E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances
The increasing use of e-scooters in urban areas has raised safety concerns, necessitating research for effective safety interventions. This study analyzes three years of e-scooter crash data from the United Kingdom using statistical and machine learning methods to identify key factors influencing cr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | IATSS Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111225000135 |
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| author | Ali Agheli Kayvan Aghabayk Matin Sadeghi Subasish Das |
| author_facet | Ali Agheli Kayvan Aghabayk Matin Sadeghi Subasish Das |
| author_sort | Ali Agheli |
| collection | DOAJ |
| description | The increasing use of e-scooters in urban areas has raised safety concerns, necessitating research for effective safety interventions. This study analyzes three years of e-scooter crash data from the United Kingdom using statistical and machine learning methods to identify key factors influencing crash severity. We employed a random parameters logit model and investigated several machine learning algorithms, with XGBoost performing best. Analysis reveals that severe injuries are more likely in crashes involving senior riders, at night with lighting, and at T, staggered, or crossroad junctions. Further insights from the XGBoost-SHAP analysis and heterogeneity in means and variances of random parameters revealed nuanced patterns. While crashes involving female riders or crashes at give way or uncontrolled junctions typically have less severe outcomes, specific condition (young female riders or nighttime crashes at these junctions) intensify the risk of severe injuries. These insights advocate for tailored public policy adjustments and infrastructure enhancements to mitigate e-scooter risks, ensuring safer urban mobility for all demographics. |
| format | Article |
| id | doaj-art-9e0736cc90644efd9613b65ebb137a43 |
| institution | Kabale University |
| issn | 0386-1112 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj-art-9e0736cc90644efd9613b65ebb137a432025-08-20T03:29:03ZengElsevierIATSS Research0386-11122025-07-0149215516810.1016/j.iatssr.2025.03.004E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variancesAli Agheli0Kayvan Aghabayk1Matin Sadeghi2Subasish Das3School of Civil Engineering, College of Engineering, University of Tehran, IranSchool of Civil Engineering, College of Engineering, University of Tehran, Iran; Corresponding author.School of Civil Engineering, College of Engineering, University of Tehran, IranIngram School of Engineering, Texas State University, San Marcos, USAThe increasing use of e-scooters in urban areas has raised safety concerns, necessitating research for effective safety interventions. This study analyzes three years of e-scooter crash data from the United Kingdom using statistical and machine learning methods to identify key factors influencing crash severity. We employed a random parameters logit model and investigated several machine learning algorithms, with XGBoost performing best. Analysis reveals that severe injuries are more likely in crashes involving senior riders, at night with lighting, and at T, staggered, or crossroad junctions. Further insights from the XGBoost-SHAP analysis and heterogeneity in means and variances of random parameters revealed nuanced patterns. While crashes involving female riders or crashes at give way or uncontrolled junctions typically have less severe outcomes, specific condition (young female riders or nighttime crashes at these junctions) intensify the risk of severe injuries. These insights advocate for tailored public policy adjustments and infrastructure enhancements to mitigate e-scooter risks, ensuring safer urban mobility for all demographics.http://www.sciencedirect.com/science/article/pii/S0386111225000135Electric scooterMicromobilityE-scooter injury severityInterpretable machine learningUnobserved heterogeneity |
| spellingShingle | Ali Agheli Kayvan Aghabayk Matin Sadeghi Subasish Das E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances IATSS Research Electric scooter Micromobility E-scooter injury severity Interpretable machine learning Unobserved heterogeneity |
| title | E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances |
| title_full | E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances |
| title_fullStr | E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances |
| title_full_unstemmed | E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances |
| title_short | E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances |
| title_sort | e scooter crash severity in the united kingdom a comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances |
| topic | Electric scooter Micromobility E-scooter injury severity Interpretable machine learning Unobserved heterogeneity |
| url | http://www.sciencedirect.com/science/article/pii/S0386111225000135 |
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