Gender Equality and E-Scooters: Mind the Gap! A Statistical Analysis of the Sicily Region, Italy

Campisi, Tiziana; Skoufas, Anastasios; Kaltsidis, Alexandros; Basbas, Socrates

doi:10.3390/socsci10100403

Cited by 26 publications

(9 citation statements)

References 54 publications

(39 reference statements)

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“…With regard to the frst stream of studies, earlier escooter research eforts followed survey based approaches to investigate and understand dockless e-scooter shared systems [3,[11][12][13][14]. Most of these studies focused on understanding perceptions of e-scooter riders and nonriders [5,11]; diferences in e-scooter renters and owners [15]; impact of age, gender, and level of education on e-scooter usage [15,16]; relation of e-scooter with transit [13]; differences in the knowledge of rules and regulations among escooter riders and nonriders [5]; and behavior of long term users [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin

Alsulami

Tirtha

Yasmin

et al. 2023

Journal of Advanced Transportation

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The goal of the current study is to identify and quantify the influence of various contributing factors on dockless e-scooter demand. Drawing on high-resolution e-scooter trip level data for 2019 from Austin, Texas, we develop census tract (CT) level demand data for four time periods of the day. The time-period specific data is partitioned for weekdays and weekends. Using the prepared datasets, we develop a joint panel linear regression (JPLR) model framework that accommodates for the influence of unobserved factors at multiple levels-CT, month, day, and time period levels. The analysis results indicate that the proposed JPLR models outperform the independent linear regression models for both weekdays and weekends. The results also manifest a significant association between e-scooter demand and several independent variables including sociodemographic attributes, transportation infrastructure variables, land use and built environment variables, meteorological attributes, and situational attributes. Further, several panel-specific correlation effects are found to be significant across four dimensions highlighting the importance of accommodating the influence of common unobserved factors on e-scooter demand across different time-of-day dimensions. The model validation exercise results revealed that the proposed models performed well compared to the independent models. Finally, the estimated models are employed to conduct a policy exercise illustrating the value of the estimated models for understanding CT level e-scooter demand on weekdays and weekends. The results indicate that land use mix, proportion of commuters, and season are some of the most influential factors for e-scooter demand.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin

Alsulami

Tirtha

Yasmin

et al. 2023

Journal of Advanced Transportation

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“…So I wouldn't use it for that" (P18, female). For more gender equitable use there is a need for e-scooter fleets with baskets (Campisi et al, 2021).…”

Section: Urban Structuresmentioning

confidence: 99%

Gender perspectives on electric micromobility use

Parnell

Merriman

Plant

2023

Hum Ftrs & Erg Mfg Svc

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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.

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“…Recently, a study [16] on predicting demand using deep learning methods was also performed. There have been other studies suggesting ways to improve the legal system [1,8,17,18], promoting ways to enhance inclusiveness such as gender equity [19], selecting the location of battery charging systems [20], and estimating travel purpose [21]. Furthermore, a teaching concept differentiated by education level was introduced to improve the understanding of electric mobility including e-scooter [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

et al. 2022

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The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

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Gender Equality and E-Scooters: Mind the Gap! A Statistical Analysis of the Sicily Region, Italy

Cited by 26 publications

References 54 publications

Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin

Modeling Dockless Shared E-Scooter Demand by Time of Day: A Case Study of Austin

Gender perspectives on electric micromobility use

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

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