TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents

Xiangliang Zhang (University of Notre Dame) · Di Wang (Wuhan University) · Chen Zhang (Tsinghua University, Tsinghua University) · Tian Lan (George Washington University) · Ziyue Li (Technical University of Munich (TUM)) · Xiaochuan Gou (King Abdullah University of Science and Technology) · Junpeng Lin (Tsinghua University, Tsinghua University) · zhishuai Li (SenseTime Research) · Bingyu Zhao (Technische Universität Wien)
causal relationshipsdeep learning modelsforecasting tasksglobal causal analysisincident classificationincident risk inferenceincident typeslocal causal analysismeta-attributesroad segmentsspatiotemporal alignmenttime-series analysistraffic flow predictiontraffic-incident interactionstraffident dataset

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) from year 2022 to 2024: our TraffiDent dataset includes traffic, i.e., time-series indexes on traffic flow, lane occupancy, and average vehicle speed, and incident, 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. Previous datasets typically contain only traffic or incident data in isolation, limiting research to general forecasting tasks. TraffiDent integrates both, enabling detailed analysis of traffic-incident interactions and causal relationships. To demonstrate its broad applicability, we design: (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 https://xaitraffic.github.io.