Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour

The paper presents an econometric investigation of the behavioural relationship between transportation system performance in terms of travel time changes and daily activity–travel scheduling processes. Innovative survey data on the complete daily activity-scheduling adaptation process is used jointly with revealed scheduling information. The survey, conducted in Zurich, Switzerland, collected daily scheduling information together with stated adaptation responses corresponding to four adaptation scenarios. The four scenarios are defined by applying hypothetical increases in travel time of 50%, 100%, and 200% and a 50% decrease in travel time. Stated adaptation responses are collected in the context of 24-h activity scheduling. Data are used to estimate RUM based daily travel activity scheduling models. Models are estimated for one revealed schedule and four stated scheduling datasets. In addition, a joint model is estimated for pooled revealed and stated scheduling data. In the joint model, separate scale/variance parameters are estimated for revealed and stated information. Results clearly identify the nonlinear responses of activity–travel scheduling to the changes in travel time. Asymmetric responses are shown for travel time increases and decreases. People become more conservative with time expenditures when scheduling activities subject to increased travel times. However, beyond a certain limit of travel time increase, scheduling behaviour becomes more unpredictable. The lessons learned from this investigation have implications in the application of activity-based models for forecasting and policy analyses. Models developed using only a revealed preference dataset should not be used to extrapolate to situations where travel times changes by large margins. The results also prove that significant improvements in capturing behavioural responses in the activity scheduling process are possible by pooling revealed preference and stated preference data sets and jointly modelling with an explicit representation of RP scale/variance differences.