Andreas Justen^a,
Francisco J. Martínez^b,
Cristián E. Cortés^b
a German Aerospace Center (DLR), Institute of Transport Research, Rutherfordstr. 2, 12489 Berlin, Germany
^b University of Chile, Faculty of Physical and Mathematical Sciences, Division of Transport Engineering, Blanco Encalada 2002, Santiago, Chile


Highlights

•We derive space–time constraints from a Chilean household and travel survey.
•Spatial detours dependent on the home-work distance are estimated to describe the potential path area using a GIS.
•According to the mode-dependent daily travel time the set of feasible locations is further reduced.
•The approach covers about 50% of the locations decisions reported in the survey.


Keywords
Space–time constraints; Potential path area; Location choice; Discretionary activity; Detour factor



Abstract
The development of methods of studying individuals’ selection of discretionary activity locations remains a challenge for empirical analysts and transport modelers. Time geography and, in particular, the concept of space–time constraints provides a useful framework for these selection processes. In this work we empirically determine space–time constraints from the Chilean household and travel survey. Based on a specific activity pattern example, where trips are made from home to work to a discretionary activity and back home, we estimate detour factors. Detour factors describe the spatial deviations that are made from the home-work axis to conduct the discretionary activity. Using GIS we estimate potential path areas (PPAs), where discretionary activities may be located. Within the PPAs, applying a time constraint that is the maximum daily travel time refines the selection of discretionary activity locations. The thresholds of the daily travel time vary according to the PPA-size and mode combinations. We were able to reproduce between 38% and 72% of the discretionary location choices observed in the survey (according to the rigor of the constraints applied).



Article Outline
1. Introduction and background
2. Data and methods
2.1. Drawing the PPA using detour factors

3. Empirical results
3.1. Descriptive statistics of detour factors
3.2. PPA and FOS: applying the space–time constraints
3.3. PPA and FOS: level of matching between survey and model

4. Discussion and outlook
Acknowledgements
References


Figures
    

Fig. 1.

The ellipse concept.


Fig. 2.

Power functions based on detour factors (PCTL = Percentile).


Fig. 3.

Effect of space–time constraints on the selection of discretionary activities locations (Left side: HW-distance is 15.3 km resulting in a detour factor of 1.06 (applying the power function derived from percentile 60, see Fig. 2); all dots together represent the TAZs inside the PPA (limited by the ellipse boundary); grey dots represent the FOS after applying the time constraint for the mode combination CpPtPt. Right side: same as left except for applying the time constraint for mode combination PtWaPt).


Fig. 4.

Ranking of the feasible opportunity set based on travel times and land-use densities (Larger dot sizes reflect a higher rank. The travel time towards discretionary activities refers in the left case to times by public transport (CpPtPt) and in the right case to walking times (PtWaPt). Land-use is included as density where for each TAZ the sum of built floor space for commercial and service purposes was divided by the TAZ-area. Land-use information was taken from a model run of Santiago’s land-use model MUSSA).


Tables

Table 1. Change of detour factors with increased distance between home and work locations.a

Table 2. Regression between daily travel time and distances by mode combinations.a

Table 3. Selection of discretionary activity locations: comparing survey and model.

Table 4. Effectiveness of space–time constraints on reducing choice alternatives.a