Changing household car ownership level and life cycle events: an action in anticipation or an action on occurrence

This paper analyzes households’ decision to change their car ownership level in response to actions/decisions regarding mobility issues and other household events. Following recent literature on the importance of critical events for mobility decisions, it focuses on the relationship between specific events (e.g. childbirth and buying an extra car), rather than trying to explain the status of car ownership from a set of stationary explanatory variables. In particular, it is hypothesized that changes in household car ownership level take place in response to stressors, resulting from changed household needs or aspirations. The study includes a broad range of events. Apart from changes in work status, employer and residential location, it analyzes demographic events such as household formation and childbirth. Also, it scrutinizes the temporal sequence in which chains of related events are most likely to occur. To this end, data from a retrospective survey that records respondents’ car ownership status, as well as residential and household situation over the past 20 years are used. A panel analysis has been carried out to disentangle typical relationships. The results suggest that strong and simultaneous relationships exist between car ownership changes and household formation and dissolution processes. Childbirth and residential relocation invoke car ownership changes. Changes are also made in anticipation of future events such as employer change and childbirth. Childbirth is associated with increasing the number of cars, whereas the effect of employer change goes the opposite way. Job change increases the probability of car ownership change in the following year.     

Modelling Complex Activity‐Travel Scheduling Decisions: Procedure for the Simultaneous Estimation of Activity Generation and Duration Functions

Abstract

Activity generation is a key factor in individual's choices of trip frequency and trip purpose. This paper describes the results of an experiment conducted to estimate functions of several temporal factors on individuals' propensity to schedule a given activity on a given day. The theory on which the experimental design is based states that the probability of scheduling an activity is a complex and continuous function of how long ago the activity was lastly performed, the duration constraints for the activity and the amount of available time in the activity schedule of the day considered. Aurora, an existing model of activity scheduling, assumes S‐shaped utility functions for the history as well as the duration functions, whereas most time‐use studies assume monotonically decreasing marginal utilities. The stated‐choice experiment involves a range of flexible activities and a large sample of individuals to measure the utility effects of a set of carefully chosen levels for the factors and tests these specific assumptions. The results suggest that the amount of discretionary time on a day has no significant impact on the scheduling decisions provided that enough time is available for the activity. The effects of other factors are as expected and show diminishing marginal utilities. We find mixed evidence for an initial phase of increasing marginal returns as assumed in an S‐shaped function.     

Adaptive choice behaviour of motorists in congested shopping centre parking lots

Currently existing models of parking choice behaviour typically focus on the choice of types of parking spaces. Implicitly these models assume that motorists have a free choice in that spaces are available. The adaptive behaviour which they reveal when faced with congested parking spaces is not explicitly modelled. The aim of this paper is to contribute to the growing literature on parking choice modelling by developing and testing a stated choice model of adaptive behaviour of motorists who are faced with fully occupied parking lots. The findings of the analyses indicate that the model performs satisfactory as indicated by its goodness-of-fit and the fact that all significant parameters were in anticipated directions.     

The identification of factors influencing destination choice: An application of the repertory grid methodology

A common problem of all cognitive-behavioural models of destination choice is that of the identification of factors influencing the behaviour of interest. This paper considers the applicability of Kelly's repertory grid methodology to identify the factors influencing consumer choice of shopping centres. Firstly, some methodological issues in the assessment of the relative importance people attach to certain variables in deciding where to shop are discussed. Secondly, the main findings of an application of the repertory grid methodology are presented. The paper concludes by discussing some implications of the measurement of the determinants of choice behaviour and the construction of mathematical models of destination choice.     

Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics

This paper develops a model, based on Bayesian beliefs networks, for representing mental maps and cognitive learning into micro-simulation models of activity-travel behavior. Mental maps can be used to address the problem that choice sets in models of travel demand are often ad hoc specified. The theory underlying the model is discussed, a specification is derived and numerical simulation is used to illustrate the properties of the model.     

Incorporating time and income constraints in dynamic agent-based models of activity generation and time use: Approach and illustration

Existing theories and models in economics and transportation treat households’ decisions regarding allocation of time and income to activities as a resource-allocation optimization problem. This stands in contrast with the dynamic nature of day-by-day activity-travel choices. Therefore, in the present paper we propose a different approach to model activity generation and allocation decisions of individuals and households that acknowledges the dynamic nature of the behavior. A dynamic representation of time and money allocation decisions is necessary to properly understand the impact of new technologies on day to day variations in travel and activity patterns and on performance of transportation systems. We propose an agent-based model where agents, rather than acting on the basis of a resource allocation solution for a given time period, make resource allocation decisions on a day-by-day basis taking into account day-varying conditions and at the same time respecting available budgets over a longer time horizon. Agents that share a household interact and allocate household tasks and budgets among each other. We introduce the agent-based model and formally discuss the properties of the model. The approach is illustrated on the basis of simulation of behavior in time and space.     

Incorporating planned activities and events in a dynamic multi-day activity agenda generator

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.     

Involvement in clubs or voluntary associations, social networks and activity generation: a path analysis

Leisure activities have received increasing attention from travel behavior researchers over the past decade. However, these activities are often treated as a single category, neglecting their differences. Whereas most leisure activities are flexible, club activities are usually scheduled longer in advance and are more fixed in time, location and company. Hence, trip-generating properties of club activities are likely to differ from those of other leisure activities. As very little is known about involvement in clubs or voluntary associations in relation to trip generation, voluntary association activities deserve further research in relation to travel. Therefore, in this paper a path analysis is conducted, analyzing the relationships between participation in clubs or voluntary associations, trip frequencies, and social network characteristics. The analyses are based on data collected in 2011 in Eindhoven in the Netherlands in a survey among 516 respondents. The results show interesting relationships between the social context and involvement in clubs. They indicate that people become club members through their social networks, and frequent club activities increase social network size. Family oriented people were found to go less often to clubs. Club membership and the frequency of going to club activities were also found to be affected by socio-demographics, such as gender, age, education, work, presence of young children in the household and owning a season ticket for public transport.     

Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment

This paper develops a framework for modeling dynamic choice based on a theory of reinforcement learning and adaptation. According to this theory, individuals develop and continuously adapt choice rules while interacting with their environment. The proposed model framework specifies required components of learning systems including a reward function, incremental action value functions, and action selection methods. Furthermore, the system incorporates an incremental induction method that identifies relevant states based on reward distributions received in the past. The system assumes multi-stage decision making in potentially very large condition spaces and can deal with stochastic, non-stationary, and discontinuous reward functions. A hypothetical case is considered that combines route, destination, and mode choice for an activity under time-varying conditions of the activity schedule and road congestion probabilities. As it turns out, the system is quite robust for parameter settings and has good face validity. We therefore argue that it provides a useful and comprehensive framework for modeling learning and adaptation in the area of activity-travel choice.     

Changing the timing of activities in resolving Scheduling Conflicts

Following the growing interest in the characterisation and modelling of activity scheduling and re-scheduling behaviour, this paper reports the results of a study on the resolution of activity scheduling conflicts. Using empirical data collected through an Internet survey, the modification of the timing of pre-planned activities to accommodate a new activity in the schedule was analysed. Schedule adjustment was studied using a parametric hazard model. The results indicate that the characteristics of the activities involved are the most important factors influencing the process of schedule change. Several correlations among schedule modifications were found. Harry Timmermans is a Professor at the Eindhoven University of Technology. He is also Director of the Urban Planning Group and the European Institute of Retailing and Services Studies. Tomás Ruiz is a Lecture and researcher at the Technical University of Valencia (Spain). Prior to his employment with the University, Dr. Ruiz was a consultant at Estudios, Proyectos y Planificación S.A.