FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
Zhanyu Liu Chumeng Liang Guanjie Zheng Hua Wei
This paper proposes the fine-grained traffic prediction task (e.g. interval
between data points is 1 minute), which is essential to traffic-related
downstream applications. Under this setting, traffic flow is highly influenced
by traffic signals and the correlation between traffic nodes is dynamic. As a
result, the traffic data is non-smooth between nodes, and hard to utilize
previous methods which focus on smooth traffic data. To address this problem,
we propose Fine-grained Deep Traffic Inference, termed as FDTI. Specifically,
we construct a fine-grained traffic graph based on traffic signals to model the
inter-road relations. Then, a physically-interpretable dynamic mobility
convolution module is proposed to capture vehicle moving dynamics controlled by
the traffic signals. Furthermore, traffic flow conservation is introduced to
accurately infer future volume. Extensive experiments demonstrate that our
method achieves state-of-the-art performance and learned traffic dynamics with
good properties. To the best of our knowledge, we are the first to conduct the
city-level fine-grained traffic prediction.