Verhaltenshomogene Gruppen in Längsschnitterhebungen

In this dissertation the hypotheses will be tested, if the population can be segmented into groups which are clearly distinct from each others in terms of travel behaviour. This question is important to travel behaviour analysis since long because a proper segmentation which explains a big amount of the behavioural variability has two advantages. First, it can help to create a supply which is suitable for different target groups. Second, travel forecast based on this groups can lead to solid results within less effort. Compared to former research progress could be achieved because of two reasons 1) New methods like the sequence alignment method to measure similarity are tested and applied. Those measures are able to capture more contextual information of performed activities than former measures. 2) The analysis is based on a database, which is to a large extend unique in terms of the survey duration. The dissertation is guided by the following questions: • How can travel behaviour be described in a way as comprehensive as possible? How should one quantify and measure similarity in travel behaviour? How solid are segmentations dependent on the chosen similarity measure? • How important is the intrapersonal variability? Which period of time has to be looked at to cover as much of the intrapersonal variability that is necessary to avoid misclassifications? • How are persons to be grouped so that differences in travel behaviour within the groups are smaller than the differences between the groups? • How do the members of a group differ from each other in terms of their sociodemographic characteristics? Verhaltenshomogene Gruppen in Längsschnitterhebungen ___________________________________ September 04 II To evaluate how similarity can be measured some established measures as well as the MultiDimensional Sequence Alignment Method are calculated and compared. The results of the calculations differ from each other depending on the different measures. The Sequence Alignment Method is the most comprehensive method which is the only one to consider the ordering of activities. Due to the large amount of intrapersonal variability it is not sufficient just to use a few days per person as a basis for the segmentation. Even if one looks at a few typical days instead of random days only a part of the behavioural variability can be captured. Since the application of the Sequence Alignment Method requests very much computing time, in the context of this work only three different days per person can be considered for the calculation. Thus a second approach is chosen which is based on the Euclidean distance as a similarity measure. This clustering process contains the amount of intrapersonal variability as a variable. Besides the second approach is an evaluation of the first measurement. It could be shown, that the Sequence Alignment is an useful measure that can be implemented even for big datasets. Nevertheless the results of both approaches show big differences. While clusters created on behavioural variables and the Euclidean Distance are different from each others in terms of their sociodemographic variables, clusters based on three typical days per person (calculated with the Sequence Alignment) cannot be characterised in a useful way. Although the theoretical background of the implementation is appealing, the resulting groups cannot empfehlenswert. Still it seems promising to integrate the order of activites into similarity measurement. The big amount of heterogeneity within groups which don ́t consider this information indicates that another way to incorporate the order helps to build better groups.