Web content mining analysis of e-scooter crash causes and implications in Germany

The characteristics of e-scooter accidents reported by police in Türkiye

OBJECTIVES

Shared e-scooter service has been offered by various operators in Türkiye since 2019. The use of private and shared e-scooters is increasing, especially in large cities. This raises social concerns about the dangers e-scooters pose regarding traffic safety and injuries requiring medical attention. This study aims to investigate the accidents related to e-scooters in Türkiye to determine the contributing factors and accident characteristics.

METHODS

In this study, accident reports (collision reports) for 780 e-scooter collisions that occurred in 2021 in Türkiye were examined, and 771 accidents were included. Accident data were obtained from the Traffic Department of the Ministry of Interior, General Directorate of Security. Descriptive statistics of the factors affecting e-scooter accidents are presented to determine the relationship and differences; chi-square tests, independent samples t-tests, one-way analysis of variance, and binary logistic regression methods were used.

RESULTS

Male e-scooter riders are involved in crashes and injured approximately 4 times more often than female riders. The average age for men injured in e-scooter accidents is 30.4, and the mean age for women is 27.2. For both males and females, most injuries occurred in the 15 to 20 age group. Riders under the age of 18 constitute a significant proportion of the accidents (32.5%). Most e-scooter accidents occur on Mondays and during the month of August. Most accidents occurred between 12:00 p.m. and 1:59 p.m. (15.7%) and between 4:00 p.m. and 5:59 p.m. (15.7%), mainly during the daytime. About half of the accidents occurred at intersections. In 10.5% of accidents, the accident occurred at a pedestrian crossing. Approximately one-fifth of the accidents involved falls, and the most common type of collision was a side collision (44.2%). The binary logistic regression model showed that multivehicle accidents occur more often at intersections and during busy traffic hours. Single-vehicle accidents are more common on concrete roads and stone block roads.

CONCLUSIONS

Deaths and injuries caused by road traffic accidents are a public health problem in Türkiye and constitute a significant health burden. If necessary precautions are not taken, this burden is likely to increase. We hope that the findings from this study will help reduce e-scooter accidents in Türkiye.

Injury severity analysis of single-vehicle and two-vehicle crashes with electric scooters: A random parameters approach with heterogeneity in means and variances

In recent years, the electric scooter has become one of the most popular means of transportation on short trips. Due to the lag in the formulation of transportation policies and regulations, coupled with the increasing number of electric scooter crashes, there has been growing concern about the safety of pedestrians and electric scooter riders. For the first time in the extant literature, this study aims to analyze injury severity of electric scooter crashes by unobserved heterogeneity modeling approaches. A random parameters approach with heterogeneity in means and variances is utilized to examine the factors influencing injury severity, using data collected from the STATS19 road safety database. Electric scooter crashes are classified as single-vehicle crashes and two-vehicle crashes, with injury severity categorized into two groups: fatalities or serious injuries, and slight injuries. The model estimation was conducted by considering several variables including roadway, environment, temporality, vehicle, and rider characteristics, as well as second-party vehicle and driver characteristics and manners of collision specific to two-vehicle crashes. The results of the model estimation reveal that certain factors had relatively stable effects with the varying degree of crash injury severity outcomes in both single-vehicle crashes and two-vehicle crashes. These factors include nighttime incidents, weekdays, male riders, and an increase in rider age, all of which are associated with more severe injury outcomes. Moreover, the random parameters logit model with heterogeneity in means and variances is more flexible in accounting for unobserved heterogeneity and exhibits better goodness of fit. This study improves the understanding of electric scooter safety, and the finding can better inform public policy regarding electric scooter use to improve road safety and reduce injury severity of electric scooter crashes.

An AI Safety Monitoring System for Electric Scooters Based on the Number of Riders and Road Types

Electric scooters are quickly becoming a popular form of mobility in many cities around the world, which has led to a surge in safety incidents. Moreover, electric scooters are not equipped with safety devices for riders, which can lead to serious accidents. In this study, a footrest, data-collection module, and accelerometer module for electric scooters were developed to prevent various accidents caused by the rapid increase in the use of electric scooters. In the experiment, the boarding data of the electric-scooter riders were collected from the footrest and data-collection module. Moreover, the driving data of the electric scooters for different road types were collected with the accelerometer module. We then trained two artificial intelligence (AI) models based on convolutional neural networks (CNNs) for different types of data. When we considered the learning accuracy and mean square error (MSE), which are performance indicators of the ability of the trained AI models to discriminate data, for each AI model, the learning accuracy converged to 100% and the MSE converged to 0. Further, this study is expected to help reduce the accident rate of electric scooters by resolving the causes of frequent accidents involving electric scooters around the world.