Details
| Original language | English |
|---|---|
| Article number | 106833 |
| Journal | Accident Analysis and Prevention |
| Volume | 178 |
| Early online date | 29 Sept 2022 |
| Publication status | Published - Dec 2022 |
Abstract
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.
Keywords
- Accident analysis, E-scooter, Network graph, Sentiment analysis, Web content mining
ASJC Scopus subject areas
- Social Sciences(all)
- Human Factors and Ergonomics
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Medicine(all)
- Public Health, Environmental and Occupational Health
Sustainable Development Goals
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In: Accident Analysis and Prevention, Vol. 178, 106833, 12.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Web content mining analysis of e-scooter crash causes and implications in Germany
AU - Brauner, Tim
AU - Heumann, Maximilian
AU - Kraschewski, Tobias
AU - Prahlow, Oliver
AU - Rehse, Jan
AU - Kiehne, Christian
AU - Breitner, Michael H.
N1 - Funding Information: ☆ Acknowledgments This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Accident analysis
KW - E-scooter
KW - Network graph
KW - Sentiment analysis
KW - Web content mining
UR - http://www.scopus.com/inward/record.url?scp=85138818592&partnerID=8YFLogxK
U2 - 10.1016/j.aap.2022.106833
DO - 10.1016/j.aap.2022.106833
M3 - Article
AN - SCOPUS:85138818592
VL - 178
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
SN - 0001-4575
M1 - 106833
ER -