Simulating Household Travel Survey Data

Better (more comprehensive, higher quality, and more detailed) data on travel demand related to the sociodemographic and spatial characteristics of individuals and household are critical for modern transport planning and policy development. Provision of such data relies largely on household travel surveys (HTSs), in which a small sample of the population (usually about 2,000 to 5,000 households) records their travel patterns over some given time period. HTSs are notoriously expensive planning activities with current cost typically ranging from $150 to $200 (US) per completed household, depending on the method used. Additionally, such surveys are plagued by nonparticipation and non- or misreporting from those who do participate—for instance, response rates of thirty to forty percent are considered about as good as can be achieved for other than face-to-face surveys. Given that most survey specialists maintain that any survey with less than a ninety percent response rate is potentially flawed, the implications for the quality and representativeness of the resulting data are startling. The purpose of this chapter is to provide background information on HTS, with a view to triggering information debate. The chapter covers the rationale, current approaches and critical issues associated with simulating HTS data before providing some recommendations on potential future directions.

[1]  Marcus Blake,et al.  An evaluation of synthetic household populations for census collection districts created using optimisation techniques , 2002 .

[2]  Peter R. Stopher SIMULATING HOUSEHOLD TRAVEL SURVEY DATA IN AUSTRALIA: ADELAIDE CASE STUDY. , 2002 .

[3]  Peter R. Stopher,et al.  Transferability of Transportation Planning Data , 2001 .

[4]  Frank S. Koppelman,et al.  TRANSFER MODEL UPDATING WITH DISAGGREGATE DATA , 1985 .

[5]  D. Rubin,et al.  Multiple Imputation for Nonresponse in Surveys , 1989 .

[6]  Ryuichi Kitamura,et al.  Generation of Synthetic Daily Activity-Travel Patterns , 1997 .

[7]  Kay W. Axhausen,et al.  Simulating Activity Chains: German Approach , 1989 .

[8]  Stephen Greaves,et al.  Simulating Household Travel Survey Data: Application to Two Urban Areas , 2003 .

[9]  Peter R. Stopher,et al.  Monte Carlo Simulation of Household Travel Survey Data for Sydney, Australia: Bayesian Updating Using Different Local Sample Sizes , 2004 .

[10]  Moshe Ben-Akiva,et al.  DATA COMBINATION AND UPDATING METHODS FOR TRAVEL SURVEYS , 1988 .

[11]  M. McNally AN ACTIVITY-BASED MICROSIMULATION MODEL FOR TRAVEL DEMAND FORECASTING , 1996 .

[12]  Peter R. Stopher,et al.  Monte Carlo simulation of household travel survey data with Bayesian updating , 2003 .

[14]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[15]  Peter R. Stopher,et al.  Creating a Synthetic Household Travel and Activity Survey: Rationale and Feasibility Analysis , 2000 .

[16]  Michael G. McNally,et al.  A Microsimulation of Daily Activity Patterns , 2000 .

[17]  M. D. McKay,et al.  Creating synthetic baseline populations , 1996 .