Complement or compete? The effects of shared electric scooters on bus ridership

Ziedan, Abubakr; Shah, Nitesh R.; Yu, Wen; Brakewood, Candace; Cherry, Christopher; Cole, Justin

doi:10.1016/j.trd.2021.103098

Cited by 35 publications

(8 citation statements)

References 14 publications

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“…Within the second stream of research, a number of studies compared docked bikes and dockless e-bikes or e-scooters in several US cities including Washington, D.C., San Francisco, Louisville, Chicago, and Austin [7,[17][18][19][20][21][22][23][24]. In terms of the interaction between e-scooter and transit modes, previously published papers suggest that public transit and scooter complemented each other [25][26][27].…”

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

View full text Add to dashboard Cite

show abstract

“…Zuniga-Garcxia et al [36] observed that a better public transportation service decreases e-scooter demand and viceversa. Ziedan et al [37] performed a case study in Nashville, Tennessee. They have found only marginal effects of e-scooter sharing systems influencing the public transportation network at all.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis

2022

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Micromobility service systems have recently appeared in urban areas worldwide. Although e-bike and e-scooter services have been operating for some time now, their characteristics have only recently been analyzed in more detail. In particular, the influence on the existing transportation services is not well understood. This study proposes a framework to gather data, infer micromobility trips, deduce their characteristics, and assess their relation to a public transportation network. We validate our approach by comparing it to similar approaches in the literature and applying it to data of over a year from the city of Aachen. We find hints at the recreational role of e-scooters and a larger commuting role for e-bikes. We show that micromobility services in particular are used in situations where public transportation is not a viable alternative, hence often complementing the available services, and competing with public transportation in other areas. This ambivalent relationship between micromobility and public transportation emphasizes the need for appropriate regulations and policies to ensure the sustainability of micromobility services.

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“…As e-scooters become increasingly popular, the debate over their contribution, or lack thereof, to user health as well as motorized transport substitution continues (Fearnley 2020(Fearnley , 2021Fearnley, Johnsson, and Berge 2020;Luo et al 2021;Reck, Martin, and Axhausen 2022;Wang et al 2022;Ziedan et al 2021). This article poses the research question: What factors affect e-scooters' replacement of car use, public transport (PT) use, walking and cycling?…”

Section: Questionsmentioning

confidence: 99%

Factors Affecting e-Scooter Mode Substitution

Fearnley

2022

Findings

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What transport modes do e-scooters replace? This article analyses the results from a web survey conducted among customers who were registered with five different shared e-scooter apps in Norway in October-November 2021. It is found that the context of the e-scooter trip, as well as characteristics of the users and their choice situation, impact the answer to that question. In all circumstances but for night rides, e-scooters most often replace walking. However, e-scooters are also found to replace cars as a transport mode, especially with longer e-scooter trips, if the user is male, if the e-scooter is privately owned (as opposed to shared), and to destinations that are poorly served by public transport.

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Complement or compete? The effects of shared electric scooters on bus ridership

Cited by 35 publications

References 14 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

Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis

Factors Affecting e-Scooter Mode Substitution

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