Measuring and predicting adaptation in multidimensional activity-travel patterns

2+131hlm.;24c

Applying sequential alignment methods to large activity-travel data sets: exploration of a heuristic approach

Sequence alignment methods have recently found increasing application in transportation research for segmentation and classification analysis. Because these methods account for the sequential nature of activity-travel diary data, they are valuable for classifying activity-travel diary data. However, computation time requirements are overwhelming, implying that large-scale applications of these methods have not yet been possible. The performance and applicability of a heuristic approach was explored by using smart card data from Seoul, South Korea. Results were promising and suggest that the proposed heuristic approach could be used to perform segmentation analysis of large activity-travel data sets. Avenues for further improvement of the suggested method are discussed

A position-sensitive sequence-alignment method illustrated for space-time activity-diary data

Sequence-alignment methods which have been recently introduced in time-use research analyse the cross-sectional as well as the sequential information embedded in space - time activity-diary data. As these methods have been developed to trace the evolutionary history of random mutations of the elements in biological sequences, they are not appropriate to measure the distances by which activities change their sequential order in activity diaries. In this paper we report on the development of a position-sensitive sequence-alignment method for activity analysis. First, the problem is specified, and the key concepts to analyse the problem are addressed. Then, the principles of the new method are introduced. The method is illustrated with data on activity patterns collected in the Netherlands. The paper concludes with a critical discussion of the basic assumptions of the method and avenues of future research are identified

Sequence Alignments Methods

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Sequence Alignments Methods

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Pattern recognition in complex activity travel patterns : comparison of Euclidean distance, signal-processing theoretical, and multidimensional sequence alignment methods

The application of a multidimensional sequence alignment method for classifying activity travel patterns is reported. The method was developed as an alternative to the existing classification methods suggested in the transportation literature. The relevance of the multidimensional sequence alignment method is derived from the fact that structural information (both interdependency and sequential relationships) embedded in activity travel patterns is taken into account - a property not shared with existing classification methods. The performance of the multidimensional sequence alignment method is compared with several other methods (Euclidean distance and signal processing) that have been used in activity analysis in the past

Characterisation and comparison of gender-specific utility functions of shopping duration episodes

This paper reports the results of an analysis of gender differences in the parameters of utility functions derived from activity duration data. The estimated utility functions allow a comparison of the marginal utility, inflection point and the (a)symmetry of the utility of activity duration. Some significant differences between men and women are found. The results of the analyses indicate that the utility that men and women derive from basic in-home leisure activities is more or less the same. Gender differences were found for out-of-home activities, including grocery and non-grocery shopping activities

Activity-travel scheduling and rescheduling decision processes : empirical estimation of Aurora model

Predicting short-term activity-travel decisions is high on the research agenda because it constitutes a critical step in assessing the impact of policy decisions on daily traffic flows. The Aurora model was developed for predicting short-term activity scheduling and rescheduling decisions. Previous studies, using numerical simulations, have shown the potential of this model. The results of a study that estimated the model using empirical activity-travel diary data collected in the Amsterdam-Utrecht corridor, in the Netherlands, are reported