Cross-Section Assessment by Age Group with a Focus on Seniors. Journal of Transport Geography, Vol. 15, No. 4, 2007, pp. 298–312. Google Scholar |
| 19. | Diana, M. , and Pronello, C. Traveler Segmentation Strategy with Nominal Variables Through Correspondence Analysis. Transport Policy, Vol. 17, No. 3, 2010, pp. 183–190. Google Scholar |
| 20. | U.S. Census Bureau . 2006–2008 American Community Survey 3-Year Estimates—Table S1501. http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml. Accessed Feb. 29, 2012. Google Scholar |
