E-Scooter Safety: Issues and Solutions (2022)

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2 2. Characterize the relationship between e-scooter crashes, injuries, and fatalities and contributing factors (both behavioral and envi- ronmental); and 3. Summarize how cities are working to support, manage, and reg- ulate the use of e-scooters to prevent and mitigate injuries and provide a series of case studies highlighting real-world practices. These results are derived from the work completed during Phase I of BTSCRP Project BTS-10, “E-Scooter Safety: Issues and Solutions.” CHAPTER 2 STUDY METHODS AND DATA SOURCES Drawing from published literature and existing travel survey data, the study to date has used a mixed-methods approach to build a foun- dation of knowledge around e-scooter safety issues and themes. The research team used a web-based survey to collect additional data from key stakeholders regarding existing safety management practices and identified needs. The following sections summarize the study methods and data sources underlying the findings presented in this digest. The research team used keyword searches to identify relevant studies about standup electric scooter injuries, usage trends, and safety management practices. The databases searched were PubMed, Google Scholar, the Transport Research International Doc- umentation database, and Articles+ (via the University of North Carolina Libraries). Keywords included “electric scooter,” “e-scooter/ e scooter/escooter,” “micromobility/micro-mobility,” “injury,” “crash,” and “safety.” The researchers manually reviewed the search results and excluded items that focused only on motorcycles, mopeds, and personal mobility scooters, as these modes do not fit within this research’s definition of an e-scooter that could be personally owned or rented through a shared micromobility program. Studies published before January 1, 2017, or after October 31, 2020, were also excluded. The term “micromobility” often includes bikeshare systems in addition to e-scooter systems. Because the electric bikeshare (e-bike) body of literature is large and beyond the scope of this research, the research team excluded bikeshare-specific items. However, some recent e-bike literature was included if it specifically compared e-scooter and e-bike injuries or if it pertained to micromobility man- agement practices that may extend to e-scooters. Gray literature in the form of reports published by governments or others with experience with micromobility was included. Gray literature was identified by searching the individual websites of relevant organizations and municipalities for publications. Organiza- tions targeted in the search included • National Association of City Transportation Officials (NACTO), • Institute of Transportation Engineers (ITE), • Association of Pedestrian and Bicycle Professionals (APBP), • Governor’s Highway Safety Association (GHSA), • Law Enforcement Bicycle Association (LEBA), • American Driver and Traffic Safety Education Association (ADTSEA), • National Conference of State Legislatures (NCSL), • Association of Metropolitan Planning Organizations (AMPO), • American Association of Retired Persons (AARP), and • AAA Foundation for Traffic Safety. Additionally, the project team scanned information on e-scooter injuries and safety management practices provided by city or county governmental organizations. As there are more than 190 e-scooter markets in the United States, the team focused on gathering reports from large U.S. cities representing diverse geographic contexts. In total, the research team identified 349 studies and reports that met the inclusion criteria. Most of these were scholarly articles pub- lished in peer reviewed journals, but some were agency, municipal- ity, or academic research reports (i.e., gray literature). The research team reviewed the studies in detail and created a spreadsheet of literature on e-scooter injuries that includes study title, date, authors, publication, setting or location, population, demographics, meth- odology, data source, inclusion criteria, and key findings. The team categorized each item into the relevant task areas: injury literature (58 studies), policy or practice literature (94 studies), and behavioral and usage trend literature (56 studies). Some items pertained to multiple task areas and were therefore cross-tagged. An additional 46 studies were tagged as pertaining to e-bikes and 14 studies related to COVID-19. After review, 81 items were excluded as not relevant or out of scope for the study. CHAPTER 3 E-SCOOTER CONTEXT AND SAFETY ISSUES An understanding of e-scooter user behavior and associated safety issues requires an understanding of the context of e-scooter use, including the evolution of the devices and safety features, rider sociodemographic characteristics, trip patterns and characteristics, and perceptions of safety related to e-scooter use (both for e-scooter users and nonusers). This area of e-scooter research continues to accrete as city agencies evaluate their e-scooter pilot programs and external parties seek to address gaps in research regarding how e-scooters affect the transportation system. Although variation in survey methodologies limited direct comparisons between studies, overall trends within the body of literature are clear. This section presents findings from research related to e-scooters from at least 20 cities across the United States. These cities allowed e-scooters and often conducted formal evaluations through pilots. Many pilots lasted for several months and were then evaluated. In a few cases, local universities conducted studies that were unrelated to formal evaluations but still provide important insights into e-scooter user behavior and context. Also included are relevant results from the 18-metro Populus survey, which are presented in TCRP Research Report 230: Transit and Micromobility (Murphy et al. 2021). The findings described in this section are derived mostly from online surveys about e-scooter use and analysis of ridership data from e-scooter companies. Most surveys received at least 1,000 responses. These response numbers provide a baseline level of confidence in the results, though results are still subject to response bias. The bias may be particularly notable for surveys based on convenience sam- ples, which may systematically miss certain segments of the popu- lation, such as racial minorities, women, low-income communities, people with limited access to broadband, and other marginalized groups. Ridership surveys may also be biased toward certain demo- graphics if ridership itself is skewed. Given the consistency of survey

3 eration may also be human controlled with kicking or foot braking, respectively (SAE International 2019). In late 2018, approximately a year after the first deployment of e-scooters in the United States, it was estimated that four out of five e-scooters being used across the globe were manufactured by the parent company of Segway, Ninebot (Bergen and Brustein 2018). Two early e-scooter models, the Ninebot-Segway ES2 and Xiaomi M365, were widely utilized by the first shared e-scooter operators. These scooters were lightweight (12.5  kg/28  lbs), foldable, splash resistant, equipped with electric front brakes and mechanical rear brakes, and had a range of 25 to 30 km (15.5 to 18.6 mi). The Ninebot-Segway ES2 had 7.5- and 8-inch (19.1- and 20.3-cm) solid tires, front and rear shock absorbers, a digital display in between the handlebars indicating battery life and speed, and a battery located within the center column. In contrast, the Xiaomi M365 had 8.5-inch (21.6-cm) pneumatic tires, exposed wires, a series of LED lights between the handlebars indicating battery life, and a battery located in the foot platform (Wiles 2020). Ultimately, disputes sur- rounding alleged manufacturing defects that posed potential safety hazards (i.e., sudden ignition and braking, structural failure) led to the severing of ties between various companies and certain manu- facturers (Bergen and Brustein 2018). As more companies have taken responsibility for the engineering and design of their e-scooters, vehicle designs have become more robust and now exceed 20 kg (44 lbs) in weight (Santacreu et  al. 2020). The project team examined the current product literature of the following e-scooter companies: Bird, Lime, Bolt, Superpedestrian (operating as Link), and Veo. Who Rides E-Scooters? An understanding of who rides e-scooters can help contextualize injury patterns, provide insights into safety messaging, and highlight e-scooter barriers that need to be addressed. This section discusses findings about patterns according to gender, race and ethnicity, age, income, and riding frequency. Because this literature focused over- whelmingly on shared micromobility systems, these trends pertain only to shared micromobility users and may not reflect the trends and demographics of those using privately owned devices. Gender Early research suggested that men and women used e-scooters approximately evenly (4% and 3% of the general population, respec- tively) and that women were slightly more likely to have a positive view of e-scooters than men did (Populus 2018). However, later surveys consistently found that men were more likely to ride e-scooters, take more trips, and respond to surveys. For example, male respon- dents comprised between 56% and 81% of respondents to online e-scooter user surveys in Provo, Utah (n = 1,070), and Portland (n = 1,400), and broader community surveys about e-scooter use in Denver, Colorado (n = 2,084); Chicago, Illinois (n = 12,446); San Francisco (n unknown); Santa Monica, California (n unknown); Rosslyn, Virginia (n = 181); and Baltimore, Maryland (n = 5,283) (Glenn et al. 2020; Portland Bureau of Transportation and Alta Planning & Design 2020; Denver Public Works 2019; City of Chicago 2020a; SFMTA 2019; City of Santa Monica 2019; Baltimore City Department of Trans- portation 2019a; NACTO 2020; James et al. 2019). These surveys also consistently found that men were more likely to ride e-scooters, ranging from 65% to 76% of reported riders (Denver Public Works findings, however, several key trends are worthy of discussion and consideration; these findings are described in the remainder of this section. What Services, Devices, and Components Are E-Scooter Riders Using? E-Scooter Services and Operations E-scooter services have emerged from either new companies ded- icated solely to e-scooters, or dockless bikeshare companies shift- ing their operational domains (NACTO 2019b). Many e-scooter operators grew out of companies that began as dockless or station- based bikeshare companies with similar operations and business models. Basic shared micromobility services typically consist of a mobile app that allows riders to locate, unlock, and pay for a vehicle. Operations include citywide deployment, charging, main- tenance, and re-distribution of vehicles carried out by independent contractors or company employees (Populus 2018; Portland Bureau of Transportation 2019; Santacreu et al. 2020). Shared e-scooter users are required to play a flat fee ($1.00) to unlock vehicles, with an additional pay-per-minute charge ($0.15 to $0.39 per minute) (Populus 2018; NACTO 2020). Although the pay-per-minute method is widely utilized by shared e-scooter operators, some researchers assert that this payment method encourages high and unsafe speeds (Santacreu et al. 2020). After charging is complete, next-day deploy- ment of vehicles is frequently based on ridership trends or distribu- tion requirements mandated by the agencies (Portland Bureau of Transportation 2019). Most commonly, dedicated vans are utilized for such operations. However, a few companies have opted to use cargo bikes for recharging and rebalancing in order to offset the neg- ative impacts of vans. The use of cargo bikes has been made possible by the introduction of swappable batteries. Ongoing research seeks to quantify the comprehensive environmental impacts of e-scooters and company business models through life-cycle analyses that can illuminate if and how much motor vehicle use for e-scooter opera- tions has adversely impacted congestion, pollution, and road safety (Santacreu et al. 2020). Some cities, such as Portland, Oregon, and San Francisco, California, have either required or requested that total vehicle miles traveled for operations purposes be reported, although company compliance with these requests has varied (Portland Bureau of Transportation and Alta Planning & Design 2020; SFMTA 2019). E-Scooter Devices and Components When e-scooters first appeared on city streets in the United States, they were largely unfamiliar vehicles and lacked a formal and inter- disciplinary definition. This led to inconsistencies and problems in police crash reporting, policy making, and hospital data. In 2019, standardized classifications and terms for powered micromobility vehicles (including e-scooters) were established by the Society of Automotive Engineers (SAE) International. E-scooters are operator- controlled, motor-powered, transport no more than one individual, and are formally referred to as “powered standing scooters.” They are structurally composed of a center column with handlebar, a foot platform for the operator to stand on, and a frame holding two or three wheels in the longitudinal direction of travel. The center col- umn and handlebars are used by the operator for stability and steer- ing and are often equipped with a throttle for acceleration, braking controls, and information accessories. Scooter acceleration or decel-

4 25–34 than nonusers (Denver Public Works 2019; City of Santa Monica 2019; SFMTA 2019), a finding mirrored in surveys of e-scooter users and pedestrians in other cities (James et al. 2019, Sanders et al. 2020; City of Chicago 2020a; NACTO 2020; Murphy 2021). Income In terms of usage, most survey results indicate that e-scooter users earning middle incomes are overrepresented compared with the sociodemographics of surrounding areas. In Portland, for example, lower-income residents were less likely to ride regularly than higher- income Portlanders (Portland Bureau of Transportation and Alta Planning & Design 2020). Income in Chicago skewed higher overall and was similar for the e-scooter riders and nonriders in the city’s survey (City of Chicago 2020a). The average income of e-scooter users matched the median income of about $75,000 in the Santa Monica and Minneapolis, Minnesota, areas. In San Antonio, Texas, rider income was higher than the median income of about $55,000 (NACTO 2020). In Denver, in contrast, e-scooter users tended to have a higher concentration at both the lower and higher ends of the income spectrum as compared with nonusers in the same survey population (Denver Public Works 2019). Populus survey results did not show much variation in income between e-scooter riders and nonriders but did see slightly higher proportions of e-scooter riders at the lower- and higher-income levels, as in Denver (Murphy 2021). How Often and for What Purposes Do People Ride E-Scooters? NACTO reported that in 2018, 40% of all e-scooter trips in the United States occurred in just three cities: Los Angeles, California; Austin, Texas; and San Diego, California (NACTO 2019b). The following year (2019), NACTO reported that e-scooter ridership was greatest in Atlanta, Georgia; Austin; Dallas, Texas; Los Angeles; San Francisco; and Washington, DC. Trips in these cities accounted for 38% of all e-scooter trips in the United States (NACTO 2020). Additionally, e-scooter trips increased by 123% from 2018 to 2019, from 40 million to 86 million trips (NACTO 2019b, 2020). However, some cities, such as Portland and Atlanta, reported year-over-year decreases, although e-scooter ridership increased in Portland’s equity service areas from 2018 to 2019 (Portland Bureau of Transportation and Alta Planning & Design 2020; City of Atlanta 2020). The factors contributing to a decrease in certain places were not clear from the literature, and the COVID-19 pandemic further muddied any discernible patterns. Prior to the pandemic (i.e., in January 2020), there were 205 shared e-scooter programs in the United States; by that August, there were only 146. In 2020, 133 e-scooter systems suspended service (for at least part of the time), while only 77 remained open the entire time (Bureau of Transporta- tion Statistics 2020). Although 2020 ridership remained lower than it likely would have in the absence of the pandemic, micromobility proved to be an important mode of socially distanced transportation and recreation. Postpandemic e-scooter programs are likely to continue in many cities, given their widely recognized benefits and capacity to expand current transportation options (Portland Bureau of Trans- portation and Alta Planning & Design 2020; Baltimore City Depart- ment of Transportation 2019a). These benefits, as well as other 2019; City of Chicago 2020a; Baltimore City Department of Trans- portation 2019a; City of Santa Monica 2019; NACTO 2020), and to ride more frequently (SFMTA 2019; Sanders et al. 2020). Populus sur- vey data showed a higher representation of female e-scooter riders, at 54% (n = 1,496), than male riders, which may be a reflection of the survey method that was built for representative sampling of a broader population as opposed to the direct sampling of e-scooter riders (Murphy 2021). These trends by gender mirror trends in bicycling and are likely related to differences in perceived barriers according to gender, as discussed in a later section. Race and Ethnicity Surveys of e-scooter riders imply that users are predominantly White, although the proportion of different races and ethnicities among users varies by location. For example, 61% of survey respondents to San Francisco’s e-scooter survey were White, 16% were Asian or Pacific Islander, 7% were Hispanic/Latinx, and 2% were Black. Each group representing people of color was underrepresented by about half of their proportion of the population (SFMTA 2019). In Chicago’s online convenience survey, 76% of respondents were White, 10% were Hispanic or Latinx, 5% were Black, and 6% were Asian. The race and ethnicity of daily riders were more diverse: 56% were White, 18% were Hispanic or Latinx, 13% were Black, and 8% were Asian (City of Chicago 2020a). In Washington, DC, and Baltimore, Latinx riders reported more usage than their share of the population would suggest. In Baltimore, 75% of Latinx respondents reported using e-scooters (NACTO 2020; Young et al. 2019). The percentage of Black users in Baltimore approximated their percentage of the population (58%), but they were underrepresented among riders in Washington, DC. White users were overrepresented in Baltimore, both in terms of survey respondents and ridership (75% and 68%, respectively), but evenly represented in Washington, DC (Baltimore City Depart- ment of Transportation 2019a; NACTO 2020). Populus survey data for e-scooter users found 42% of respondents to be White—10% lower than the 52% for all survey respondents. Other races showed nearly the same proportions as White riders, with Hispanic respon- dents slightly higher for e-scooter users (26% of e-scooter respon- dents compared with 20% of all survey respondents) (Murphy 2021). The potential influence of racial and ethnic differences in survey responses and rider usage is an important gap to address in future research. For example, Portlanders of color were less likely than White Portlanders to report riding e-scooters regularly, and they were more likely to report riding for fun or because they do not have a car (Portland Bureau of Transportation and Alta Planning & Design 2020). They were also more likely to report cost as a barrier to more frequent e-scooter usage (as well as other barriers related to safety, covered in a later section), and to indicate that having more e-scooters near transit could encourage usage. Findings like these are less apparent when people of color are underrepresented in sur- vey responses. Age Younger segments of the population, particularly those aged 18–34, tend to be overrepresented among e-scooter users, although this varies by location. For example, 56% of respondents to the Provo e-scooter user survey were aged 18–24 (Glenn et al. 2020). E-scooter users in Denver and Santa Monica were much more likely to be aged

5 Several studies found that weekend days seem to have a fairly sustained period of use from 11:00 a.m. to 6:00 p.m. (Chang et al. 2019), with peak times that varied. In some cities, such as Chicago, the peak was around 3:00 p.m. to 4:00 p.m. (City of Chicago 2020a), while in others, such as Washington, DC, it was around noon (Zou et al. 2020). Use continued through the early evening in other cities, such as Portland (Portland Bureau of Transportation and Alta Planning & Design 2020). In several cities (Austin; Louisville, Kentucky; Atlanta, Denver; and Indianapolis), e-scooters were ridden more frequently and for longer distances on weekends (Denver Public Works 2019; Chang et al. 2019; City of Atlanta 2020; Mathew et al. 2019; Caspi et al. 2020). These patterns differ from bikeshare patterns and may reflect e-scooters’ novelty, lower difficulty to use (compared with bikes), the influence of specific contexts, social influences, or other unique aspects of e-scooter use. Those pilots that evaluated ridership over multiple seasons found that ridership tended to peak in the summer months and be relatively low in the winter months (Portland Bureau of Transportation and Alta Planning & Design 2020; Mathew et al. 2019). Trip Replacement The degree to which e-scooters influence transportation safety is not entirely clear, given the varying risks associated with different modes. Surveys consistently find that e-scooter trips replace walking and bicycling trips to the same or a greater degree than they replace auto trips, although the difference varies by location (Chang et al. 2019). For example, 58% of e-scooter users in Portland and 66% in Denver reported that their most recent trip would have been taken by a low-carbon mode (walking, bicycling, or transit) if e-scooters were not available, compared with only 40% and 33% by car, respec- tively (Portland Bureau of Transportation and Alta Planning & Design 2020; Denver Public Works 2019). In Provo and Tempe, 48% and 65% of e-scooter users reported that their most recent e-scooter trip replaced a walking or bicycling trip, respectively, compared with 29% and 25% replacing auto trips (Glenn et al. 2020; Sanders et al. 2020). Figure 1 compares the percentage of e-scooter trips that replaced car trips and low-carbon trips in seven cities. The proliferation of shared e-scooters has affected the use of bikeshare programs in the United States. In Portland, BIKETOWN’s reasons for riding and the resulting patterns in trip characteristics, are described below. Riding Frequency Surveys that ask about riding frequency tend to find that most people ride infrequently and, in some cases, have ridden only once. For example, 49% of respondents to the City of Chicago’s survey had ridden only once, compared with 15% who had ridden at least five times (City of Chicago 2020a). These data differ from data that e-scooter companies report, which indicate that 41% of users ride e-scooters at least once per week, while 42% use them occasionally, but less than once per week. In contrast to Chicago, the proportion of respondents to Baltimore’s community survey that reported riding each day, a few times per week, and once per week was 5%, 32%, and 14%, respectively (Young et al. 2019). Among university staff in Tempe, Arizona, only 5% reported riding e-scooters at least once/week, with another 16% reporting occasional use, and 12% reporting past use (Sanders et al. 2020). Populus survey data showed that most respondents that rode e-scooters (49%) used them infrequently (less than monthly), with 4% riding daily (or almost daily) and 12.5% riding at least weekly (Populus, San Francisco, CA, 2019, unpublished data set: “Populus Groundtruth Data”). Sanders et al. (2020) found that riding frequency had a sig- nificant positive correlation with the perceived benefits of e-scooter use but a significant negative correlation with the perceived barriers related to safety. Trip Timing E-scooter trips tend to follow fairly consistent patterns, with relatively low morning ridership on weekdays and a peak between approxi- mately noon and the afternoon commute hour (5:00 or 6:00 p.m.) (Chang et  al. 2019; Portland Bureau of Transportation and Alta Planning & Design 2020; Denver Public Works 2019; Zou et al. 2020), although the e-scooter afternoon peak was later in some places, like Indianapolis, Indiana (Mathew et al. 2019). A study comparing e-scooter trips in Minneapolis and Austin found that e-scooter use was higher at night in Minneapolis, and that ridership in Minneapolis was fairly consistent across the days of the week, compared to a clear peak in ridership over the weekend in Austin (Bai and Jiao 2020). Source: Portland Bureau of Transportation and Alta Planning & Design 2020. FIGURE 1 Percentage of e-scooter trips that replaced other travel modes in seven cities.

6 in Provo were more likely than non-college-aged students to report using e-scooters to replace a transit trip (Glenn et al. 2020). These findings about trip replacement are underscored by the distribution patterns of e-scooters and e-scooter ridership within cities, including both where e-scooter companies want to concentrate their fleets and where fleets naturally concentrate during the day— downtown areas and other locations with a high density of destina- tions (Jiao and Bai 2020; City of Chicago 2020a; City of Santa Monica 2019; Portland Bureau of Transportation and Alta Planning & Design 2020). Further investigation into the connections between urban form and e-scooter use, walkability, bikeability, and the availability of public transit would be helpful in determining the potential overall impacts on traffic safety outcomes, physical activity, and differences in the feasibility of e-scooters as a form of urban mobility. What Perceptions of Safety and Risk Are Associated with E-Scooters? The perceived safety and risk associated with e-scooters have been examined in several studies, some of which have asked how safe e-scooter users feel riding in general or around certain types of road- way users. Among university staff in Tempe, about 65% of previous and current e-scooter riders reported feeling some degree of safety while riding; the proportion of those who indicated feeling very safe increased significantly (p ≤ 0.001) with an increase in e-scooter expe- rience (Sanders et al. 2020). Main barriers to the use of e-scooters included traffic safety concerns such as • Worrying about hitting someone or being hit (indicated by more than 40% of nonriders and approximately 50% of past and occa- sional riders), • Unsteadiness and worries about falling or lacking control when riding (approximately 30% of nonriders and 25% of past riders), and • Feeling a lack of safe locations to ride (nearly 35% of past riders and more than 25% of occasional riders). In an online Qualtrics panel survey of residents in Idaho, Washington, and Oregon cities with micromobility systems (n = 1,502), the most common deterrent to using e-scooters and bikes was danger from auto traffic (92% of survey respondents), which was a stronger deterrent for e-scooter use than for bike or e-bike use (Pimentel and Lowry 2020). Some studies have investigated whether or not other roadway and sidewalk users feel safe walking, biking, or driving around e-scooter users. Fifty-six percent of survey respondents in Rosslyn reported feel- ing unsafe walking around e-scooters, and this response had a highly negative correlation with past e-scooter ridership (James et al. 2019). The percentage of people who felt unsafe around e-scooter users was much higher than those who felt unsafe walking around bicyclists on their own bikes (11%) or those on dockless e-bikes (29%). Similarly, approximately two-thirds of respondents reported that driving around e-scooters felt uncomfortable, and this had a negative correlation with past e-scooter ridership. A higher percentage of respondents reported feeling uncomfortable driving around e-scooters than driving around bicyclists on their own bikes (16%) or on dockless e-bikes (21%). The perception of safety among e-scooter users in proximity to specific aspects of infrastructure (e.g., riding in the roadway, in bike decline in ridership may be attributed to the introduction of e-scooters, among other reasons (BIKETOWN is a bicycle-sharing system in Portland). Visual inspection of a ridership graph shows e-scooter ridership was approximately 4.5 times that of BIKETOWN in 2019 (Portland Bureau of Transportation and Alta Planning & Design 2020). In Denver, there are 15 times more e-scooter rides than e-bike rides per day, despite the e-scooter fleet size being only 2.5 times higher than dockless e-bikes (Denver Public Works 2019). NACTO has noted in multiple reports that the rise of e-scooters in 2018 coincided with the dwindling presence of dockless bikes in cities (NACTO 2019b). Data from 2019 corroborate this trend; of the 136 million trips on shared bikes, e-bikes, and e-scooters (an increase for all modes), nearly 86 million of those trips were on shared e-scooters, while only 10 mil- lion and 40 million trips were on dockless e-bikes and station-based bikeshare bikes, respectively (NACTO 2020). Trip substitution may indicate small but important changes in longer-term travel, although the density of the city and availability of transit and bicycle facilities may affect how trip substitutions are made. For example, in a survey of university staff in Tempe, those who mainly walked or biked were significantly more likely to have ridden an e-scooter at least once per week in the past month than those who mainly drove for transportation, and 15% and 13% of respondents indicated that they walked and biked less, respectively, now that they used e-scooters (Sanders et  al. 2020). In contrast, nearly 6% of respondents to Portland’s 2018 e-scooter user survey (n = 3,444) reported getting rid of a car due to the availability of e-scooters (Portland Bureau of Transportation 2019). More than 40% of respondents to surveys in San Francisco and Chicago reported replacing an auto trip with an e-scooter trip— slightly more than replaced walking or bicycling in both cities—while between 11% and 15% replaced using transit (SFMTA 2019; City of Chicago 2020a). Forty-six percent of respondents to the Rosslyn survey reported replacing car (rideshare and personal) trips with their most recent e-scooter trip, while 45% replaced a walking or bike (bikeshare or personal) trip (James et al. 2019). Additionally, 52% of respondents to the Rosslyn survey reported an overall decrease in rideshare and taxi trips since the introduction of e-scooters, followed by decreased bikeshare usage for 44%, driving for 35%, and walking for 28%. In Santa Monica, 49% of shared mobility trips replaced car trips, while 39% replaced walking trips (City of Santa Monica 2019). Approximately 60% of Santa Monica respondents reported walking, bicycling, and using transit to the same degree after the introduc- tion of e-scooters, which suggests a potentially important shift in the travel behavior of the population. For the major metropolitan areas covered in TCRP Research Report 230: Transit and Micromobility, there were some differences between high-, medium-, and low- density transit areas, but driving alone or in another car—with another passenger or through ridehail—was the mode most fre- quently reported as being replaced by an e-scooter trip (Murphy et al. 2021). Walking was the second most frequent response in all cases. Examinations of trip replacement by other characteristics, such as income, race, and age, suggest several correlations. For example, lower-income e-scooter users in Portland were more likely to say that their e-scooter trip replaced walking or transit; in contrast, higher- income Portlanders were more likely to report that it replaced a car trip. Respondents of color were less likely than White respondents to replace walking with an e-scooter trip, but more likely to replace driving or using transit with an e-scooter trip. College-aged students