Influence of the built environment on E-scooter sharing ridership: A tale of five cities

Huo, Jinghai; Yang, Hongtai; Li, Chaojing; Zheng, Rong; Yang, Linchuan; Yu, Wen

doi:10.1016/j.jtrangeo.2021.103084

Cited by 93 publications

(27 citation statements)

References 37 publications

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“…However, further research on the introduction of MaaS should also include other cities because of the varying context of each city. In this respect, research [45,46] using hierarchical models that can deal with spatial correlation issues is also needed. Lastly, as MaaS becomes more concrete in the future, an enhanced SP survey design considering more realistic traffic conditions such as waiting time is required.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

et al. 2021

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Mobility as a Service (MaaS), which integrates public and shared transportation into a single service, is drawing attention as a travel demand management strategy aimed at reducing automobile dependency and encouraging public transit. In particular, there have been few studies that recognize traffic congestion during peak hours and identify related factors for practical application. The purpose of this study is to explore what factors affect Seoul commuters’ mode choice including MaaS. A web-based survey that 161 commuters participated in was conducted to collect information about personal, household, and travel attributes, together with their mode preference for MaaS. A latent class model was developed to classify unobserved latent groups based on trip frequency by means and to identify factors influencing mode-specific utilities (in particular, MaaS service) for each class. The result shows that latent classes are divided into two groups (public transit-oriented commuters and balanced mode commuters). Most variables have significant impacts on choice for MaaS. The coefficient of MaaS choice of Class 1 and Class 2 were different. These findings suggest there is a difference between the classes according to trip frequency by means as an influencing factor in MaaS choice.

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Section: Conclusion and Discussionmentioning

confidence: 99%

What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

et al. 2021

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“…When analyzing the studies in different cities, the properties of e-scooter trips differ from area to area, indicating a considerable influence of spatial factors. Huo et al [18] compared the e-scooter usage in five cities in the United States. The temporal distribution of trips is similar, with peaks between 11:30 and 17:30.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zou et al [26] investigated the influence of biking infrastructure on micromobility trips and discovered that roads with biking infrastructure increase the likelihood of the road being used in a micromobility trip. High usage of e-scooters has generally often been observed in the city center and next to university campuses [16,18,24]. Similarly, a more significant density of bus stops is related to more e-scooter trips [15,18,29]; however, whether e-scooter trips and bus stops are independent is not clear.…”

Section: Literature Reviewmentioning

confidence: 99%

“…High usage of e-scooters has generally often been observed in the city center and next to university campuses [16,18,24]. Similarly, a more significant density of bus stops is related to more e-scooter trips [15,18,29]; however, whether e-scooter trips and bus stops are independent is not clear. Luo et al [22] concludes that most e-scooter trips substitute walking or public transportation trips in Louisiana, hinting that e-scooter usage is independent of bus stop density, as bus trips can be replaced with e-scooter trips.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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“…e literature review of related studies is composed of two parts: the influencing factors of public bicycle ridership before the outbreak of COVID-19 and the impact of COVID-19 on bike-sharing usage. External factors mainly refer to built environment factors, including density, diversity, and design [7,[11][12][13][14]. Additionally, demographic factors are also regarded as external factors, including age, private car ownership, and income [15][16][17][18].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Spatially Heterogeneous Effects of the Built Environment on Bike-Sharing Usage during the COVID-19 Pandemic

Yang

Guo

Zhai

et al. 2022

Journal of Advanced Transportation

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Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly.

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Influence of the built environment on E-scooter sharing ridership: A tale of five cities

Cited by 93 publications

References 37 publications

What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

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

Exploring the Spatially Heterogeneous Effects of the Built Environment on Bike-Sharing Usage during the COVID-19 Pandemic

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