Are daily life and daily travel behavior in Germany more flexible today than it was 10 to 15 years ago? Against the background of an increasing use of information and communication technologies (ICT) and the question of that impact on daily life, this paper analyzes changes in the variability of travel behavior. The paper examines the data from individual travel diaries of a whole week covering a longitudinal perspective over 15 years. These two dimensions of information allow for more detailed evaluation of variability profiles. The analyses were based on traditional key values of daily travel as well as new customized measures of individual variability in travel behavior in the course of a whole week. The paper investigated the temporal development by sociodemographic group. The results show that travel behavior patterns of young men and woman in Germany are becoming more similar. The young age group drives several trends, including multimodality. However, at the same time, young men are shifting their leisure habits toward more home-located activities; this effect is possibly partly induced by the influence of ICT.
Multiday and multiperiod panel surveys are state-of-the-art methods to assess changes in individual travel behavior. Though important for transport planners, these surveys are rather time-consuming for participants and therefore might lead to erroneous and biased mobility data. Variability in the data quality significantly affects statistical analyses of mobility figures as well as common microscopic travel demand models that use the mobility data as the basis for generating activity plans. Supplementary to the well-known approach of weighting biases in key figures of mobility, this paper focuses on methods for detecting data quality differences between individual travel diaries. These quality measures address aspects of motivation loss at different stages of the survey. A classification approach based on these new quality measures helps to detect erroneous data and possible dropouts. The results might help reduce dropouts in general by addressing the potential dropouts individually in advance and boosting their motivation. Quality measures are tested with recent data from the German Mobility Panel. For participants older than 60 years of age, the quality measures show good classification results in regard to accuracy, but for participants younger than 35 years of age the quality measures are not effectual in identifying dropouts. Such an individual approach combined with the partial inspection and correction of travel diaries may be useful for microscopic travel demand modeling based on external activity chains.
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