Impact of e-scooter sharing on bike sharing in Chicago

Yang, Hongtai; Huo, Jinghai; Bao, Yongxing; Li, Xuan; Yang, Linchuan; Cherry, Christopher

doi:10.1016/j.tra.2021.09.012

Cited by 48 publications

(15 citation statements)

References 45 publications

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“…To prove the link of these two modes of transport, a study carried out in Chicago analysed the data of two sharing companies. The results proved that users of bike sharing decreased by 10.2% in the area reserved for shared vehicles with the introduction of shared e-scooters [ 9 ]. However, the real difference between these two transport modes lies in safety.…”

Section: Introductionmentioning

confidence: 99%

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

Pazzini

Cameli

Lantieri

et al. 2022

IJERPH

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Negative effects of a massive use of cars, such as congestion, air pollution, noise, and traffic injuries, are affecting the cities everywhere. Recently introduced shared vehicles, such as e-scooters and electric bicycles, could potentially accelerate the transition towards sustainable mobility. Although these vehicles are becoming increasingly common and accepted within regulatory frameworks, some local governments are not yet ready to integrate e-scooters into their transport systems. Indeed, the legislation is unclear as it is not easy to determine whether the e-scooter is more like a bicycle or a vehicle. Moreover, it is difficult to predict the impact of e-scooters on road traffic, as well as the type of road infrastructure chosen by e-scooter drivers or the possible interaction of such vehicles with weak road users, such as pedestrians or cyclists. This study showed an analysis of speed and behaviour of e-scooter drivers in the city of Trondheim (Norway) to investigate how to manage this mode of transport. A total of 204 e-scooters were observed on six different roads in the city centre. The speed of e-scooter drivers was measured by a speed tracker (average value 15.4 km/h) and their behaviour recorded by a hidden observer in the field. Gender, age, distance from pedestrians, speed adaptation to the environment, and type of vehicle used were registered for each e-scooter. Through a Binomial Logit analysis, the data obtained were used to analyse the type of road infrastructure preferred by e-scooter drivers. Results showed that the cycle path is more widely used with percentage value from 60% to 90% of users. In addition, the probability of choice depended mainly on the road environment. The aim of this analysis was to assist local authorities in regulating the safe use of e-scooters and developing appropriate policies for their integration into cities.

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Section: Introductionmentioning

confidence: 99%

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

Pazzini

Cameli

Lantieri

et al. 2022

IJERPH

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“…Moreover, average dockless e-scooter trips were longer in terms of travel distance by a third and approximately twice as long in terms of travel time than the average docked shared bike trips [20]. Another study in Chicago found that the average travel time of scooter trips is shorter than bike trips [22]. Surprisingly, earlier work found that dockless shared e-scooters are less sensitive to weather conditions than docked shared bikes [23].…”

Section: Literature Reviewmentioning

confidence: 86%

“…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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“…Research on the network evolution of e-scooter sharing is still limited, although a particular interest is to compare e-scooter/e-bike sharing with conventional bike-sharing. The former’s advantages lie in better performance regarding sharing frequency, fleet size [ 64 ], and better replacement potential of first and last-mile or short trips [ 8 , 60 ], compared with bike-sharing.…”

Section: Related Workmentioning

confidence: 99%

The periodicity and initial evolution of micro-mobility systems: a case study of the docked bike-sharing system in New York City, USA

Zhang

Song

2022

Eur. Transp. Res. Rev.

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Objectives This study developed an analytical framework that aims at understanding the evolutionary processes of a micro-mobility system (for example, bike-sharing), which offers insights into the transforming nature of a city transport system. Methods Firstly, the framework applied a Gaussian Mixture Model to examine the long-term fluctuations of travel demands. Secondly, it investigated the growth trajectories of service points via exponential and logistic growth models. Cumulative connections with other points represented the growth of a service location. An eigendecomposition approach was used to uncover the hidden structures behind the growth curves. Results This framework was applied in the docked bike-sharing program in New York City, USA. The results show that there existed periodic patterns of travel demands in the long term. The majority of stations grew rapidly after they began to operate. However, the temporal signatures of stations’ growth displayed some variations across different locations. Conclusion This proposed workflow can be employed in other cities with similar context to better investigate how micro-mobility systems evolve.

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Impact of e-scooter sharing on bike sharing in Chicago

Cited by 48 publications

References 45 publications

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

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

The periodicity and initial evolution of micro-mobility systems: a case study of the docked bike-sharing system in New York City, USA

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