  Traffic prediction is one of the key elements to ensure the safety and
convenience of citizens. Existing traffic prediction models primarily focus on
deep learning architectures to capture spatial and temporal correlation. They
often overlook the underlying nature of traffic. Specifically, the sensor
networks in most traffic datasets do not accurately represent the actual road
network exploited by vehicles, failing to provide insights into the traffic
patterns in urban activities. To overcome these limitations, we propose an
improved traffic prediction method based on graph convolution deep learning
algorithms. We leverage human activity frequency data from National Household
Travel Survey to enhance the inference capability of a causal relationship
between activity and traffic patterns. Despite making minimal modifications to
the conventional graph convolutional recurrent networks and graph convolutional
transformer architectures, our approach achieves state-of-the-art performance
without introducing excessive computational overhead.
