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.

翻译：长期以来，针对两个高度相关领域——交通与事故——的研究一直相互分离。交通领域的研究见证了日益复杂的深度学习模型，例如，旨在将预测精度再提升几个百分点；而事故领域的研究则仅关注事故本身，例如，用于推断事故风险。我们首次在一个大规模区域（16,972个交通节点）内，将这两个领域的数据在时空上对齐（时间跨度为2022年至2024年）：我们的TraffiDent数据集包含了交通数据（即关于交通流量、车道占用率和平均车速的时间序列指标）以及事故数据（其记录与交通数据在时空上对齐，涵盖七种不同的事故类别）。此外，每个节点都包含了详细的物理层面和政策层面的车道元属性。以往的数据集通常仅包含孤立的交通数据或事故数据，将研究限制在一般性的预测任务上。TraffiDent将两者整合，使得对交通-事故相互作用及因果关系的详细分析成为可能。为展示其广泛的适用性，我们设计了以下任务：(1) 事故后交通预测，以量化不同事故对交通指标的影响；(2) 基于交通指标的事故分类，以确定事故类型并采取预防措施；(3) 交通指标、元属性与事故之间的全局因果分析，为各种因素间的相互关系提供高层指导；(4) 道路节点内的局部因果分析，以检验不同事故如何影响路段间的关系。该数据集可在 https://xaitraffic.github.io 获取。