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Burn injuries related to lithium-ion batteries from personal mobility devices, such as electric bikes and electric scooters, have emerged as a global concern. By examining the literature, this study aims to provide an overview of the demographics, patterns, and outcomes of personal mobility device-associated burns. A Singaporean cohort revealed burns resulting predominantly from fires occurring due to the combustion of unattended personal mobility device batteries during charging. In contrast, an Israeli cohort showed a higher total body surface area and highlighted the vulnerability of limbs to burn injuries in such incidents. A Beijing cohort, consisting of pediatric patients indicated potential child safety concerns regarding personal mobility device usage. Finally, a Shanghai cohort demonstrated the potential dangers of personal mobility device battery chargers. The observed differences between those experiencing burn injuries and the broader population of personal mobility device riders in terms of age and gender suggest that younger males may be at higher risk, perhaps due to risky practices such as leaving personal mobility devices unattended while charging. This review emphasizes the need for safety education, adherence to regulations, and responsible consumer choices to mitigate burn injuries. Recommendations include promoting child safety measures, using certified personal mobility devices, and cautious handling of DIY conversion kits. Further large-scale studies are essential to gain comprehensive insights and develop effective safety strategies to protect personal mobility device riders from burn injuries. [ABSTRACT FROM AUTHOR]

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor's degree, and 92% possess a driver's license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility. [ABSTRACT FROM AUTHOR]

The remarkable impact that e-scooters have had on the transportation system drives research on this phenomenon. The widespread use of e-scooters also poses several new safety issues, which should be necessarily studied. The aim of this paper points in this direction, investigating the main contributing factors, causes, and patterns of recorded e-scooter crashes, considering also different crash types and severity, using the City of Bari (Italy) as a case study. The crash dataset based on police reports and referring to the period July 2020–November 2022 (i.e., the first period of e-scooter implementation in the City of Bari) was investigated. Crashes were clustered according to several variables. No fatal crashes occurred, even though crashes mostly resulted in injuries (70%). Considering road type, divided roads were found to be less safe than undivided ones, due to higher mean speeds than on other roads and to a less constrained e-scooter driving behavior. Calm (off-peak) daytime hours seem to lead to more frequent e-scooter crashes with respect to both peak and nighttime hours, even if the latter hours are associated with an increased severity. Once controlled for exposure, season, lighting conditions, and the private/sharing ratio do not seem influential. E-scooters are more prone to be involved in single-vehicle and pedestrian crashes at segments than other vehicles, but they show similar crash trends than other vehicles (i.e., angle crashes) at intersections. As emerged from traffic surveys, not all e-scooter users were found to use cycle paths. Combining this information with crash data, it seems that not using cycle paths is considerably less safe than using them. Besides engineering measures and policies, awareness campaigns should be promoted to elicit safe users' behavior and to tackle the several violations and misbehaviors emerging from the crash data. [ABSTRACT FROM AUTHOR]

Urban mobility has undergone a transformation with the advent of micromobility vehicles, leading to a multitude of studies investigating the factors that drive early adoption and the sustainability and equity implications. However, in a context where local administrations struggle regarding how to fit different micromobility systems within the urban ecosystem, little is understood about how political regulations impact micromobility users' modal choices This study aims to shed light on the differences between private e-scooters and shared bikes in Barcelona, a city where micromobility options face distinct regulatory frameworks, and also to understand the factors that influence an individual's choice between these two modes of micromobility. The study employs a self-reported intercept survey on 651 micromobility users and builds a logistic binary regression model to examine the characteristics that differentiate e-scooter and shared bike adopters. Results indicate notable differences between adopters of the two modes and suggest that city regulations might play a role in determining the choice of mode. Furthermore, the study finds that while both modes follow similar mode replacement paths, shared bikes have a higher potential to keep users away from cars. These findings contribute to the limited knowledge on the choice between different modes of micromobility and highlight the impact of policy design on diverse population groups. As more cities are banning free-floating from city centres, it is essential to understand how these selective bans and restrictive policies have an impact on micromobility users' modal choices. • Gender and age gap are confirmed to exist in emerging mobility services usage. • Socioeconomic differences might be partially explained by the city restrictive regulatory framework. • Shared bikes might have a higher potential than private e-scooters to keep users away from the car in the long term. • Environmental and social sustainability of micromobility modes is questioned. [ABSTRACT FROM AUTHOR]

Urban mobility is a current and hot topic for local administrations, and not only due to the numerous road accidents in urban areas, often caused by the incorrect behaviour of scooter drivers. The diffusion of electric scooters occurred, above all, after the pandemic. While on the one hand, social distancing has forced many to re-evaluate traveling by car, there are many citizens who have adopted innovations such as pedal-assisted bicycles and, above all, electric scooters - the latter being the protagonists of a real boom. In addition to ensuring fast travel and ease of use, the electric scooter and its use have sparked public debate regarding safety and apparent deregulation. Very often in Italy and beyond, the use of electric scooters is associated with a perception of fear and scepticism about micro-mobility, as well as the diffusion of some models relating to sustainable mobility. This derives in part from the previous characteristics of the vehicles produced and manufactured in China that do not comply with the law, and in that case, are truly dangerous. But given the recent rules at both the Italian and European levels, we are very often protected against products that do not comply with the laws, and therefore, we have greater road safety. The recent legislation on use on Italian roads has changed the characteristics of users, vehicles, and infrastructures in which to circulate. The safety of the electric scooter has been a long-debated topic, especially since this vehicle has seen a boom in sales and rentals since 2020, thanks to the incentives for the purchase and the willingness of Italians to use public transport less and less. To define the choice of an electric scooter as a sustainable modal choice, it is necessary, beyond safety, to investigate the perception of users in terms of gender equity, use in the first and last mile, and perception in terms of green transport modes. Starting from this, a survey was implemented, and subsequently, a descriptive statistical analysis was conducted on about 550 users in the metropolis of Palermo in Sicily. The results related to the period 2019-2022 showed that the electric scooter is certainly a practical and green vehicle but must be driven with the same attention we pay in cars or motorcycles. The chi-square analysis defined the trend variations connected to the users' psychosocial parameters according to the user's gender and age. The scenario shows the need for timely interventions, above all to protect vehicle users but also pedestrians who are often involved in accidents due to the use of vehicles on sidewalks. The survey shows that scooter users are the first to request more safety, starting with the creation of protected lanes such as cycle paths or dedicated areas. [ABSTRACT FROM AUTHOR]

Previous literature has shown mixed findings on whether bike or e-scooter share programs, collectively called shared micromobility, play a role in reducing vehicle travel. In this study, we focused on two types of shared micromobility (i.e., bikes—both regular and e-bikes—and escooters). We examined their impact on vehicle travel in urbanized areas in the United States between 2012 and 2019. Employing the difference-in-differences (DID) approach, we quantified the collective effect of shared micromobility on daily vehicle miles of travel (VMT) per capita at the urbanized area level. Our findings suggest that bike shares could reduce VMT while e-scooter share alone might not have a significant impact on lowering vehicle travel. Our results also indicate that there may be a synergistic effect of bike and scooter shares in VMT reduction when both types of shared micromobility are available in an urbanized area. Our findings also demonstrate regional variations in the impact of shared micromobility on vehicle travel. [ABSTRACT FROM AUTHOR]

Electric micromobility (e‐micromobility) offers the potential to enhance the sustainability of first‐ and last‐mile journeys in urban areas by reducing the number of private vehicle trips. As a new mode of transport, it is imperative that it is not subject to the same male bias that has been evidenced across our existing transport networks. An in‐depth qualitative study was conducted with 24 UK participants (12 females) to assess the gender factors that relate to the incentives and barriers of e‐micromobility (electric bike and electric scooter) use. Focus groups and interviews were conducted and the data analysis was disaggregated by gender to reveal the differences and similarities between female and male perspectives on e‐micromobility use. Differences in the types of trips made and perceptions of fear were prevalent. Key gender‐related findings and recommendations are made. By reviewing, and acting upon, the different perspectives that males and females have towards e‐micromobility they can be made more inclusive for all. This can enhance their uptake and reduce the dependence on private vehicles. [ABSTRACT FROM AUTHOR]

Analysing user behaviour and thus travel mode choice is an important task in transport planning and policy-making in order to understand and predict travel demand. Recent pandemic events have challenged the modal choices of European users by reducing the use of public transport at various times and favouring walking and/or the use of electric bikes and scooters for last-mile travel. A number of studies have focused on analysing how the pandemic affected workers' choice of transport mode, with particular reference to local public transport, by developing multinomial logistic regression and artificial neural network models to analyse travellers' choice of transport mode before and after COVID-19. Particularly in non-European contexts, studies have been conducted on the relationship between socio-economic factors and the duration of e-scooter trips before and during the health crisis caused by the pandemic. in these contexts, a general increase in the duration of escooter trips after the pandemic was shown. Few studies, however, have analysed the European context. Several factors relating to services and infrastructure as well as sociodemographic components contributed to the propensity to use e-scooters as evidenced by a number of literature works in the European context. However, little research has been conducted using the machine learning approach to understand which factors and how they may influence modal choices. The present research work focused on the analysis of last-mile transport choices by investigating the propensity of a sample of users residing in Sicily during different time phases before and after the COVID-19 pandemic. In this study, 35 different classes were determined for a total of 545 data. The classification process was carried out using SMO, KNN and RF machine learning algorithms. The results showed a reduction in the frequency of e-scooter use during the health crisis caused by the pandemic. The results showed that this was a temporary behaviour, even though the purpose of e-scooter use by most individuals changed during the health crisis caused by the pandemic. However, it was observed that the frequency of e-scooter use decreased in most individuals during the health crisis caused by the pandemic and this became a permanent behaviour. The results suggest that the analysis of the importance of variables in relation to different periods and is essential for a better understanding and effective modelling of people's travel behaviour and for improving the attractiveness of these means of transport for companies operating services in the areas examined. [ABSTRACT FROM AUTHOR]

• E-scooters represent a new transport modality that is smaller in size than an average motorcycle, and one with riding patterns that can be unpredictable. • All shared e-scooter companies offer solely narrow-base e-scooters that force riders to use the foot behind foot riding position. • Foot behind foot riding position is associated with higher rates of orthopaedic fractures. Background: The popularity of electric scooters (e-scooters) and the shared e-scooter services as new means of transportation worldwide led to high numbers of injuries requiring emergency department (ED) visits. Private and rental e-scooters have different sizes and capabilities, offering several possible riding positions. While the rising use of e-scooters and their associated injuries have been reported, little is known about the influence of riding position on injury characteristics. The purpose of this study was to characterize e-scooter riding positions and their related injuries. Methods: E-scooters related ED admissions were retrospectively collected between June 2020 and October 2020, in a level-I trauma center. Demographics, ED presentation data, injury information, e-scooter design, and clinical course were collected and compared according to e-scooter riding position ("foot-behind-foot" vs "side-by-side"). Results: During the study period, 158 patients were admitted to the ED with e-scooter related injuries. The majority of riders used the foot-behind-foot position (n = 112, 71.3 %) compared to the side-by-side position (n = 45, 28.7 %). Orthopedic fractures were the most common injuries (n = 78, 49.7 %). "Foot-behind-foot" group had a significantly higher fracture rate compared to "side-by-side" group (54.4 % vs 37.8 % within group, respectively; p = 0.03). Conclusions: Riding position is associated with different injury types, with orthopedic fracture rates significantly higher in the more commonly used "foot-behind-foot" riding position. Practical Application: These study findings suggest that the common e-scooter narrow-based design is considerably more dangerous, calling for further research to introduce safer e-scooter designs and update recommendations for safer riding positions. [ABSTRACT FROM AUTHOR]

Means of transport should be able to fulfil their main function safely and comfortably for travellers and drivers. The effects of vibrations on ride comfort are in the frequency range of 0.5 to 80 Hz and can be analysed using the UNE-2631 standard. This type of analysis has been conducted for several means of transport (bicycles, motorcycles, cars, trucks, etc.), but the literature on e-scooter comfort is very scarce. Existing research describes methodologies, simulation models, and a few measurements related to e-scooter comfort. This paper presents, for the first time, a comfort analysis using an Arduino-based data acquisition system at a sampling frequency of 200 Hz (higher than that in previous studies). Acceleration and speed measurements were obtained by sensorising an e-scooter with inflated wheels without any additional damping systems, which is one of the commonly used e-scooter types. In this study, the comfort for two different speeds (20 and 28 km/h), two types of pavements (pavers and asphalt), and two drivers with different weights was investigated. The results indicate the lowest comfort values for higher velocities and paver pavement. Furthermore, the comfort values were extremely low for all scenarios. In addition, the results demonstrate the necessity of using a sampling rate of at least 80 Hz for this e-scooter model. [ABSTRACT FROM AUTHOR]

Shared micromobility in the U.S. has rebound after the decline caused by the COVID-19 pandemic, with a substantial increase in the adoption of shared e-bikes nationwide. However, research on hybrid e-bike sharing, which combines station-based and dockless systems, is limited. This study addresses this gap by comparing spatial determinants of hybrid e-bike and dockless e-scooter sharing link flows in 32,965 street segments in Portland, Oregon during 2022, using gradient boosting decision tree (GBDT) models. Distance to the city center emerges as the most important determinant for both modes, with closer proximity to the city center associated with higher link flows. Factors such as the presence and types of bike facilities, the availability of streetlights and street trees, and job density also significantly influence e-bike and e-scooter link flows. A notable difference between the two modes is that e-scooter trips are more sensitive to distance to the city center than e-bike trips. Furthermore, bike facilities have a greater impact on e-bike link flows, whereas job density is more influential in determining e-scooter link flows. These findings offer strategies for policymakers and urban planners to promote and manage shared micromobility and optimize the built environment. These strategies include enforcing higher device availability requirements in underprivileged neighborhoods, transitioning e-scooter sharing systems into a hybrid model, expanding the off-street bike trial network and bikeway network, and augmenting the coverage of streetlights and street trees along the bikeway network. • Distance to the city center is the most important determinant for both modes • Bike facilities, streetlights, street trees, and job density are also key factors. • E-scooter trips are more spatially concentrated than e-bike trips. • Bike facilities are more crucial for e-bike than e-scooter link flows. • Job density is more crucial for e-scooter than e-bike link flows. [ABSTRACT FROM AUTHOR]

(1) Background: Electric micro-mobility vehicles (i.e., e-bikes and e-scooters) represent a fast-growing portion of the circulating fleet, leading to a multiplication of accident cases also attributable to risky behaviours adopted by the riders. Still, data on vehicle performance and rider behaviour are sparse and difficult to interpret (if not unavailable). Information regarding the overall accident dynamics can, however, aid in identifying users' risky riding behaviour that actually led to a harmful event, allowing one to propose efficient strategies and policies to reduce the occurrence of road criticalities. (2) Methods: Speed and acceleration data of six cyclists of traditional and electric bikes were extracted from six closed-circuit experiments and real road tests performed in the city of Florence (Italy) to derive their behavioural patterns in diverse road contexts. (3) Results: The application of analysis of variance and linear regression procedures to such data highlights differences between men and women in terms of performance/behaviour in standing start; additionally, the use of e-bikes favours a higher speed ride in correspondence to roundabouts and roads with/without the right of way. To thoroughly assess the rider's responsibilities in an eventual accident, an ancillary procedure was highlighted to evaluate whether a micro-mobility vehicle complies with the applicable regulations. (4) Conclusion: With these results, the prospective recognition of rider behaviour was facilitated during the investigation process, and the abilities to extract such relevant information from in-depth accident data wereconsequently enhanced. [ABSTRACT FROM AUTHOR]

Objective: E-scooter use has grown rapidly in the United States. Its rise in popularity has coincided with the promotion of cycling in many cities, but more needs to be known about how these transportation modes compare to determine if cycling should serve as an appropriate benchmark for policy decisions and safety expectations regarding e-scooters.Methods: We examined characteristics of adults seeking treatment in a Washington, DC, emergency department (ED) for injuries associated with riding e-scooters during 2019 (n = 99) or bicycles during 2015-2017 (n = 337).Results: E-scooter incidents less frequently involved moving vehicles (13.1% vs. 37.7%) or occurred on roads (24.5% vs. 50.7%) than cycling incidents. A smaller proportion of injured e-scooter riders were ages 30-49 (32.3% vs. 48.4%) and a larger proportion were 50 and older (34.3% vs. 22.6%) or female (45.5% vs. 29.1%). Distal lower extremity injuries were more common among e-scooter riders (13.1% vs. 3.0%; RR, 2.76; 95% CI, 1.79-3.54), and injuries to the proximal upper extremity (9.1% vs. 20.5%; RR, 0.49; 95% CI, 0.24-0.92) or chest, abdomen, and spine (3.0% vs. 14.0%; RR, 0.24; 95% CI, 0.07-0.70) were less common. Head injury rates were similar, but e-scooter riders more often experienced concussion with loss of consciousness (4.0% vs. 0.6%; RR, 3.03; 95% CI, 1.20-4.09) and were far less likely to wear helmets (2.0% vs. 66.4%). Estimated ED presentation rates per million miles traveled citywide were higher among e-scooter riders than cyclists (RR, 3.76; 95% CI, 3.08-4.59).Conclusions: E-scooters and bicycles are both popular forms of micromobility, but the characteristics of riders injured on them, the ways in which they become injured, and the types of injuries they sustain differ substantially. E-scooter rider injury rates, though currently high, may decrease as they gain experience; however, if the number of new users continues to climb, they will persist in using the ED more often than cyclists per mile that they travel. [ABSTRACT FROM AUTHOR]

When considering the sharp growth rate of the use of e-micromobility vehicles, such as e-scooters and e-bikes, it is necessary to investigate whether these emerging modes of transport play a sustainable role in cities in terms of their energy efficiency, emissions, and their relationship with other modes of mobility, such as public transport. This paper aims to provide a comprehensive overview of the impacts of e-micromobility through a systematic review of relevant studies in the field of e-scooters and e-bikes. We followed the steps of PRISMA to conduct a systematic literature review, including identification, screening, eligibility and inclusion steps. One hundred forty-six studies were reviewed and compiled, and 29 of these studies were selected for the focus of this review and their research data were synthesized. The impacts of e-micromobilities were categorized into four categories—travel behaviors, energy consumption, environmental impacts, and safety and related regulations. The category of travel behaviors includes the analysis of the purposes of travel, modal shift from different modes of transport to e-micromobility vehicles, average travel time, and distance. In this review, the findings of relevant studies in different cities around world are compared to each other and synthesized to give an insight into the role of e-micromobility in the present and in the future of urban transportation. [ABSTRACT FROM AUTHOR]

• We compare car-involved scooter crashes and bicycle crashes using standard typology. • Scooter-crashes and bicycle-crashes share many common features. • Most crashes occur at intersections or driveways. • Most scooter and bicycle riders are local residents nearby home. • Scooter riders are more often tourist. • Most scooter crashes occur on the transition between the sidewalk and the roadway. Introduction: The market share of e-scooters in the United States has proliferated in cities: 86 million trips were made on shared e-scooters in 2019, a more than 100% increase compared to 2018. However, the interaction of e-scooters with other road users and infrastructure remains uncertain. Method: This study scrutinized 52 e-scooter and 79 bicycle police-reported crashes in Nashville, Tennessee, from April 2018 to April 2020 from the Tennessee Integrated Traffic Analysis Network (TITAN) database. We used descriptive analysis and a recent prototype version of the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to classify crashes based on the locations of the crashes relative to roadway segments or intersections, as well as the maneuver of the motor vehicle and e-scooter/bicycle relative to the motor vehicle. Results: Two crash typologies can explain the majority of e-scooter crashes, while bicycle crashes are distributed over several crash typologies. Additionally, 1 in 10 e-scooter- and bicycle-motor vehicle crashes leads to the injury or fatality of the e-scooter rider or bicyclist. Furthermore, we noted statistically significant differences in spatial and temporal distribution, demographics, lighting conditions, and crash distance from home for e-scooter and bicycle crashes. Conclusions: The police crash report provides a comprehensive picture of e-scooter safety complementing existing literature. We found that e-scooter crash characteristics do not fully overlap with features of bicycle crashes. Practical Implications: A generalized engineering, education, and enforcement treatment to reduce and prevent e-scooter and bicycle crashes, injuries, and fatalities might not result in equal outcomes for each mode. More rigorous enforcement could be implemented to deter e-scooters riders under the age of 18 years and e-scooter safety campaigns could target female riders. [ABSTRACT FROM AUTHOR]

This study compares the equality and equity of four shared micromobility models, including station-based bikesharing, hybrid e-bike sharing, hybrid e-scooter sharing, and dockless e-scooter sharing, with a case study in Minneapolis, Minnesota. Equality assessment is made using Lorenz curves and Gini coefficients, which reveals that hybrid and dockless models are relatively more equal than the station-based model. Equity assessment is conducted using regression analysis. Results on supply equity show that station-based and hybrid models are more equitable than the dockless model, as they allocate more devices to economically disadvantaged areas. Regarding utilization equity, regression models yield mixed results: depending on the disadvantaged indicators used to identify disadvantaged communities, both the station-based and hybrid/dockless models can exhibit characteristics of equity or inequity. Overall, our findings suggest that the hybrid model shows the most promising potential for improving both the equality and equity of the spatial distribution of shared micromobility services. [ABSTRACT FROM AUTHOR]

The primary objective of the study was to identify the factors that influenced the comfort perception of e-bike, e-scooter and bicycle riders in mid-block bicycle lanes on urban streets and to estimate the bicycle level of service (BLOS) of a mid-block bicycle lane with mixed two-wheeled traffic. Data were collected at thirty locations on thirty different streets in Nanjing area in China. Pearson’s Chi-square tests were conducted to make comparisons of the comfort perception among different cyclist groups. The factors that significantly affected the comfort perception of the cyclists included the age of the cyclists, the type of two-wheeled vehicles, the volume of two-wheeled vehicles, the width of mid-block bicycle lanes, the proportions of e-bikes and e-scooters in two-wheeled vehicles, the physical separation between motorized, bicycle and pedestrian lanes, the slope of bicycle lanes, the roadside access points and the roadside land use. Ordered probit models were developed to quantitatively evaluate the impacts of different contributing factors on the comfort perception of the riders of e-bikes, e-scooters and bicycles. The results showed that compared to the riders of bicycles, the riders of e-bikes and e-scooters were more likely to perceive a poor comfort level. The comfort perception of the cyclists increased with an increase in the width of the mid-block bicycle lane, whereas it decreased with an increase in the volume of two-wheeled vehicles. The proportions of e-bikes and e-scooters in two-wheeled vehicles negatively affected the comfort perception of the cyclists. In addition, the presence of physical separation between the motorized, bicycle and pedestrian lanes significantly increased the comfort perception of the cyclists. With the comfort perception models, a procedure was developed and insights were gained to help transportation professionals estimate the BLOS of a mid-block bicycle lane with mixed two-wheeled traffic. [ABSTRACT FROM AUTHOR]

Evidence shows that the use of micromobility vehicles is currently skewed towards young men, who are more likely to adopt risky behaviours, such as fast or aggressive riding. However, research on micromobility travel behaviour founded on GPS data has repeatedly failed to disaggregate findings by gender, or to account for intertwined sociodemographic identities. In this study, we investigate how bike-share cyclists and private e-scooter riders navigate through Barcelona's cycling facilities, and whether intersecting identities (in terms of gender, age and parenthood) influence their performance. Using 911 GPS-tracked trips of 89 participants, we build a multilevel linear mixed effects model that analyses the speed at which cyclists and e-scooter riders travel. The results show that the speed gender gap is particularly salient among e-scooter users, while being almost non-existent among cyclists. The model further indicates that cycling facilities influence riding speed, and that intersecting identities significantly explain how micromobility users circulate through the city. Drawing on theories of performativity and embodiment, we argue that findings pointing to gender differences in travel behaviour might be explained by women's unwillingness to negotiate traffic or tolerate harassment in highly masculinised spaces. These findings contribute to the limited available knowledge on the objective travel behaviour of micromobility users from a gender and intersectional perspective, i.e. recognising the complex way in which multiple forms of discrimination associated with identity combine. We also provide valuable insights into how the design of urban environments and targeted policies can have diverse effects on different micromobility users. • The travel behaviour of Barcelona's micromobility users is defined by their gender, age, and child-care responsibilities. • Men riding e-scooters travel faster than women, whereas the speed gender gap is almost non-existent among bike-share cyclists. • Women avoid using the bike-sharing system at night or sharing the space with motorised vehicles. • Intertwined identities influence travel speed and route choice differently than the sum of isolated social attributes. [ABSTRACT FROM AUTHOR]

Shared vehicles are expected to improve urban mobility and reduce transport related emissions. Since 2017, e-scooters are deployed in sharing schemes in cities around the world. However, user behavior including replaced modes and motivations for mode change as well as impact on overall emissions is still in question. Based on a Germany wide user survey, we determine mode substitution, estimate a multinomial logit model to reveal influence of motivational aspects on shared e-scooter choice, and calculate impact on emissions. Results show that majority of shared e-scooter trips replace walking, followed by public transport and at the same rate private bikes and private cars. The calculated net emission balance is currently negative, although a path to a positive effect is likely. Reasons for car users to choose a shared e-scooter are less time spent searching for parking and avoiding congestion, but also sustainable behavior was a significant impact factor reported. [ABSTRACT FROM AUTHOR]

Objective:The primary objective of this study was to compare the risky behaviors of e-bike, e-scooter, and bicycle riders as they were crossing signalized intersections. Methods:Pearson's chi-square test was used to identify whether there were significant differences in the risky behaviors among e-bike, e-scooter, and bicycle riders. Binary logit models were developed to evaluate how various variables affected the behaviors of 2-wheeled vehicle riders at signalized intersections. Field data collection was conducted at 13 signalized intersections in 2 cities (Nanjing and Kunming) in China. Results:Three different types of risky behaviors were identified, including stop beyond the stop line, riding in motorized lanes, and riding against traffic. Two-wheeled vehicle riders’ gender and age and traffic conditions were significantly associated with the behaviors of 2-wheeled vehicle riders at the selected signalized intersections. Conclusions:Compared to e-bike and bicycle riders, e-scooter riders are more likely to take risky behaviors. More specifically, they are more likely to ride in motorized lanes and ride against traffic. [ABSTRACT FROM AUTHOR]

• A log–log regression model of e-scooter trips was estimated from four cities' data. • Manhattan trips were related to existing modes to quantify substitution effects. • A nonlinear factor model shows differences between access/egress and direct trips. • Insights obtained on carpool, bike, taxi and varying distance substitution rates. An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log–log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75 K daily e-scooter trips in Manhattan for a deployment of 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute based on statistical similarity. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters could replace 32% of carpool; 13% of bike; and 7.2% of taxi trips. The distance structure of revenue from access/egress trips is found to differ from that of other substituted trips. [ABSTRACT FROM AUTHOR]

• The study surveyed users and non-users in a city where e-scooters became rapidly a trend. • Shared e-scooters mostly replaced walking and public transport trips. • People travelling with bicycle or motorcycle were not attracted by e-scooters novelty. • Males are more likely to be engaged with the new mobility option. • Both users and non-users identified the lack of infrastructure as a critical aspect. Micromobility and especially e-scooter sharing have recently attracted a lot of attention, due to the rapid spreading of e-scooters in many cities around the world. However, many local authorities have not yet been prepared for efficiently integrating e-scooters in their transport systems and the exact impact of e-scooters is still unclear. It is therefore essential to understand the way e-scooters operate and their users' profile. To address these questions, a study was designed based on 578 questionnaires (271 by e-scooter users and 307 by non-users) in the city of Thessaloniki, Greece. The analysis utilized a classification tree model for identifying the characteristics of people that are attracted by e-scooters (i.e., used them more than once) and a latent variable logit model for understanding the attributes of the regular e-scooter users. The results show that shared e-scooters mostly replaced walking and public transport trips; therefore, the positive impact of e-scooters on the environment is questioned. Also, the results indicate that people traveling with bicycle or motorcycle were not at all attracted by e-scooters. Moreover, females seem to be less keen on using e-scooters compared to males, while people living downtown are more regular users compared with those living in longer distances from the city center. These findings can aid policymakers in shaping the manner with which e-scooters can be incorporated in their cities. [ABSTRACT FROM AUTHOR]

• E-scooter route choices preferences on university campus. • Revealed GPS route choice data. • Route choice modeled using recursive logit model with dynamic choice sets. • Bicycle friendly infrastructures and roads with low speed limits are preferred. E-scooter is an innovative travel mode that meets the demand of many travelers. A lack of understanding of user routing preferences makes it difficult for policymakers to adapt existing infrastructures to accommodate these emerging travel demands. This study develops an e-scooter route choice model to reveal riders' preferences for different types of transportation infrastructures, using revealed preferences data. The data were collected using Global Positioning System units installed on e-scooters operating on Virginia Tech's campus. We applied the Recursive Logit route choice model to 2000 randomly sampled e-scooter trajectories. The model results suggest e-scooter riders are willing to travel longer distances to ride in bikeways (59% longer), multi-use paths (29%), tertiary roads (15%), and one-way roads (21%). E-scooter users also prefer shorter and simpler routes. Finally, slope is not a determinant for e-scooter route choice, likely because e-scooters are powered by electricity. [ABSTRACT FROM AUTHOR]

• An integrated process-based LCA is conducted on micromobility modes in Paris. • Shared e-micromobility ranks between active modes and personal ICE modes. • E-micromobility performance is globally driven by the vehicle manufacturing. • Ownership does not directly affect the performance, vehicle lifetime mileage does. • Assessing the sole carbon footprint leads to biased environmental decision-making. The environmental performance of shared micromobility services compared to private alternatives has never been assessed using an integrated modal Life Cycle Assessment (LCA) relying on field data. Such an LCA is conducted on three shared micromobility services in Paris – bikes, second-generation e-scooters, and e-mopeds - and their private alternatives. Global warming potential, primary energy consumption, and the three endpoint damages are calculated. Sensitivity analyses on vehicle lifespan, shipping, servicing distance, and electricity mix are conducted. Electric micromobility ranks between active modes and personal ICE modes. Its impacts are globally driven by the vehicle manufacturing. Ownership does not affect directly the environmental performance: the vehicle lifetime mileage does. Assessing the sole carbon footprint leads to biased environmental decision-making, as it is not correlated to the three damages: multicriteria LCA is mandatory to preserve the planet. Finally, a major change of paradigm is needed to eco-design modern transportation policies. [ABSTRACT FROM AUTHOR]

• First comprehensive choice models for 4 different shared micromobility modes. • Generally applicable methodology for analysis at a high spatiotemporal resolution. • Largest empirical dataset to-date. • Nested mode choice (dockless, docked) with docked modes preferred for commuting. • Fundamental relationship between fleet density and usage revealed. Shared micromobility services (e-scooters, bikes, e-bikes) have rapidly gained popularity in the past few years, yet little is known about their usage. While most previous studies have analysed single modes, only few comparative studies of two modes exist and none so-far have analysed competition or mode choice at a high spatiotemporal resolution for more than two modes. To this end, we develop a generally applicable methodology to model and analyse shared micromobility competition and mode choice using widely accessible vehicle location data. We apply this methodology to estimate the first comprehensive mode choice models between four different micromobility modes using the largest and densest empirical shared micromobility dataset to-date. Our results suggest that mode choice is nested (dockless and docked) and dominated by distance and time of day. Docked modes are preferred for commuting. Hence, docking infrastructure for currently dockless modes could be vital for bolstering micromobility as an attractive alternative to private cars to tackle urban congestion during rush hours. Furthermore, our results reveal a fundamental relationship between fleet density and usage. A "plateau effect" is observed with decreasing marginal utility gains for increasing fleet densities. City authorities and service providers can leverage this quantitative relationship to develop evidence-based micromobility regulation and optimise their fleet deployment, respectively. [ABSTRACT FROM AUTHOR]