Association Rules in Identification of Spatial-Temporal Patterns in Multiday Activity Diary Data

The question of identifying temporal patterns in activity diary data has received only scant attention in the transportation literature, but interest is rapidly increasing. Most of the existing research uses well-known econometric methods to quantify change. Use of association rules to explore activity diary panel data, involving two waves, for possible stationary and time-varying patterns in activity-travel patterns is reported. The data for this analysis stem from the municipality of Voorhout in the Netherlands. Data were collected in 1997 and 1998 before and after opening of a new railway station. Results of the analysis indicate that specific household and individual attributes have a larger effect on daily activity patterns than others and that the effect of these attributes has significantly changed. Because changes in other sociodemographic attributes are almost nonexistent and activity patterns for communities are known to be stable, this study claims that the observed shifts in dependencies come from this new station.

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Exploratory data analysis of activity diary data: a space–time GIS approach

Journal of Transport Geography ◽

10.1016/j.jtrangeo.2010.11.002 ◽

2011 ◽

Vol 19 (3) ◽

pp. 394-404 ◽

Cited By ~ 97

Author(s):

Jie Chen ◽

Shih-Lung Shaw ◽

Hongbo Yu ◽

Feng Lu ◽

Yanwei Chai ◽

...

Keyword(s):

Data Analysis ◽

Exploratory Data Analysis ◽

Space Time ◽

Diary Data ◽

Exploratory Data ◽

Activity Diary

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Coevolutionary Approach to Extracting and Predicting Linked Sets of Complex Decision Rules from Activity Diary Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1752-17 ◽

2001 ◽

Vol 1752 (1) ◽

pp. 126-132 ◽

Cited By ~ 1

Author(s):

Theo Arentze ◽

Harry Timmermans

Keyword(s):

Decision Rules ◽

Diary Data ◽

Complex Decision ◽

Activity Diary

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Multi-level temporal autoregressive modelling of daily activity satisfaction using GPS-integrated activity diary data

International Journal of Geographical Information Science ◽

10.1080/13658816.2018.1504219 ◽

2018 ◽

Vol 32 (11) ◽

pp. 2189-2208 ◽

Cited By ~ 3

Author(s):

Guanpeng Dong ◽

Jing Ma ◽

Mei-Po Kwan ◽

Yiming Wang ◽

Yanwei Chai

Keyword(s):

Daily Activity ◽

Autoregressive Modelling ◽

Diary Data ◽

Multi Level ◽

Activity Diary ◽

Activity Satisfaction

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Reinduction of Albatross Decision Rules with Pooled Activity-Travel Diary Data and an Extended Set of Land Use and Cost-Related Condition States

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1831-26 ◽

2003 ◽

Vol 1831 (1) ◽

pp. 230-239 ◽

Cited By ~ 17

Author(s):

Theo Arentze ◽

Frank Hofman ◽

Harry Timmermans

Keyword(s):

Process Model ◽

Goodness Of Fit ◽

Decision Rules ◽

Data Sets ◽

Related Condition ◽

Diary Data ◽

Travel Diary ◽

Complete Set ◽

Individual Decisions ◽

Activity Diary

Operationalization of the Albatross model—a rule-based model of activity-travel scheduling—is described for applications on a national scale. For this purpose, the original model was extended to include the generation of schedule skeletons and travel cost variables. Furthermore, to account for the increase of scale of the study area, the location decision component of the model was completely restructured. The complete set of 27 decision trees involved in the decision process model were newly induced from several pooled existing activity diary data sets in the Netherlands. The results indicate that the goodness of fit of the model is satisfactory at the level of individual decisions as well as aggregate distributions.

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