[en] To support policy makers combating travel-related externalities, quality data are required for the design and management of transportation systems and policies. To this end, much money has been spent on collecting household- and person-based data. The main objective of this paper is to assess the quality of origin-destination (O-D) matrices derived from household activity travel surveys. To this purpose, a Monte Carlo experiment is set up to estimate the precision of O-D matrices given different sampling rates. The Belgian 2001 census data, containing work- and school-related travel information for all 10,296,350 residents, are used for the experiment. For different sampling rates, 2,000 random stratified samples are drawn. For each sample, three O-D matrices are composed: one at the municipality level, one at the district level, and one at the provincial level. The correspondence between the samples and the population is assessed by using the mean absolute percentage error (MAPE) and a censored version of the MAPE (MCAPE). The results show that no accurate O-D matrices can be derived directly from these surveys. Only when half of the population is queried is an acceptable O-D matrix obtained at the provincial level. Therefore, use of additional information to grasp better the behavioral realism underlying destination choices and collection of information about particular O-D pairs by means of vehicle intercept surveys are recommended. In addition, results suggest using the MCAPE next to traditional criteria to examine dissimilarities between different O-D matrices. An important avenue for further research is the investigation of the effect of sampling proportions on travel demand model outcomes.
Research Center/Unit :
Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège LEMA - Local Environment Management and Analysis
Disciplines :
Civil engineering Special economic topics (health, labor, transportation...)
Haustein, S., and M. Hunecke. Reduced Use of Environmentally Friendly Modes of Transportation Caused by Perceived Mobility Necessities: An Extension of the Theory of Planned Behavior. Journal of Applied Social Psychology, Vol. 37, No. 8, 2007, pp. 1856-1883.
Steg, L. Can Public Transport Compete with the Private Car? IATSS Research, Vol. 27, No. 2, 2003, pp. 27-35.
TRB Committee on Travel Survey Methods. The Online Travel Survey Manual: A Dynamic Document for Transportation Professionals. http://trbtsm.wiki.zoho. com. Accessed July 22, 2009.
Stopher, P., R. Alsnih, C. Wilmot, C. Stecher, J. Pratt, J. Zmud, W. Mix, M. Freedman, K. Axhausen, M. Lee-Gosselin, A. Pisarski, and W. Brög. NCHRP Report 571: Standardized Procedures for Personal Travel Surveys. Transportation Research Board of the National Academies, Washington, D.C., 2008.
Tourangeau, R., M. Zimowski, and R. Ghadialy. An Introduction to Panel Surveys in Transportation Studies. Report DOT-T-84. FHWA, U.S. Department of Transportation, 1997.
Stopher, P. R., and S. P. Greaves. Household Travel Surveys: Where Are We Going? Transportation Research Part A: Policy and Practice, Vol. 41, No. 5, 2007, pp. 33-40.
Rubinstein, R. Y. Simulation and the Monte Carlo Method. John Wiley and Sons, Inc., New York, 1981.
Fan, X., A. Felsóvályi, S. A. Sivo, and S. C. Keenan. SAS® for Monte Carlo Studies: A Guide for Quantitative Researchers. SAS Institute, Cary, N.C., 2000.
Patel, A., and M. Thompson. Consideration and Characterization of Pavement Construction Variability. In Transportation Research Record 1632, TRB, National Research Council, Washington, D.C., 1998, pp. 40-50.
Awasthi, A., S. S. Chauhan, S. K. Goyal, and J.-M. Proth. Supplier Selection Problem for a Single Manufacturing Unit Under Stochastic Demand. International Journal of Production Economics, Vol. 117, No. 1, 2009, pp. 229-233.
Groves, R. M., F. J. Fowler, M. Couper, J. M. Lepkowski, E. Singer, and R. Tourangeau. Survey Methodology. John Wiley and Sons, Inc., Hoboken, N.J., 2004.
Armstrong, J. S., and F. Collopy. Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons. International Journal of Forecasting, Vol. 8, No. 1, 1992, pp. 69-80.
Makridakis, S. Accuracy Measures: Theoretical and Practical Concerns. International Journal of Forecasting, Vol. 9, No. 4, 1993, pp. 527-529.
Goodwin, P., and R. Lawton. On the Asymmetry of the Symmetric MAPE. International Journal of Forecasting, Vol. 15, No. 4., 1999, pp. 405-408.
Good, P. I. Resampling Methods: A Practical Guide to Data Analysis, 3rd ed. Brikhäuser, Boston, Mass., 2006.
Groves, R. M. Survey Errors and Survey Costs. John Wiley and Sons, Inc., Hoboken, N.J., 1989.
Cools, M., E. Moons, T. Bellemans, D. Janssens, and G. Wets. Surveying Activity-Travel Behavior in Flanders: Assessing the Impact of the Survey Design. In Proceedings of the BIVEC-GIBET Transport Research Day 2009, Part II (C. Macharis and L. Turcksin, eds.). VUBPRESS, Brussels, 2009, pp. 727-741.
Pendyala, R. M., T. Yamamoto, and R. Kitamura. On the Formulation of Time-Space Prisms to Model Constraints on Personal Activity- Travel Engagement. Transportation, Vol. 29, No. 1, 2002, pp. 73-94.
Nakamya, J., E. A. Moons, S. Koelet, and G. Wets. Impact of Data Integration on Some Important Travel Behavior Indicators. In Transportation Research Record: Journal of the Transportation Research Board, No. 1993, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 89-94.
Abrahamsson, T. Estimation of Origin-Destination Matrices Using Traffic Counts: A Literature Survey. IIASA Interim Report IR-98-021. International Institute for Applied Systems Analysis, Laxenburg, Austria, May 1998.