Final Report Summary - U4IA ((Euphoria): Emerging Urban Futures and Opportune Repertoires of Individual Adaptation)
The goal of the U4IA research program has been to formulate a comprehensive model of dynamic activity-travel patterns, expanding and integrating concepts and partial approaches that have been suggested over the last few years. Dynamics involve short-term rescheduling of activities and travel in time and space, mid-term adjustment and long-term adaptation. Dynamics relate to both endogenous change and exogenously triggered change. Conceptually, it is assumed that individuals and households beliefs about their environment (their cognitive environment) will change when they travel as they learn, due to social contacts and due to active or passive exposure to new information. Changing beliefs may trigger changes in the organisation of activities in time and space: some coping strategy will be reinforced; others not because individuals learn under which conditions particular strategies are most effective. Thus, a repertoire of activity-travel patterns will evolve. The accumulated decisions of individuals will lead to emerging aggregate patterns to which individuals need to react or pro-act strategically, contributing to further endogenously generated dynamics. Long-term decisions such as demographic change, changing job or house may also prompt or force people to adapt their activity-travel patterns. Exogenously triggered change involves change in the urban and/or transportation environment and/or the larger socio-economic institutional contexts. It may be unplanned or planned (policies). The dynamic multi-agent model of activity-travel repertoires simulates the primary, secondary and higher order effects of such emerging urban futures on dynamic repertoires of activity-travel patterns.
The program has been organized in terms of several PhD projects and integrating postdoc project. Long-term, mid-term and short-term adaptation processes have been addressed. The first project has modeled how travellers learn by travelling and travel information and in doing so built up a dynamic cognitive representation of the urban and transportation environment. The PhD project led to the formulation of this models that can be used as a cognitive layer in the multi-agent model. A second PhD project analyzed the effects of dynamic social networks on changes in activity-travel behavior. Results of this project emphasized path-dependency in dynamic social networks and it impact on travel patterns. To date, this project remains the only project on dynamic social networks in the travel behavior community. Another long-term effect is related to residential move. Using a retrospective survey, the traditional built environment – travel behavior relationship has been redefined in this PhD project to encompass the dynamics of life trajectories and corresponding housing and job careers. Results suggest that the already weak link between travel and the built environment is further weakened if the dynamics are considered. In the short-term, individuals and household may consider adapting one or more components of their repertoire of activity-travel behavior in response to exogenous policies. This relationship has been addressed in two other PhD projects. One project was concerned with the effects of accumulated pricing policies on dynamic activity-travel demand, while the second project was concerned with the dynamic effects of dramatically increased energy prices. A main result of the first project is that positive effects of pricing/bonus policies dampen out over time. Increasing energy prices cause a reduction in vehicle miles travelled, particular for discretionary travel, but the impact differs between different socio-demographic groups. Finally, a model of endogenous change was developed in the context of another PhD project. The model shows how habits, scripts and repertoires built over time under stationary conditions and how accumulated stress built in on a daily basis when traffic conditions do not meet aspirations, ultimately result in long-term change to cope with increasing stress.
The project also led to innovations and new experiences in the use of new technology in collecting activity-travel diaries. First, up to three months GPS data were collected for a sample of respondents. These data were also used to formulate a model of multitasking. Second, a data collection system allowing a detailed measurement of activity-travel repertoires and interactive experiments to collect data on the adaptations of these repertoires in response to particular policies was built. Third, several retrospective surveys were administered. Finally, mixture amount experimental design were developed for some PhD projects; this approach to the design of experiment was new to the travel behavior community.
The program has been organized in terms of several PhD projects and integrating postdoc project. Long-term, mid-term and short-term adaptation processes have been addressed. The first project has modeled how travellers learn by travelling and travel information and in doing so built up a dynamic cognitive representation of the urban and transportation environment. The PhD project led to the formulation of this models that can be used as a cognitive layer in the multi-agent model. A second PhD project analyzed the effects of dynamic social networks on changes in activity-travel behavior. Results of this project emphasized path-dependency in dynamic social networks and it impact on travel patterns. To date, this project remains the only project on dynamic social networks in the travel behavior community. Another long-term effect is related to residential move. Using a retrospective survey, the traditional built environment – travel behavior relationship has been redefined in this PhD project to encompass the dynamics of life trajectories and corresponding housing and job careers. Results suggest that the already weak link between travel and the built environment is further weakened if the dynamics are considered. In the short-term, individuals and household may consider adapting one or more components of their repertoire of activity-travel behavior in response to exogenous policies. This relationship has been addressed in two other PhD projects. One project was concerned with the effects of accumulated pricing policies on dynamic activity-travel demand, while the second project was concerned with the dynamic effects of dramatically increased energy prices. A main result of the first project is that positive effects of pricing/bonus policies dampen out over time. Increasing energy prices cause a reduction in vehicle miles travelled, particular for discretionary travel, but the impact differs between different socio-demographic groups. Finally, a model of endogenous change was developed in the context of another PhD project. The model shows how habits, scripts and repertoires built over time under stationary conditions and how accumulated stress built in on a daily basis when traffic conditions do not meet aspirations, ultimately result in long-term change to cope with increasing stress.
The project also led to innovations and new experiences in the use of new technology in collecting activity-travel diaries. First, up to three months GPS data were collected for a sample of respondents. These data were also used to formulate a model of multitasking. Second, a data collection system allowing a detailed measurement of activity-travel repertoires and interactive experiments to collect data on the adaptations of these repertoires in response to particular policies was built. Third, several retrospective surveys were administered. Finally, mixture amount experimental design were developed for some PhD projects; this approach to the design of experiment was new to the travel behavior community.