Doctor of Philosophy / Electric scooters, or e-scooters, have become an increasingly popular form of transportation over the recent years. However, there have been numerous reports of safety concerns, crashes, and injuries for e-scooter riders and other road users as a result of e-scooter misuse. Until recently, very little formal research has been conducted on the safety of this micromobility solution. This dissertation describes a series of studies that have investigated the contributing factors to safety concerns and identified countermeasures, such as policy recommendations, design optimization, and training, that can be implemented with an end goal of improving e-scooter safety.

Micro-mobility use, such as electric scooters (e-scooters), offers convenience and environmental benefits (Christoforou et al., 2021; Vestri, 2021) and it has increased over the last five years following the introduction of shared e-scooter schemes in the United States in 2017 (Christoforou et al., 2021 ). Following the introduction of shared e-scooters there has been an observed increase in the number of people choosing to use personal devices (Haworth et al., 2021). E-scooters are typically used more for transport (Sanders et al., 2020), often replacing active travel modes than motor vehicle use (Sanders et al., 2020) although that is location-dependent (Wang et al., 2022). The use of shared and personal e-scooters is primarily associated with travel time and money savings, as well as the enjoyability of the transport mode (Christoforou et al., 2021 ). Perceived lack of safety has been shown to influence consumer acceptance (Kopplin et al., 2021). E-scooter riders have been shown to be at risk of trauma to the head and extremities (Bauer et al., 2020), although little is known about the events leading to trauma (e.g., fall as a result of rough terrain, collision with a vehicle). Protective equipment can reduce the risk of incidents (e.g., improving visibility of vulnerable road users) or lessen the risk of injury (e.g., helmets). Generally, little is known regarding the use of helmets and other protective equipment by e-scooter riders, except when injuries occur. Trauma studies have reported low ( 4.4%; Trivedi et al., 2019) to moderate (46%; Mitchell et al., 2019) use of helmets. While the majority of e-scooter presentations occur during evenings (Vemon et al., 2020), little is known about the use of reflective equipment by scooter riders. The aim of this paper is to explore factors that influence the use of protective equipment, including helmets and reflective equipment, by e-scooter riders. [From: Introduction]

The rise of micromobility, most notably electric standing scooters (e-scooters), has resulted in new challenges for traffic planning and road safety. One such issue is the fact that in most European countries, e-scooter users are obliged to ride their vehicle on cycling infrastructure and thereby share this infrastructure with bicyclists. This increases the use of and, subsequently, demand for bicycle lanes, which is an obvious challenge for transport planning. However, for adequate planning and construction of cycling infrastructure, information on route choice behavior of bicyclists and e-scooter users and its influencing factors is necessary. While research on bicyclists' route choice is well advanced, research on e-scooter riders is still in its infancy. For bicyclists, the presence of bicycle facilities, traffic volume, and travel time are among others particularly important for route choice. However, the question arises whether this also applies to e-scooter riders as vehicle dynamics are different and riders are, at least for now, less skilled due to lack of training and exposition. In order to fill this research gap, we aimed to analyze the determinants for route choice of e-scooter users in comparison to bicyclists in a field study. [from Introduction]

This dissertation conducted an extensive examination of dockless e-scooter dynamics using high-resolution trip data from Austin, Texas. Four studies were conducted to capture the multifaceted nature of e-scooter operations and demand. The first study aimed to identify and quantify the influence of contributing factors affecting e-scooter demand by partitioning the data by time period for weekdays and weekends. Utilizing a joint panel linear regression (JPLR) model, significant associations were observed between e-scooter demand and variables such as sociodemographic attributes, transportation infrastructure, land use, meteorological attributes, and situational factors. The second study shifted focus to shared e-scooter origin-destination (OD) flows in the urban region. By employing a joint binary logit-fractional split model, e-scooter OD flows were analyzed, emphasizing variations across distinct time periods and the subsequent implications for e-scooter deployment and rebalancing strategies. The third study delved into e-scooter utilization efficiency, introducing a time-to-book (TtB) measure. Through a Mixed Grouped Ordered Logit (MGOL) model, the study highlighted variations between regular and peak weeks, offering operators a chance to enhance fleet utilization. The final study addressed the broader context of the e-scooter industry, investigating the impact of the COVID-19 pandemic. By analyzing datasets spanning January 2019 through December 2021, a spatial approach illuminated changes in e-scooter demand patterns before, during, and after the pandemic, highlighting the effects of COVID-19-related factors and vaccine attributes on e-scooter trends. These collective insights from the four studies provide valuable contributions to understanding and enhancing e-scooter operations in urban landscapes

This master´s thesis deals with a design of an electric stroller. This alternative means of transport for a parent and a child was designed based on a design and technical analysis. This product allows independent and fast movement in the city. Using the product requires an introduction of a new arrangement of streets, with designated communications for micromobility. Modern technologies are used in the concept of the designed product. The advantage of the design concept is that the product can be used as a classic electric scooter.

The global micromobility market is a fast growing market valued at USD 40.19 Billion in 2020. As the market grows, it is of great importance for companies to gain market shares in order to stay competitive and be the first choice within micromobility services. This can be achieved by, e.g., offering a safe micromobility service, for both riders and other road users. With state-of-the-art technology, accident prevention and preventing misuse of scooters and cities’ infrastructure is achievable. This study is conducted in collaboration with Voi Technology, a Swedish micromobility company that is committed to eliminate all serious injuries and fatalities in their value chain by 2030. Given such an ambition, the aim of the thesis is to evaluate the possibility of using unsupervised machine learning for anomaly detection with sensor data, to distinguish abnormal and normal riding behaviours. The study evaluates two machine learning algorithms; isolation forest and artificial neural networks, namely autoencoders. Beyond assessing the models ability to detect abnormal riding behaviours in general, they are evaluated based on their ability to find certain behaviours. By simulating different abnormal riding behaviours, model evaluation can be performed. The data preparation performed for the models include transforming the time series data into non-overlapping windows of a specific size containing descriptive statistics. The result obtained shows that finding a one-size-fits all type of anomaly detection model did not work as desired for either the isolation forest or the autoencoder. Further, the result indicate that one of the abnormal riding behaviours appears to be easier to distinguish, which motivates evaluating models created with the aim of distinguishing that specific behaviour. Hence, a simple moving average is also implemented to explore the performance of a very basic forecasting method. For this method, a similar data transformation as previously described is not performed as it utilises a sliding window of specific size, which is run on a single feature corresponding to an entire scooter ride. The result show that it is possible to isolate one type of abnormal riding behaviour using the autoencoder model. Additionally, the simple moving average model can also be utilised to detect the behaviour in question. Out of the two models, it is recommended to deploy a simple moving average due to its simplicity. / Den globala mikromobilitetsmarknaden är en snabbt växande marknad som år 2020 värderades till 40,19 miljarder USD. I takt med att marknaden växer så ökar också kraven bland företag att erbjuda produkter och tjänster av hög kvalitet, för att  erhålla en stark position på marknaden, vara konkurrenskraftiga och förbli ett förstahandsval hos sina kunder. Detta kan uppnås genom att bland annat erbjuda mikromobilitetstjänster som är säkra, för både föraren och andra trafikanter. Med hjälp av den senaste tekniken kan olyckor förebyggas och skadligt bruk av skotrar och städers infrastruktur förhindras. Följande studie utförs i samarbete med Voi Technology, ett svenskt mikromobilitetsföretag som har åtagit sig ansvaret att eliminera samtliga allvarliga skador och dödsfall i deras värdekedja till och med år 2030. I linje med en sådan ambition, är syftet med avhandlingen att utvärdera möjligheten att använda oövervakad maskininlärning för anomalidetektering bland sensordata, för att särskilja onormala och normala körbeteenden. Studien utvärderar två maskininlärningsalgoritmer; isolation forest och artificiella neurala nätverk, mer specifikt autoencoders. Utöver att bedöma modellernas förmåga att upptäcka onormala körbeteenden i allmänhet, utvärderas modellerna utifrån deras förmåga att hitta särskilda körbeteenden. Genom att simulera olika onormala körbeteenden kan modellerna evalueras. Dataförberedelsen som utförs för modellerna inkluderar omvandling av den råa tidsseriedatan till icke överlappande fönster av specifik storlek, bestående av beskrivande statistik. Det erhållna resultatet visar att varken isolation forest eller autoencodern presterar som förväntat samt att önskan om att hitta en generell modell som klarar av att detektera anomalier av olika karaktär inte verkar uppfyllas. Vidare indikerar resultatet på att ett visst onormalt körbeteende verkar enklare att särskilja än resterande, vilket motiverar att utvärdera modeller skapade i syfte att detektera det specifika beteendet. Följaktligen implementeras därför ett glidande medelvärde för att utforska prestandan hos en mycket grundläggande prediktionsmetod. För denna metod utförs inte den tidigare nämnda datatransformationen eftersom metoden använder ett glidande medelvärde som appliceras på en variabel tillhörande en fullständig åktur.  Följande analys visar att autoencoder modellen klarar av att urskilja denna typ av onormalt körbeteende. Resultatet visar även att ett glidande medelvärde klarar av att detektera körbeteendet i fråga. Av de två modellerna rekommenderas en implementering av ett glidande medelvärdet på grund av dess enkelhet.

This project seeks to demonstrate routing optimization that would allow pedestrian and micromobility user groups to select and prioritize different route features according to their preferences. Through the creation of a routing demonstrator that considers both static and dynamic parameters in the form of pavement quality, elevation climb, travel time, and air quality, along with user-specified weights for their prioritization of each of these parameters, a number of routes were created and mapped to qualitatively compare against routes representing only a shortest path. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

With rapidly increasing e-scooter usage in the United States [1], a growing number of studies aim to understand the safety aspect of these emerging modes. The existing literature has a limited understanding of time-of-day and seasonal patterns of e-scooter crashes. While many e-scooter safety policies are based on the number of crashes [2, 3], accounting for exposure provides a measure of risk to inform effective preventive strategies [4]. This study focuses on motor-vehicle involved crashes since they constitute the most severe and fatal injuries. We compared daytime and nighttime motor-vehicle involved e-scooter crashes and combined them with micromobility trip data to generate exposure variables and estimate crash risk. The key research question of this paper is as follows: 1. Are crashes or crash rates disproportionately higher at night than in the day? [From: Introduction]

Mobility has changed in recent years in cities worldwide, th.anks to tb.e strong rise in vehicles of micromobility. Bicycle riding is the most widespread micromobility transport mode, followed by stand-up electric scooters (e-scooters). This increase in its use has also led to an increase in related crashes. Both cyclists and e-scooter riders are vulnerable road users and are lik.ely to sustain severe injuries in crashes, especially with motor vehicles. The crashes consequences involving cyclists and other micromobility users have already investigated using numerical simulation software, such as MADYMO and PC-Crash. Most of them have been focused on bicycles and electric bicycles, whereas only few of tbem have analyzed e-scooter crashes consequences. Posirisuk: et al. [1] carried out a computational prediction ofhead-ground impact k:inematics :in e-scooter falls. Ptak et al. [2] analyzed the e-scooter user kinematics after a crash against SUV when the e-scooter chives into the sidefront of tbe vehicle, a side B-pillar crash and a frontal impact initiated by tbe e-scooter to tbe front-end of the vehicle. However, they did not study the consequ.ences of a car to e-scooter side impact crashes. Xu et al. [3] did study these crashes but considering electric self-balancing scooters that are less widespread than e-scooters. Current study focuses on the consequences of a car to micromobility user (cyclist and e-scooter rider) side impact crashes. The analysis is based on numerical simulations with PC-Crash software.

Micromobility has a direct impact on the urban area, since it tries to make cities more liveable, o:ffering an alternative transport option that contributes to reduce air and noise pollution. Additionally, it promotes intennodality, promotes money savings, reduces parking space and helps to avoid road congestion in cities that have their own lanes for the use of micromobility vehicles such as bicycles, stand-up e-scooters (escooters) and other personal mobility vehicles (PMVs). In Spain, micromobility has significantly increased in recent years, through the increase in the supply and demand for bicycles and other PMVs, mainly e-scooters. There are many reasons that have motivated users to prioritize the bicycle and the other PMVs over other means of tra.nsport. In addition to the growing concern for health and the environment, the COVID-19 pandemic has also driven the growth in the use ofthe different PMVs in 2020. Accordmg to data from Global Public Transport Report, published by the mobility application Moovit, 31 % of Spanianis have used bicycles, scooters or e-scooters in 2020, increasing their use by 7% since 2019. However, in parallel and because of the increase in PMVs exposure, the number of crashes involving users of these vehicles has also increased in recent years. For this reason, among road safety researchers, interest and concern for the study of this kind of crashes have also increased The aim of this research is to characterize the crashes in Spain in which at least one PMV (bicycle, e-scooter or other PMV) is involved between the years 2016 and 2020.