A novel activity pattern generation incorporating deep learning for transport demand models
This work addresses the need for more reliable activity-travel patterns in transport demand systems, though it is incremental as it applies deep learning to a less-explored task within an existing domain.
The paper tackled the problem of generating activity patterns for transport demand models by proposing a novel framework that incorporates deep learning with travel domain knowledge, achieving high accuracy in predicting start and end times for work and school activities and replicating start time patterns for primary work activities.
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.