Structured incident information on transportation systems from traffic news

Historical traffic news provides detailed information describing the affected objects, scopes, status, and the special arrangements of traffic incidents, which is a critical complement to empirical data for incident analysis and response. To extract the Knowledge Graph (KG) of incidents from unstructured traffic news, an automatic workflow is proposed in this paper integrating rule-based matching methods and the pre-trained language processing model. Over eight thousand traffic incidents from 2016 to 2020 in Hong Kong are depicted into structured RDF (Resource Description Framework) triples by this workflow successfully and stored in the Science Data Bank. This dataset can be reused to evaluate the impact and response of historical incidents and to adjust parameters for traffic simulation. The code of this workflow can also be used to synchronously generate the KG of incidents from real-time traffic news for further proofreading and tracking of incident information.

历史交通新闻报道了受影响的对象、范围、状态以及交通事件特殊安排的详细信息，这对于事故分析和响应的实证数据而言，是一种至关重要的补充。本文提出了一种自动工作流程，以从非结构化的交通新闻中提取事故知识图谱（KG），该流程整合了基于规则的匹配方法和预训练的语言处理模型。成功地将2016年至2020年香港的超过八千起交通事件描述为结构化的RDF（资源描述框架）三元组，并存储在科学数据银行中。此数据集可用于评估历史事件的影响和响应，以及调整交通模拟的参数。此外，此工作流程的代码也可用于实时同步生成事故知识图谱，以进行进一步的校对和事故信息的追踪。