A time-space constrained approach for modeling travel and activity patterns

In this thesis we develop a tour-based approach for modeling activity and travel pattern considering time-space constraints. A hierarchical structure of choice-making builds theoretical background for the model and is based on a set of axiomatic rules. Our central argument is that the time-space constraints can be used for reducing the number of choices and, respectively, control the combinatorics associated with the probabilistic approach. The empirical analysis of our use case, a tour of type ‘Home-Work-SecondaryActivity-Home’, is based on Santiago’s travel survey. In addition, we apply GIS to estimate the so-called search spaces (potential areas where secondary activities are realized) and justify their sizes with the empirical findings. From the data analysis we identify thresholds for the tour-based maximum daily travel times considering a set of mode combinations. We define regimes of starting times and duration of activities depending on socio-economic user groups. The estimation of search spaces is realized considering the time spent at work as well as the distance between the home and work locations. Both criteria were found to be statistically significant. The comparison of modeled results with survey observations allowed concluding that the search spaces are realistic since they capture most of the observed trip destinations. For the estimation of spatial path flows of activities and trips (using SPSS programming language), we define a final choice set of no more than seven alternatives per primary location considering zone-based accessibility and land-use attractiveness. The obtained results support the argument that time-space constraints (daily travel time, search spaces) allow an effective control of combinatorial complexity. Basing on the experience obtained in process of modeling the exemplary tour, the approach can be applied to further tour types offering the possibility to estimate the entire transport demand of Santiago city.

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