在德国,警方通过新闻界发表的报告既不统一撰写,也不向公众开放。缺乏对电动踏板车撞车及其原因的全面和基于事实数据的分析。我们基于系统的Web内容挖掘过程,通过德国新闻门户网站在两年内收集了1936年与崩溃相关的报告。情绪分析结果显示,警方报告的报道主要是事实和中立的,因此,可用于基于关键字的分析。在确定报告中最相关的46个关键词后,我们生成了一个邻接矩阵来调查关键字\'依赖关系,可视化了最相关关键词的网络和依赖关系,并使用Louvain算法将它们分为四个主题簇。我们的结果和发现表明,在药物影响下驾驶,尤其是酒精,是一个严重的问题。成对骑电动踏板车,在禁止的地形或错误的方向也是撞车的常见原因。电动滑板车骑手的后果是重伤,吊销驾驶执照,罚款,刑事指控,并导致财产损失。Further,在电动踏板车的乘客中,佩戴防护装备和头盔的接受度很低。根据我们的结果和发现,我们建议在某些地方的夜间禁止电动滑板车,首次使用电动踏板车之前的强制性驾驶测试,戴头盔。
In Germany, police reports published via press are neither uniformly written nor accessible to the public. There is a lack of comprehensive and factual data-based analyses of e-scooter crashes and their causes. We collected 1936 crash-related reports over two years via the German press portal based on a systematic web content mining process. Sentiment analysis results revealed that the police reports\' coverage is predominantly factual and neutral and, therefore, useful for keyword-based analyses. After identifying the 46 most relevant keywords in the reports, we generated an adjacency matrix to investigate the keywords\' dependencies, visualized the network and dependencies of the most relevant keywords, and categorized them into four thematic clusters using the Louvain algorithm. Our results and findings reveal that driving under drug influence, especially alcohol, is one serious problem. Riding e-scooter in pairs and on forbidden terrain or in the wrong direction are also common causes of crashes. Consequences for e-scooter riders are severe injuries, driving license revocation, fines, criminal charges, and incurring for property damage. Further, wearing protective gear and helmets is of low acceptance among the e-scooter ridership. Based on our results and findings, we recommend e-scooter bans during the night times for some locations, obligatory driving tests before first e-scooter use, and helmet wearing.