Citation

Lin HZ, Lo HP, Chen XJ. Transp. Res. A Policy Pract. 2009; 43(6): 626-638.

Copyright

(Copyright © 2009, Elsevier Publishing)

DOI

10.1016/j.tra.2009.04.002

PMID

unavailable

Abstract

Trip-based approach and activity-based approach are two extremes in the use of activity related information when developing travel demand models. Creating lifestyle clusters for a population is a compromise between the two. On the one hand, it has taken into account travel-activity patterns in the development of the clusters. On the other hand, the clusters represent homogenous groups of individuals and simple activity-based travel demand models can be developed for each cluster. However, the development of such clusters requires knowledge of activity-travel patterns of individuals, which can only be obtained from a large-scale survey. It is still an open question how to create travel/activity-related lifestyle clusters using readily available socio-demographic data (such as census data) alone. This paper attempts to answer this question by proposing a procedure of lifestyle classification that moves from specific surveys to a general population. This paper first studies issues related to the development of homogeneous clusters using socio-economic, demographic and activity-travel data. The second part of the paper addresses the issue of data insufficiency and points out that in order to use the clusters developed for travel demand estimation, it is important to know how to allocate individuals in the population to the developed clusters. As a first attempt, this paper proposes to use a recently developed technique called, Support Vector Machine (SVM), to develop classification functions that based on readily available information only. The methodologies proposed are applied to a sub-urban area in Hong Kong. Six lifestyle clusters are first produced using factor analysis and cluster analysis. SVM is then used to develop classification functions that are based on fewer variables. Results show that the two sets of lifestyle clusters are similar and that the SVM outperforms other traditional classification methods. Keywords: Lifestyle; Activity-travel patterns; Support Vector Machine; Clustering; Classification