Evaluating the reliability of reported distance data in urban travel behaviour analysis

The objective of the present paper is to analyze the accuracy of reported distances in travel behaviour research, and to distil from this analysis some useful recommendations for data collection and handling in activity-based modelling. This issue is important because we know from the literature on distance cognition that conjecture, perception and rounding in distance reporting is a rule, rather than an exception. The outcome is a biased transport modelling result. The paper introduces some theoretical reflections on distance measurements and cognition. Next, using household travel survey data collected in 2000 for the city of Ghent, Belgium, the problem of reported distance reliability is examined. A comparison of travel distances using detour factors revealed that self-reported distances provide reasonable estimates of shortest distance path distances. With respect to the effect outliers and rounding have on the reliability of reported distances it is found that mean detour factors are very much dominated by outliers over short distances, but that rounding has little impact. The accuracy of self-reported distances is also influenced by the socio-demographic profile of the respondents, the characteristics of the trip, and the type of transport mode used.

[1]  David A. Hensher,et al.  Travel behaviour research : the leading edge , 2001 .

[2]  H. Mahmassani.  In Perpetual Motion: Travel Behaviour Research Opportunities and Application Challenges , 2002 .

[3]  P. Bovy,et al.  ROUTE CHOICE: WAYFINDING IN TRANSPORT NETWORKS , 1990 .

[4]  Reginald G. Golledge.  DEFINING THE CRITERIA USED IN PATH SELECTION. , 1995 .

[5]  Christian Wiedemann,et al.  EXTERNAL EVALUATION OF ROAD NETWORKS , 2003 .

[6]  Robert Allen Lowrey,et al.  Distance Concepts of Urban Residents , 1970 .

[7]  E. Sadalla,et al.  The Perception of Traversed Distance , 1980 .

[8]  M. Bierlaire,et al.  Discrete Choice Methods and their Applications to Short Term Travel Decisions , 1999 .

[9]  T. Gärling,et al.  Distance cognition by taxi drivers and the general public , 1989 .

[10]  A. Warnes.  ESTIMATES OF JOURNEY-TO-WORK DISTANCES FROM CENSUS STATISTICS , 1972 .

[11]  K. Axhausen,et al.  Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems , 1992 .

[12]  Randolph W. Hall,et al.  Handbook of transportation science , 1999 .

[13]  Kay W. Axhausen,et al.  Precision of geocoded locations and network distance estimates , 2004 .

[14]  Reginald G. Golledge,et al.  Analytical Behavioural Geography , 1987 .

[15]  S. Bekhor,et al.  Route Choice Models Used in the Stochastic User Equilibrium Problem: A Review , 2004 .

[16]  D. Walmsley,et al.  Urban Living: The Individual in the City , 1988 .

[17]  Michael Scott Ramming,et al.  NETWORK KNOWLEDGE AND ROUTE CHOICE , 2002 .

[18]  Piet Rietveld,et al.  Rounding of Arrival and Departure Times in Travel Surveys: An Interpretation in Terms of Scheduled Activities , 2001 .

[19]  P. Raghubir,et al.  As the Crow Flies: Bias in Consumers' Map-Based Distance Judgments , 1996 .

[20]  J. Denstadli,et al.  Testing the accuracy of collected geoinformation in the Norwegian Personal Travel Survey––experiences from a pilot study , 2003 .

[21]  A. Verhetsel.  The world in our heads: An experimental tuition programme focusing on students' ability to represent and structure spatially , 1994 .

[22]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[23]  Martin Cadwallader,et al.  Problems in Cognitive Distance , 1979 .

[24]  Stig Nordbeck.  Computing distances in road nets , 1964 .

[25]  B. Zwart,et al.  On the relationship between travel time and travel distance of commuters , 1999 .

[26]  Frank Witlox,et al.  Dynamics in city regions: the intra-urban travel patterns in Antwerp and Ghent, Belgium , 2002 .

[27]  H. HIGHTON,et al.  Perpetual Motion , 1871, Nature.