Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas
Abstract
:1. Introduction
2. Systematic Review Methodology
2.1. Sampling
2.2. Analysis and Synthesis
- Demographic characteristics such as gender, age, living location, employment status, income, education level, etc.;
- Trip purpose;
- Usage frequency;
- Motives to use e-scooters;
- Deterrents from use of e-scooters.
3. Findings
3.1. Spatio-Temporal Mobility Patterns
3.1.1. Temporal Analysis
3.1.2. Spatial Analysis
3.2. Synthesis of E-Scooter User Personas and User Types
3.2.1. Usage Frequency-Based User Types
Curious One-Time Users
Casual Users
Regular/Power Users
Non-Adopters
3.2.2. Motivation-Based Personas
Users Not Satisfied with Current Mobility Options
Users Having a Positive Travel Experience
4. Discussion and Conclusions
4.1. Summary of Findings
4.2. Limitations and Future Research Directions
4.3. Implications for Steering Mobility Service Development
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Key Concepts | Synonyms/Related Concepts/Variations |
---|---|
E-scooter | “E-scooter” OR “electric scooter” OR “scooter” OR “self-balancing scooter” OR “light-weight scooter” OR “electric kick scooter” |
Micromobility | “Micromobility” OR “micro-mobility” OR “micro mobility” OR “micro-transit” |
Sharing | “Shared” OR “sharing” OR “public” OR “dockless” OR “shared vehicles” OR “free-floating” OR “rental” OR “rented” |
Author(s), Year | Data Interval | Trip Data Sample Size | Study Area | Method | Day of Week | Time of Day | Other Specific Indices |
---|---|---|---|---|---|---|---|
McKenzie, (2019) [43] | 132 Days (13 June–23 October 2018) | 937 k | Washington, DC, USA | Cosine similarity | Highest similarity: Tuesday-Thursday and Saturday-Sunday | Mid-day peak | - |
Jijo et al. (2019) [44] | 3-Month (4 September– 30 November 2018) | 425k | Indianap-olis, IN, USA | Explanatory analysis | - | Peak hour: 4 p.m. to 8 p.m. | - |
Hosseinzadeh et al., (2021) [45] | 16-months (November 2018– February 2020) | 501 k | Louisville, KY, USA | Generalized additive model | Peak time of Saturdays | Peak time from 1 p.m. to 5 p.m. | - |
Jiao and Bai, (2020) [46] | 11-Months (April 2018– February 2019) | 1.74 M | Austin, TX, USA | Negative binomial regression | Saturdays | Saturdays from 1 p.m. to 5 p.m. | - |
Zou et al., (2020) [47] | 5-Weeks (March–April 2019) | 113 k | Washington, DC, USA | Explanatory analysis | - | Midday and evening peak in principal and minor arterial | - |
Reck et al., (2020) [48] | 15 days (8–23 January 2020) | 18 k | Zurich, Switzerland | Multinomial logit | - | -Morning peak: negative and night peak: positive -Higher probability of e-scooters as compared with other micromobility modes at nights and early mornings | -E-scooter used for very short trips (median: 721 m) -Negative association of distance and e-scooter usage |
Almannaa et al. (2020) [49] | 6-Months (3 December 2018–20 May 2019) | 6 M | Austin, TX, USA | Consensus clustering | Two similar clusters: First, weekends plus Fridays; Second, the rest of the days | - | 3 a.m. to 12 p.m. (average speed 2.78 m/s, s.d. 1.64); Rest of the day (average speed 2.19 m/s; s.d. 0.73) |
McKenzie (2020) [50] | 4-Months (December 2018–March 2019) | 378 k | Washington, DC, USA | Watson’s U2 and CosSim | Faster micromobility options comparing with ride hailing options at 8–9 a.m. and 5 p.m. | - | |
Bai and Jiao (2020) [51] | 4-Months (August 2018 – November 2018) | 886 k | Austin, TX; Minneapolis, MN, USA | Negative binomial model | Weekends and peaked on Saturdays in Austin | Lowest rides in nights in Austin; Peak evening rides and lowest ridership in the morning in Minneapolis | - |
Noland, (2019) [52] | 7-Months (August 2018– February 2019) | 88 k | Louisville, KY, USA | Ordinary least squares regression | Saturdays: positive | - | Holidays: positive Rain: negative Snow: negative Duration (distance): 0 |
Mathew et al. (2019) [53] | 6-Months (4 September 2018– 28 February 2019) | 532 k | Indianapolis, IN, USA | Negative binomial model, Explanatory analysis | - | - | Temperatures drop below freezing: negative Visibility: negative Rain: negative Snow: negative Wind speed: negative |
Liu et al., (2020) [54] | 8-months (September 2018– May 2019) | 500 k | Indianapolis, IN, USA | Explanatory analysis | - | Broad Ripple fall 2018: 6 p.m. to 9 p.m. Butler University fall 2018: evening Downtown Trip fall 2018: 6 p.m. to 9 p.m. | −130% Increase in number of e-scooter trips and 181% in unique e-scooter in service during Indianapolis 500 race special event -Higher median distance/duration of recreational trips as compared with non-recreational |
Zhu et al., (2020) [55] | 4-Weeks (1-28 February 2019) | 52 k | Singapore | Explanatory analysis, Pearson correlation | Not conclusive | Peak hours at night | Temperature: not conclusive Rain: not conclusive Duration: 0 |
Younes et al., (2020) [56] | 7-Months (December 2018– June 2019) | 727 k | Washington, DC, USA | Negative binomial regression | Base is Sunday, Saturdays: positive, The rest of the days: negative | - | Holidays: positive Special events (cherry blossom festival): positive Temperature: positive Visibility: positive Humidity: negative Gas price: positive |
Caspi et al., (2020) [57] | 7-months (August 2018– February 2019) | 2 M | Austin, TX, USA | Spatial Lag, Spatial Durbin, and geographically weighted regressions | Higher median trip distance and duration on weekends/holidays | Comparing weekends and weekdays hourly trips Higher number of trips, median trip distance, and duration of evening peaks as compared with morning peaks | - |
Li & Axhausen (2021) [23] | Normal period (15 February– 14 March 2020) COVID-19 period (15 March–14 April 2020) | 1818 k before COVID-19 1003 k during COVID-19 | Zurich, Switzerland | Comparison study | Peak times before and during COVID-19 | Before COVID-19: 5 a.m., 12 p.m., 4 p.m., and 9 p.m. for weekdays | Before COVID-19 for workdays 4 p.m. is the peak hour of non-leisure activities, Leisure peak hours are 5 a.m., 12 p.m. and 9 p.m. |
Parameters | Jiao and Bai (2020) [46] | Bai and Jiao (2020) [51] | Caspi et al. (2020) [57] | Hosseinzadeh et al. (2021) [45] |
---|---|---|---|---|
Data | 11 months (April 2018– February 2019) | 4 months (August 2018– November 2018) | 7 months (August 2018– February 2019) | 16 months (November 2018– February 2020) |
City, country | Austin, TX, USA | Austin, TX/Minneapolis, MN, USA | Austin, TX, USA | Louisville, TX, USA |
Method | Z-score | Negative binomial | Spatial Durbin | Generalized additive model |
Number of trips | 1740 k | 886 k | 2 M | 501 k |
Distance to CBD | Negative | Negative | 0 (origins)/negative (destination) | - |
Land use mix index | Positive | Not conclusive | 0 (origins)/0 (destination) | 0 |
Residential land use | 0 | - | Positive (origins)/positive (destination) | 0 |
Commercial land use | Positive | Not conclusive | Positive (origins)/positive (destination) | Positive |
Office land use | - | Positive | - | - |
Industrial land use | - | Not conclusive | Positive (origins)/positive (destination) | Negative |
Institutional/Educational land use | Positive | Positive | 0 (origins)/0 (destination) | 0 |
Recreational/parks land use | Positive | Not conclusive | 0 (origins)/0 (destination) | 0 |
Walk-related scores | - | - | - | Positive |
Bicycle-related scores | - | - | Positive (origins)/0 (destination) | Positive |
Transit-related scores | Positive | - | Positive (origins)/positive (destination) | 0 |
Employment density | - | - | Positive (origins)/positive (destination) | 0 |
Intersection density | Positive | - | 0 (origins)/positive (destination) | 0 |
Cul-de-sac density | Negative | - | - | - |
local roads density | - | - | - | 0 |
Job proximity index | - | - | - | 0 |
Elevation | - | - | - | 0 |
Author(s), Year | Trip Purpose | Mode Substitution | Motives | Deterrents | How Often and When | Other |
---|---|---|---|---|---|---|
Fitt and Curl (2020) [58] | - | - | Trying it for the first time, 64% Fun, 55% Faster than the alternative, 22% Recommended by others, 17% Convenient, 15% Cheaper than the alternative, 7% Economical as compared with purchasing | Not having the essential material to rent an e-scooter (bank card, smartphone, etc.); Not being able to check the condition and functionality of e-scooter before riding one | When you see one, you may use it (material availability) | Majority of e-scooter users had never used a kick scooter |
Curl and Fitt (2020) [59] | - | - | - | - | Once 16.9% More than one occasion 52.5% | Those who cycle are more likely to have used an e-scooter than those who do not cycle; Age over 45, with health issues are less likely to use e-scooters |
Laa and Leth (2020) [60] | - ** | In general, replaces walking followed by other slower PT modes (bus and tramway); Often or always replace walking in 35% of situations for work or educational trips and almost never replaces car trips (more than 90%); Never or seldom replaces other modes in shopping trips; Sometimes or always replaces walking and bus trips in 28% of situations for leisure purposes | Saves time when you are in hurry; Fast | - | Daily basis 0.0% Several times per week 4.5% Several times per month 27.3% Less than once a month 44.5% Tried it once 23.6% | - |
Tuncer and Brown (2020) [61] | Mostly not for commuting because of the lack of ability to plan | If in a hurry: substitutes public transit; First-mile and last-mile trips: substitutes walking; New intermodal routes, substituting one slow PT to far but faster PT | Fun, feeling of freedom, continuous movement; Ease of use, convenient; Economical as compared with purchasing; Lack of concern for maintenance; Maneuverable and hybrid vehicle; Not sweating; Riding in style with office clothes; Saving time | Low charge and functional problems of the vehicle; Unavailability; Inaccuracy in map locations; Inability to find the e-scooter because of bad location or parking; Unreliability | Mostly opportunistic; When you see one, you may use it | - |
Bieliński and Ważna (2020) [62] | Fun * 51.8% Social meetings 33.9% To (or from) PT stops 30.4% Eating out 21.4% Recreational 21.4% Work, school, or university 17.9% Shopping 12.5% | - | Fun | High fees (31.8% users, 17.7% non-users) Safety concerns (10.9% users, 16.0% non-users) No scooter nearby (17.6% users, 11.6% non-users) Not enough scooters (10.4% users, 4.8% non-users) Functionality problems (6.8% users, 3.4% non-users) | Daily basis 0.3% Few times a month 1.3% Few times a year 7.3% | - |
Sanders et al. (2020) [63] | Fun or leisure * 42%; Transportation to/from activities 33%; Transportation to/from work 30%; Meeting friends or socializing 16%; Shopping 6%; Transportation to/from school 6%; Other 9% | Fun trips: 50% walking, 30% driving, 8% biking Transportation to/from activities: 51% walking, 35% driving, 10% biking Transportation to/from work: 62% walking, 19% driving, 10% biking; Meeting friends or socializing: 45% walking, 41% driving, 6% biking; Shopping: 42% walking, 42% driving, 15% biking; Transportation to/from school: 67% walking, 29% driving, 0% biking; Total: 57% walking, 25% driving, 8% biking | Faster than walking Convenient Replacing car trips For having fun and relaxing Good option in hot weather Inexpensive as compared with purchasing; Environmentally friendly; Mostly for women, safer from crime and traffic | Worried about hitting someone or being hit, feeling unsteady Not enough safe place to ride Cannot carry much Impractical for longer distances; Unavailability issues; Functional errors and uncharged batteries | Non riders, 68% never ridden an e-scooter Past riders, 12% ridden an e-scooter, but not in the last month; Occasional riders, 16% less than one ride time per week in the last month; Regular riders, 5% at least one ride per week in the last month | Participants with young children are mostly concerned about safety 76% of past and current riders and 23% of non-riders are willing to ride e-scooter somewhat or very likely in the future; 56% of those aged 18–34 would like to ride e-scooter next year as compared with 41% of 35–44 years old and 25% of upper 45 years old; 40% of walkers, cyclists and drivers would like to ride e-scooter next year as compared with 51% of PT users |
Degele et al. (2018) [13] | Leisure trips; Weekday activities such as commuting | - | Just to try it out | - | 4.41% Often user (every 4.6 days) 23.63% Monthly user (every 25 days) 58.34% Sometime user (every 19.5 days) 13.72% One-time user | Gender hardly has any influence on usage or e-scooter ride length |
Rayaprolu and Venigalla (2020) [64] | Social purposes 44.8% First-mile and last-mile trips 29.6% Work or school 12.8% Running errands and chores 12.8% | - | 59% Fun 51% TIME saving 39% Easy to use and hassle free 23% Economical 7% Healthy 4% Safe | Not interested or not viable 34% Unsafe 23% Expensive 18% Traffic or pollution 7% Time consuming 2% Uncertainty in the dockless vehicle availability | CaBi members prefer more to use e-scooters occasionally than regularly | E-scooters are popular for 5–15 min trips (low significant); E-scooter is the least preferred micromobility mode between four options in Washington, DC, USA; Young people prefer an e-scooter over CaBi and e-bikeshare; Female and medium-income households prefer CaBi over an e-scooter |
James et al. (2019) [20] | - | 39%, 52% Uber, Lyft, or a taxi 33%, 28% walked 12%, 44% Personal or shared bike 7% Bus 7%, 35% Drive *** | - | As a pedestrian, feeling unsafe around e-scooter (76% very unsafe and unsafe); As a e-scooter rider, feeling unsafe around e-scooter (24% very unsafe and unsafe); Pedestrians find e-scooters blocking the sidewalks (75% always and often); E-scooter users find e-scooters blocking the sidewalks (24% always and often) | - | - |
Author(s), Year | City, Country | Date of Shared E-Scooter Deployment | Date of Survey or Data Collection | Data Collection Method | Data Quantity | Methodology |
---|---|---|---|---|---|---|
Tuncer and Brown (2020) [61] | Paris, France | 4 months before data collection (approximately June 2018) | October 2018–January 2019 | Observation, interview | 20 Users and 10 informal shop owners | Video-ethnographic and interview text coding |
Fitt and Curl (2020) [58] * | Auckland, Hutt Valley, Christchurch, and Dunedin, New Zealand | Late 2018 and early 2019 | February and March 2019 | Online survey | 491 Respondents (341 users and 150 non-users) | Descriptive statistics, social practice logistic regression [59] |
Bieliński and Ważna (2020) [62] | Tricity, Poland | May 2019 | 21 August 2019– 27 September 2019 | Computer-assisted personal interviewing technique (CAPI) | 633 Respondents | Descriptive statistics |
Laa and Leth (2020) [60] | Vienna, Austria | - | 13 August 2019– 7 December 2019 | Online survey and field surveys at three cycle paths | 188 Respondents (166 e-scooter users and 22 non-users) | Descriptive statistics |
Sanders et al. (2020) [63] | Tempe, AZ, USA | - | 2 May 2019 | Online survey in Arizona State University (ASU) | 1256 University staff (not faculty) (849 Non-riders and 405 e-scooter riders) | Descriptive statistics and statistical tests |
Degele et al. (2018) [13] | Germany | - | 22 April 2017– 20 October 2017 | GPS trip data | 53,000 Trip data | Hierarchical clustering and descriptive statistics |
Rayaprolu and Venigalla (2020) [64] | Washington, DC, USA | - | July 2019 | Mixed-mode survey: paper-based and web-based questionnaire | 440 Responses (309 respondents who used micromobility at least once) | Logistic regression, odds ratio analysis, descriptive statistics, and statistical tests |
James et al. (2019) [20] | Arlington, VA, USA | 2017 | 4–24 April 2019 | Online survey and observation | 181 Survey responses and 606 e-scooter observations | Descriptive statistics and text analysis |
Author(s), Year, Sample **** | Demography | ||||||
---|---|---|---|---|---|---|---|
Gender | Age | Education Level | Income | Occupation Status | Citizen/Tourist | Other | |
Tuncer and Brown (2020) [61] (e-scooter users) | 90% Male 10% Female | 5% <25 years 55%: 25–35 years 40% >35 years | - | - | - | 80% Residents 20% Tourists | - |
Fitt and Curl (2020) [58] ** (respondents) | 50% Male 50% Female | 29% 18–24 year 24% 25–34 years 19% 35–44 years 15% 45–54 years 10% 55–64 years 5% 65 years and over | 24% School level qualifications; 12% Postschool level qualifications; 27% Bachelor’s degree; 32% Postgraduate qualification | 12% From $0 to 19 k 7% From $20 to 30 k 6% From $30 k to $50 k 11% From $50 k to $70 k 18% From $70 k to $100 k 45% From $100 k and over | 53% Working full time * 28% Studying full time | - | Ethnicity * European 93% Maori 7.5% Other 6% Car availability: 67% Available 32% Unavailable |
Curl and Fitt (2020) [59] ** (e-scooter users) | 57% Male 43% Female | 30% 18–24 years 26% 25–34 years 19% 35–44 years 14% 45–54 years 8% 55–64 years 3% 65 years and over | 25% School level qualifications; 13.4% Postschool level qualifications; 28% Bachelor’s degree; 33.6% Postgraduate qualification | 10.5% From $0 to $20 k 5.6% From $20 k to $30 k 4% From $30 k to $50 k 10.5% From $50 k to $70 k 13% From $70 k to $100 k 39% From $100 k and over 17.4% Other or no answer | 58% Working full time * 28% Studying full time | - | Ethnicity * European 84% Maori 7.6% Other 8.4% Health Condition: No health condition 93% Health condition 7% Car availability: 69% Available 31% Unavailable |
Bieliński and Ważna (2020) [62] (e-scooter users) | 62.5% Male 37.5% Female | Mean 31 years | - | Median income 3205 PLN~700 € | 82.1% Mostly employed 30.4% Student | - | - |
Laa and Leth (2020) [60] (e-scooter users) | 74.2% Male 25.8% Female (e-scooter users) | 20.8% 16–25 years 46.2% 26–35 years 15.4% 36–45 years 17.6% 46 years and over (respondents) | 33.7% High school 64.2% University | - | 55.5: Employed full time 13.1% Employed part time 29.9% In school/university 1.9% Other | 84.4% Residents15.6% Others | - |
Sanders et al. (2020) [63] (respondents) | 34% Male 62% Female 1% Other 3% No answer | 5% 18–24 years 31% 25–34 years 25% 35–44 years 19% 45–54 years 20% 55 years and over | - | 5% From $0 to $35 k 15% From $35 k to $50 k 22% From $50 k to $75 k 14% From $75 k to $100 k 17% From $100 k to $150 k 10% From $150 k and over 16% No answer | - | - | 67% Non-Hispanic white alone; 10% Hispanic/Latino alone; 6% Two or more races; 5% Asian alone; 3% Black/African American alone; 8% Others or no answer;30% Household with young children (under 16 years) |
(Degele et al. 2018) [13] (e-scooter users) | 77% Male 23% Female | Late 20s, ~17.5% 45–50 years, ~6.5% | - | - | - | - | - |
Rayaprolua and Venigalla (2020) [64] (e-scooter users) | 71% Male 28% Female 1% No answer or other | 63% Young 33% Middle 3% Old | 25% From $0 to $30 k 9% From $30 k to $50 k 14% From $50 k to $75 k 51% From $75 k and over 1% No answer | 67% White 30% Person of color | |||
James et al. (2019) [20] *** (respondents) | 56% Female 44% Male | 70% 18–44 years | - | 23% From $0 to $50 k 36% From $100 k and over | - | - | 69% White 31% Other |
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Dibaj, S.; Hosseinzadeh, A.; Mladenović, M.N.; Kluger, R. Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas. Sustainability 2021, 13, 11792. https://doi.org/10.3390/su132111792
Dibaj S, Hosseinzadeh A, Mladenović MN, Kluger R. Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas. Sustainability. 2021; 13(21):11792. https://doi.org/10.3390/su132111792
Chicago/Turabian StyleDibaj, Samira, Aryan Hosseinzadeh, Miloš N. Mladenović, and Robert Kluger. 2021. "Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas" Sustainability 13, no. 21: 11792. https://doi.org/10.3390/su132111792