Analysing Sources of Variability in Average Travel Time Use: an Investigation Based on Extended Structural Equation Models and the UK National Travel Survey Data
K Jahanshahi, Y Jin, Al Hagen-Zanker, University of Cambridge, UK; I Williams, WSP, UK
The investigation uses SEM innovatively by considering the interactions among travel purposes, socio-economic and demographic factors, accessibility and land use characteristics within one combined framework.
The existing findings on the variability of travel time use per person appear to be highly dependent on the scale of analysis: on the one hand, studies at the individual level show significant variations in the total travel time used per person that arise from a myriad of factors; On the other hand, aggregate studies at the city/country level suggest that the average travel time use per person across the population is very stable (between 1-1.5 hour per day) with slight or no changes over time.
This study aims to reconcile these two sets of views by investigating factors that influence the use of travel time, particularly (1) whether there are significant interactions among various travel purposes (e.g. do those commuters who have long journeys to work spend less time on other trip purposes), (2) whether there are strong offsetting effects among different groups of population (e.g. as some spend more time travelling, others cut their travel time use over time), and (3) how relevant the self selection and spatial sorting effects are across socio-economic groups and geographic locations. We adopt an innovative approach to Structural Equation Modelling and use an extensive time-series dataset - the Great Britain National Travel Survey (GB-NTS).
Due to its flexibility and capability for analysing interactions between different sets of parameters including activity durations and travel time use, structural equation models have been increasingly employed for activity based analysis, and for understanding the influence of land use patterns. This paper goes further by considering the interactions among travel purposes, socio-economic and demographic factors, accessibility and land use characteristics within one combined framework.
The results offer new insights into developing significantly more robust travel demand forecasts in trip-based and activity-based models. The extent to which travel time savings on one trip leak into longer durations for other trips is also of direct relevance to the interpretation and assessment of travel time savings.
The GB-NTS is an annual series of household surveys across Great Britain designed to provide regular, up-to-date data on personal travel and to monitor changes in travel behaviour over time. GB-NTS covers individuals' travel data for seven days of the survey week and collects a wide range of attributes regarding the individuals and their households, areas of residence and trips. It is one of the most comprehensive surveys of this type in the world. The data from the most recent years available (2002-2008) are used in this investigation, which build upon our in-depth understanding of the strengths and weaknesses of the dataset.
We analyse the influence of the socio-economic, demographic, accessibility and land use factors on the total time spent travelling by individuals and investigate whether this total travel time is stable in aggregate over time for a given type of persons, taking into account the interaction between travel purposes. The analyses are carried out in the following steps:
First, to examine whether the average travel time is constant over time when the effect of socioeconomic, demographic, accessibility and land use factors are controlled across population groups; Secondly, to evaluate the importance of interaction between trip purposes when examining travel time spent on each individual travel purpose; Thirdly, to investigate the direct effect of socioeconomic, demographic, accessibility and land use factors on travel time, as well as their indirect effect when the interactions between those factors are controlled, as explained below.
The trip purposes are analysed by making innovative use of structural equation models (SEM - a set of simultaneous regression equations coupled with factor analysis). This approach helps to represent simultaneously the direct effect of the above mentioned factors on the time people spend on individual travel purposes, in addition to the indirect effect of those through their influences on the travel time spent on other trip purposes. As an example, by using SEM to model the travel time on shopping trips, we are investigating:
First, the direct influence on shopping travel time of socio-economic, demographic, accessibility and land use factors; Secondly, the direct role these factors also play in influencing commuting travel time; Thirdly, the indirect influence of these factors on shopping travel time through interactions from commuting travel time.
Defining socioeconomic and accessibility characteristics as latent variables which are described by a set of observed factors, we are also investigating the relative importance of accessibility indicators such as area type and population density when the interaction of those with demographic factors (e.g. age, gender, car ownership and household structure) is controlled.
Association for European Transport