  Deep learning (DL) models for spatio-temporal traffic flow forecasting employ
convolutional or graph-convolutional filters along with recurrent neural
networks to capture spatial and temporal dependencies in traffic data. These
models, such as CNN-LSTM, utilize traffic flows from neighboring detector
stations to predict flows at a specific location of interest. However, these
models are limited in their ability to capture the broader dynamics of the
traffic system, as they primarily learn features specific to the detector
configuration and traffic characteristics at the target location. Hence, the
transferability of these models to different locations becomes challenging,
particularly when data is unavailable at the new location for model training.
To address this limitation, we propose a traffic flow physics-based feature
transformation for spatio-temporal DL models. This transformation incorporates
Newell's uncongested and congested-state estimators of traffic flows at the
target locations, enabling the models to learn broader dynamics of the system.
Our methodology is empirically validated using traffic data from two different
locations. The results demonstrate that the proposed feature transformation
improves the models' performance in predicting traffic flows over different
prediction horizons, as indicated by better goodness-of-fit statistics. An
important advantage of our framework is its ability to be transferred to new
locations where data is unavailable. This is achieved by appropriately
accounting for spatial dependencies based on station distances and various
traffic parameters. In contrast, regular DL models are not easily transferable
as their inputs remain fixed. It should be noted that due to data limitations,
we were unable to perform spatial sensitivity analysis, which calls for further
research using simulated data.
