Dick Ettema

Modeling departure time choice in the context of activity scheduling behavior

Development of a model of departure choice behavior in the context of activity-travel scheduling behavior is reported. Based on a brief characterization of the literature, some key properties of the desired model are identified. Then, the theoretical framework is outlined and an operational model is derived. Next, the model is estimated with activity-travel diary data, collected in the context of the Albatross model system. The results are promising. Avenues of future research are discussed.

SMASH (simulation model of activity scheduling heuristics) : some simulations

Travel decision making is increasingly regarded as a highly complex process in which individuals not only decide about frequency of trips, travel modes, and routes, but also about activity participation and sequencing and timing and duration of activities and trips. This raises the question of whether or not traditional discrete-choice models still provide the best starting point for realistically modeling such a process. Some scholars consider computational process models (CPMs) a promising approach because they allow for heuristic search and suboptimal reasoning processes, which are typical for complex decision making. A model of activity scheduling, SMASH (Simulation Model of Activity Scheduling Heuristics), which incorporates aspects of discrete-choice modeling and CPMs, has been proposed. The model describes the pretrip planning phase, in which individuals decide which activities to perform, at what locations, at what times, in which sequence, and how to travel to the various activity sites. The calibration of this model, using data collected with the interactive computerized procedure MAGIC, has been described in the literature. The results indicated that when scheduling their activities, subjects seem to trade off attributes of activities (time constraints, duration), attributes of the schedule (time spent on activities, overall travel time, realism) and characteristics of the scheduling process (amount of effort already involved in the scheduling process) to obtain feasible schedules. More extensive tests, using simulation experiments, of the models internal, predictive, and face validity are described. SMASH was used to predict subjects activity schedules based on their activity agenda and information about their spatio-temporal circumstances. The predicted schedules were then compared with the activity schedules conceived by the subjects themselves under different circumstances, to assess the models validity. The tests indicated that the model provided satisfactory results with respect to the reproduction of the observed activity schedules. The results of the validity test warrant the use of the model for assessing the effects of various policy measures such as time policies, land use policies, and travel demand management.

MODELLING PERCEPTION UPDATING OF TRAVEL TIMES IN THE CONTEXT OF DEPARTURE TIME CHOICE UNDER ITS

Traffic information is increasingly regarded as a tool to achieve a more efficient use of the road network. As traffic information is often applied in the context of routine trips, the question arises how travellers integrate traffic information with the knowledge of travel conditions gained through daily experience. To describe this process, the paper proposes a model of perception updating of travel times in the context of departure time decisions. The model applies a CHAID-based classification algorithm to describe how travellers classify trips made under various conditions (departure time and presence of traffic information) into mental classes with comparable expectations in terms of travel time. Thus, it is assumed that the learning process depends on a set of conditions, one of which is the available travel time information. The model is tested through a series of numerical experiments. The results suggest that the model describes learning and adaptation behaviour in a plausible way. Through increased experience, perception of travel times is improved, and more departure time classes are distinguished. However, this does not seem to lead to shorter travel times or higher trip utilities. Also the presence of travel time information may be, depending on the history of trip outcomes, distinguished as a significant indicator of the expected travel time. We conclude that the model provides a good starting point for the further development of learning and adaptation models in the context of ITS.

The Role of Planning for Intention-Behavior Consistency

Two studies investigated how planning affects intention-behavior consistency. In Study 1 an experimental group and control group which each consisted of 14 undergraduates were requested in computerized interviews to indicate which activities they intended to perform on the following day. Subjects in the experimental group were also requested in a second phase of the interviews to specify when and where they intended to perform the activities. The results showed that activities for which time and place had been specified were more likely to be performed. In Study 2 another 75 undergraduates volunteered to participate in an experiment in which they were requested to perform an activity (reporting mood effects of reading a prose excerpt) by themselves on one of three following days. One group of subjects only agreed to perform the activity, another group agreed to perform the activity as well as indicated when and where they would do it, and a third group in addition to this indicated which other activities they would perform on the same day. In support of the hypothesis that planning an activity increases the likelihood that it will be performed, the results showed that subjects who indicated other activities more frequently performed the target activity. More efficient time management resulting from planning may account for the findings, although further research is needed to show this conclusively.