  Activity generation plays an important role in activity-based demand
modelling systems. While machine learning, especially deep learning, has been
increasingly used for mode choice and traffic flow prediction, much less
research exploiting the advantage of deep learning for activity generation
tasks. This paper proposes a novel activity pattern generation framework by
incorporating deep learning with travel domain knowledge. We model each
activity schedule as one primary activity tour and several secondary activity
tours. We then develop different deep neural networks with entity embedding and
random forest models to classify activity type, as well as to predict activity
times. The proposed framework can capture the activity patterns for individuals
in both training and validation sets. Results show high accuracy for the start
time and end time of work and school activities. The framework also replicates
the start time patterns of stop-before and stop-after primary work activity
well. This provides a promising direction to deploy advanced machine learning
methods to generate more reliable activity-travel patterns for transport demand
systems and their applications.
