Incorporating planned activities and events in a dynamic multi-day activity agenda generator
Bibliographic Data
| ID | 19338137 |
|---|---|
| Authors | Linda Nijland (Utrecht University, corresponding author), Theo Arentze (0000-0003-2551-8650, Eindhoven University of Technology), Harry Timmermans (0000-0002-8737-4632, Eindhoven University of Technology) |
| Year | 2012 |
| Volume | 39 |
| Issue | 4 |
| Pages | 791-806 |
| Publication date | 2012-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Transportation (JOURNAL) |
| Journal identifiers | ISSN: 0049-4488 • E-ISSN: 1572-9435 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11116-012-9402-0 |
| OpenAlex | W2146304559 |
| Language | EN |
| Citations received | 5 |
| References cited | 16 |
Daily agenda formation is influenced by formal commitments, satisfaction of needs surpassing some threshold and the desire to conduct particular activities in anticipation of socially and religiously driven events such as birthdays, Christmas, etc. As part of a research program to develop a dynamic activity-based model of transport demand, this paper proposes a model to represent dynamic agenda formation, including these different underlying processes. Bayesian estimation of the model is based on data collected through a Web-based survey for a sample of approximately 300 respondents. The survey uses an extension of a 1-day activity diary where respondents are asked to recall activities in retrospect and to identify planned activities in prospect. Estimation results suggest that planned activities influence agenda formation in general, but that their significance and size depends on activity type, socio-demographics and dwelling characteristics
Anticipation (artificial intelligence) · Cognitive psychology · Demographics · Economics · Estimation · Management · Operations research · Recall · Sample (material) · Sociology · Artificial Intelligence · Computer Science · Demography · Engineering · Psychology · Transportation and Mobility Innovations · Transportation Planning and Optimization · Urban Transport and Accessibility
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Extensions of the Activity Chain Optimization Method
Accommodating (non-)monotonically changing hazards in finite mixture models for the inter-episode duration of discretionary activities
Personal activity queue inference
| Citations per year | 0.63 |
|---|---|
| Citation span | 2018 - 2026 (9) |
| Highly cited | No |