Intrapersonal changes in social activity-travel patterns: linking time use and social network dynamics
Notice bibliographique
Résumé
Motivation and objectives Recent interest on intrapersonal activity-travel dynamics has emphasized the need of understanding the link between long and short term behavioural processes in an integrated way, incorporating life events as well as daily decision making. Most of this research has focused on aggregated analysis of travel times and specific decisions, such as car ownership, with increasing focus on specific trip purposes. In this context, social activity-travel constitutes a particularly important subset, not only because it has an increasing high share on total trips, but also since it serves as a key instrument for people’s provision of social capital and quality of life. Yet, although recent studies have advanced on understanding and modeling the role of transport on social interactions, there is still the need of further evidence on the dynamic processes embedded in the context of social activities (Sharmeen et al., 2013; 2014a; 2014b). The objective of this paper is to study the interpersonal changes in social activity-travel behaviour by linking people’s daily activity-travel patterns with their social interaction context. We study these changes through data that explicitly collects information about people’s time use patterns and personal networks in two points in time, with five years of difference. Data and Methods For this, we study a sample of 105 individuals who were surveyed in 2008 and 2012 in the city of Concepcion, Chile. The survey gathered information about key socio-demographics and household contextual attributes, as well as mobility tools and use, activity-travel patterns, and personal networks. Mobility tools and use included car and information and communication technologies availability and use. Personal network information was gathered using a name generator technique, which explicitly elicits social contacts with specific criteria. Information about these people includes relationships with the respondent and between them, frequency of interaction (face-to-face and virtual), place of residence, and place of most frequent interaction. Activity-travel patterns consisted of people’s time use in one weekday and one weekend day, not only including the activity or trip temporal and spatial characteristics, but information about with whom it was performed and an explicit link with the respondent’s personal network. Structural equation models are used to account for the dynamics on activity-travel patterns and social contacts between the two years on three ways. First, the model captures changes between time uses on both years to capture dynamics on space, time, socializing, and trends on activity purposes and trips. Second, the model captures changes on the respondent’s personal networks in terms of social contacts that leave, join or maintain on the network between the two years, as well as general trends in terms of the network’s structure and composition. Third, the model captures the inter-relationships between time use and personal networks, on each year as well as the changes in the five year period. The study adds to the state of the art on travel behaviour research on two aspects. First, we add more methodological experience on the opportunities and scope of using explicit personal network data to study intrapersonal activity-travel dynamics. Second, we add empirical evidence to the role of key aspects that define the link between short and long term behavioural processes, such as key life events, lifecycle, and the role of socio-demographics. References Sharmeen, F., T. A. Arentze, and H. J. P. Timmermans. A multilevel path analysis of 30 social network dynamics and the mutual interdependencies between face-to-face and ICT modes of social interaction in the context of life-cycle events.In Travel Behaviour Research: Current Foundations, Future Prospects , Lulu Publishers, Toronto, 2013, pp. 411-432. Sharmeen, F. A., T.Timmermans, H. An analysis of the dynamics of activity and travel 27 needs in response to social network evolution and life-cycle events: A structural equation model. Transportation Research Part A: Policy and Practice, Vol. 59, 2014(a), pp. 159-171. Sharmeen, F., T. Arentze, and H. Timmermans. An analysis of the dynamics of 35 activity and travel needs in response to social network evolution and life-cycle events: A 36 structural equation model. Transportation Research Part A: Policy and Practice, Vol. 59, 2014(b), pp. 159-171.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».