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Shared electric scooters (e-scooters) offer a potential strategy to mitigate environmental concerns and congestion. However, successfully addressing these issues with e-scooters requires adoption across a diverse array of consumers. Understanding the differences between users and non-users can improve shared e-scooter appeal, operation and safety. The objective of this paper is to compare shared e-scooter users and non-users in terms of their perceptions on safety, trip behaviors, other shared modes, risk propensity and willingness to adopt technology. A survey was conducted involving 210 (51.3%) users and 199 (48.7%) non-users of shared e-scooters. Binary logistic regression and chi-squared tests were performed. The results reveal that users demonstrated a higher risk propensity and were more likely to be early adopters of new technologies. Non-users tended to place higher importance on helmet use, while users have an overall increased feeling of safety associated with riding e-scooters in vehicle lanes, on sidewalks and being passed by e-scooters as a pedestrian. Overall, users also have a more positive perception of e-scooter sanitary levels than non-users, and a more positive perception on the sanitary levels and usability of e-scooters over e-bicycles. These findings can provide guidance to urban planners, municipal authorities and micromobility providers in developing infrastructure and policies to better support micromobility adoption.

E-scooters have gained popularity worldwide in the last few years. Due to the increase in users, more accidents related to e-scooters can be observed. The present study aimed to analyse epidemiological data, characteristics, and severity of injuries in patients admitted to a Level I trauma centre in Switzerland (Inselspital Bern, University Hospital Bern) after accidents associated with e-scooters. This retrospective case series evaluated 23 patients who presented to the University Hospital of Bern between 1 of May 2019 and 31 of October 2021 after an e-scooter accident. Data were collected on patient demographics, time and cause of the accident, speed, alcohol consumption, helmet use, type and localisation of injury, number of injuries per patient, and outcome. Men were most frequently affected (61.9%). The mean age was 35.8 (STD 14.8) years. Slightly more than half (52.2%) of all accidents were self-inflicted. Most accidents were reported during the night (7 p.m. to 7 a.m., 60.9%) and in summer (43.5%). Alcohol consumption was reported in 43.5% of cases, with a mean blood alcohol level of 1.4 g/l. Most injuries were observed in the face (25.3%) and head/neck area (20.25%). Skin abrasions (56.5%) and traumatic brain injury (43.5%) were the most common types of traumata in terms of total number of patients. Only in one case it was reported that a protective helmet had been worn. Five patients required hospitalisation and four patients underwent surgery. Three patients underwent emergency orthopaedic surgery, and one patient underwent emergency neurosurgery. E-scooter accidents result in a significant number of facial and head/neck injuries. E-scooter riders would potentially benefit from a helmet to protect them in the event of an accident. Additionally, the results of this study indicate that a significant number of e-scooter accidents in Switzerland occurred under the influence of alcohol. Prevention campaigns to raise awareness of the risks of driving e-scooters under the influence of alcohol could help prevent future accidents.

E-scooters aspire to provide flexibility to their users while covering the first/last mile of a multimodal trip. Yet, their dual travel behavior, i.e., utilizing both vehicles’ roadways and pedestrians’ sidewalks, creates new challenges to transport modelers. This study aims to model e-scooter riding behavior in comparison to traditional urban transport modes, namely car and walking. The new modeling approach is based on perceived safety that is influenced by the road environment and affects routing behavior. An ordinal logistic model of perceived safety is applied to classify road links in a 7-point Likert scale. The parametric utility function combines only three basic parameters: time, cost, and perceived safety. First/last mile routing choices are modeled in a test road network developed in Athens, Greece, utilizing the shortest-path algorithm. The proposed modeling approach proved to be useful, as the road environment of an urban area is heterogenous in terms of safety perceptions. Indeed, the model outputs show that the flexibility of e-scooters is limited in practice by their low-perceived safety. To avoid unsafe road environments where motorized traffic dominates, e-scooter riders tend to detour. This decision-making process tool can identify road network discontinuities. Nevertheless, their significance regarding routing behavior should be further discussed.

New-generation technologies on vehicles provide many advantages in terms of cost, time, and the environment in the transportation, logistics, freight, and delivery service sectors. This study aimed to measure the effect of using e-scooter vehicles in mail delivery on the energy cost and delivery time in Turkey. Considering the number of test drives in e-scooter applications of potential regions, the amount of energy consumption and driving time data were used. The number of test drives for each e-scooter was assumed as a package or postal delivery amount. The methodology of this study consisted of measuring the effect of input parameters on output variables using the linear response optimization regression method and minimizing the amount of energy consumed and delivery time. The nine input variables and two output variables based on the test drive were analyzed in this study. The distance to the delivery address, region where the delivery address was located, and temperature were found to be statistically significant predictors of the amount of energy required for delivery. The statistical significance levels of time zone, distance, temperature, rainfall, and region factors were calculated as 0.053, 0.001, 0.0033, 0.044, and 0.042, respectively. Driver age, data time zone, distance, wind speed, and delivery region factors affected the time required for delivery with an e-scooter. The statistical significance levels of these factors were calculated as 0.02, 0.001, 0.001, 0.043, and 0.001, respectively. Additionally, N (p; 0.042), NE (p; 0.030), and W (p; 0.057) wind directions directly influenced the delivery time. SE (p; 0.017) was the only wind direction that statistically significantly affected energy consumption. The objective functions were estimated by calculating the optimum values of the input parameters for the minimum energy consumption and delivery time. The optimum values of both input and output variables were calculated based on the desirability values of the optimization models, which were in the optimum solution set. The average data of the optimum values of the objective functions were computed as 2.83 for the number of tests and TRY 0.021 (per 0.098 km) for the energy cost required for delivery. The necessity of using e-scooters, which are more environmentally friendly, economical, and time-saving than traditional delivery vehicles, in postal delivery service is among the prominent suggestions of this study.

This study aims to analyze the variables that affect the accidents experienced by e-scooter users and to estimate the probability of an accident during travel with an e-scooter vehicle. The data of e-scooter drivers, offered for use via rental application in 15 different cities of Turkey, were run in this study. The methodology of this study consists of testing the effects of the input parameters with the statistical analysis of the data, estimating the probability of an e-scooter accident with machine learning, and calculating the optimum values of the input parameters to minimize e-scooter accidents. By running SVM, RF, AB, kNN, and NN algorithms, four statuses (completed, injured, material damage, and nonapplicable) likely to be encountered by shared e-scooter drivers during the journey are estimated in this study. The F1 score values of the SVM, RF, kNN, AB, and NN algorithms were calculated as 0.821, 0.907, 0.839, 0.928, and 0.821, respectively. The AB algorithm showed the best performance with high accuracy. In addition, the highest consistency ratio in the ML algorithms belongs to the AB algorithm, which has a mean value of 0.930 and a standard deviation value of 0.178. As a result, the rental experience, distance, driving time, and driving speed for a female driver were calculated as 100, 10.44 km, 48.33 min, and 13.38 km/h, respectively, so that shared e-scooter drivers can complete their journey without any problems. The optimum values of the independent variables of the rental experience, distance, driving time, and driving speed for male drivers were computed as 120, 11.49 km, 52.20 min, and 17.28 km/h, respectively. Finally, this study generally provides a guide to authorized institutions so that customers who use shared and rentable micro-mobility e-scooter vehicles do not have problems during the travel process.

Shared e-scooters have the potential to increase access, complement transit, and replace automobiles, all while reducing emissions and congestion. However, there are concerns worldwide over the mode’s safety issues and risks. In this paper, we explore both the motivations and barriers to using e-scooters. Data are collected from a stated preference survey, using a sample consisting of mostly university staff and students in Singapore. Three logit models with varying specifications of e-scooters’ speed and lane use and one’s prior experience of conflict with a personal mobility device (PMD) are estimated. Overall, the three models have a very comparable fit (adjusted R2 of about 0.55) and consistent results. The results indicate preferences for e-scooters if they are faster and off the sidewalk. However, a bad or unsafe experience with a PMD would negatively affect use to a greater degree, although it varies across individuals. Our study suggests diverting scooters off the sidewalk and increasing the speed may not always be effective in encouraging behavioral shifts toward this alternative mode. Other solutions such as improving the services and enhancing traffic safety should be explored and considered instead.

The growing popularity of traveling with electric scooters in recent years is, on the one hand, a great opportunity to shift from the current forms of mobility to a new service among the public, but it is also a huge challenge for urban centers and operators. One of the main challenges related to electric scooters is the growing demand for high energy consumption, which is currently a big issue due to the growing interest in electromobility used in mobility as a service (MaaS) systems. However, in order to properly estimate the level of energy consumption of scooters, there is a real need for reliable scientific information on the actual electricity consumption of electric scooters used in shared mobility systems. An extensive literature analysis indicates the lack of research conducted in this area. Noting this research gap, studies were conducted to determine the factors influencing the energy consumption of an electric scooter. The study attempts to determine these values on the basis of the proposed research experiment. The scientific experiment was based on a three-factor plan; an experimental plan. The author conducted the research by driving and measuring electric scooters in order to compare the actual consumption with the data provided by vehicle manufacturers. The result of the research was to determine the influence of factors that influence the significant energy consumption of electric scooters from shared mobility systems. Interestingly, the results generated show that the electricity requirements for six scooters corresponded to one electric car. This proposal raises the question of whether scooters are the best form of electric mobility for the environment. In addition, the work also includes guidelines for scooter users and recommendations for shared mobility operators on the energy consumption of electric scooters.

Electric micromobility represents a sustainable mobility option for specific classes of users and distance thresholds. Had this mobility solution been integrated into a comprehensive mobility framework from the beginning, it would have expanded the coverage and accessibility of urban transit services. Instead, slow and incoherent regulation has established a contrast between enthusiastic users (who consider electric micromobility vehicles “fun” and “easy to use”) and recalcitrant public opinion (wherein electric micromobility vehicles are deemed “unsafe” and “dangerous”). Beyond the few attempts made by transport experts to assess the capability of e-scooters to become a sound mobility option (through mobility surveys, pattern analysis, fleet and routing problems), safety and infrastructure design should be developed in a consistent way in order to guarantee a balanced transport setting. With respect to this challenge, a methodology framework is proposed to address the increasing proliferation of micromobility in the context of a coherent transport system. Special attention is devoted to those aspects that have received less attention from the scientific community, namely infrastructure and safe interactions at intersections. The similarities and differences between e-scooters and bikes, chosen in this study as the representative of traditional soft mobility modes, have been taken into consideration. To support the proposed approach, tests investigating e-scooter performance and the perception of both the modes at safety-critical nodes (such as intersections) under different conditions are presented, and the methodology can be applied to a variety of urban scales. The results can be adopted by local authorities, transport companies and e-mobility providers to optimize infrastructure and increase the number and quality of available mobility options.

Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The driving behavior of e-scooter users significantly differs from other vehicles on the road, and their interactions with pedestrians are also increasing. To ensure pedestrian safety and develop an efficient traffic monitoring system, a reliable object detection system for e-scooters is required. However, existing object detectors based on IoU loss functions suffer various drawbacks when dealing with densely packed objects or inaccurate predictions. To address this problem, a new loss function, balanced-IoU (BIoU), is proposed in this article. This loss function considers the parameterized distance between the centers and the minimum and maximum edges of the bounding boxes to address the localization problem. With the help of synthetic data, a simulation experiment was carried out to analyze the bounding box regression of various losses. Extensive experiments have been carried out on a two-stage object detector, MASK_RCNN, and single-stage object detectors such as YOLOv5n6, YOLOv5x on Microsoft Common Objects in Context, SKU110k, and our custom e-scooter dataset. The proposed loss function demonstrated an increment of 3.70% at APS on the COCO dataset, 6.20% at AP55 on SKU110k, and 9.03% at AP80 of the custom e-scooter dataset.

Electric scooter sharing (e-scooter sharing) is a new urban micro-mobility service that is expected to shape individual urban mobility. The introduction of e-scooter sharing systems poses challenging questions for cities and transportation planners regarding their effects on their transportation system. This study addresses the question concerning the strategies which are applied for the introduction of e-scooter sharing systems in different operation areas in Germany. An interview study with 21 stakeholders with different backgrounds (local transport authorities, public transport providers, e-scooter sharing operators, municipalities, associations, planning offices and consulting companies, and other mobility providers) was conducted to reflect upon the introduction of e-scooter sharing systems in Germany and stakeholders’ involvement in planning. The qualitative content analysis provides insights into the stakeholders’ assessment of the introduction process and thus contributes to a multi-perspective understanding on the topic. Derived hypotheses and recommendations further contribute to knowledge sharing and learning from experience. The paper concludes with a description of three introduction styles: protective, pro-active, and laissez-faire.