  Long-separated research has been conducted on two highly correlated tracks:
traffic and incidents. Traffic track witnesses complicating deep learning
models, e.g., to push the prediction a few percent more accurate, and the
incident track only studies the incidents alone, e.g., to infer the incident
risk. We, for the first time, spatiotemporally aligned the two tracks in a
large-scale region (16,972 traffic nodes) over the whole year of 2023: our
XTraffic dataset includes traffic, i.e., time-series indexes on traffic flow,
lane occupancy, and average vehicle speed, and incidents, whose records are
spatiotemporally-aligned with traffic data, with seven different incident
classes. Additionally, each node includes detailed physical and policy-level
meta-attributes of lanes. Our data can revolutionalize traditional
traffic-related tasks towards higher interpretability and practice: instead of
traditional prediction or classification tasks, we conduct: (1) post-incident
traffic forecasting to quantify the impact of different incidents on traffic
indexes; (2) incident classification using traffic indexes to determine the
incidents types for precautions measures; (3) global causal analysis among the
traffic indexes, meta-attributes, and incidents to give high-level guidance of
the interrelations of various factors; (4) local causal analysis within road
nodes to examine how different incidents affect the road segments' relations.
The dataset is available at http://xaitraffic.github.io.
