The Impact of Simplification in a Sequential Rule-Based Model of Activity-Scheduling Behavior
Bibliographic Data
| ID | 4140379 |
|---|---|
| Authors | Elke A L M G Moons, Elke Moons (Transnational University Limburg, corresponding author), Geert P M Wets (Transnational University Limburg, corresponding author), Marc Aerts (0000-0002-1803-9072, Center for Statistics, Limburgs Universitair Centrum, 3590 Diepenbeek, Belgium, corresponding author), Theo A Arentze, Theo Arentze (0000-0003-2551-8650, Eindhoven University of Technology, corresponding author), Harry J P Timmermans, Harry Timmermans (0000-0002-8737-4632, Eindhoven University of Technology, corresponding author) |
| Year | 2005 |
| Volume | 37 |
| Issue | 3 |
| Pages | 551-568 |
| Publication date | 2005-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1068/a36167 |
| OpenAlex | W2093176637 |
| Language | EN |
| Citations received | 1 |
| References cited | 18 |
The aim of this paper is to gain a better understanding of the impact of simplification on a sequential model of activity-scheduling behavior which uses feature-selection methods. To that effect, the predictive performance of the Albatross model, which incorporates nine different facets of activity-travel behavior, based on the original full decision trees, is compared with the performance of the model based on trimmed decision trees. The results indicate that significantly smaller decision trees can be used for modeling the different choice facets of the sequential model system without losing much in predictive power. The performance of the models is compared at three levels: the choice-facet level, the activity-pattern level (comparing the observed and generated sequences of activities), and the trip-matrix level, comparing the correlation coefficients that determine the strength of the associations between the observed and the predicted origin-destination matrices. The results indicate that the model based on the trimmed decision trees predicts activity-diary schedules with a minimum loss of accuracy at the decision level. Moreover, the results indicate a slightly better performance at the activity-pattern and the trip-matrix level
Decision model · Decision tree · Feature selection · Machine learning · Mathematical optimization · Predictive power · Computer Science · Mathematics · Transportation and Mobility Innovations · Transportation Planning and Optimization · Urban Transport and Accessibility · Artificial Intelligence
| Citations per year | 0.06 |
|---|---|
| Citation span | 2009 - 2009 (1) |
| Highly cited | No |