Factors influencing shared micromobility services: An analysis of e-scooters and bikeshare

Hosseinzadeh, Aryan; Karimpour, Abolfazl; Kluger, Robert

doi:10.1016/j.trd.2021.103047

Cited by 41 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have shown that weather has the potential to change travel patterns for multiple transportation modes, including transit [45], e-scooter or bikeshare [46], and passenger vehicles [47]. The impact of weather can also influence refueling behavior among internal combustion engine vehicle drivers, which is why many petroleum refuel stations have canopy structures to protect fuel pumps and users during inclement weather.…”

Section: Analysis Of Usage With the Impact Of Rainmentioning

confidence: 99%

Evaluation of Electric Vehicle Charging Usage and Driver Activity

Mahlberg,

Desai,

Bullock

2023

WEVJ

View full text Add to dashboard Cite

As the country moves toward electric vehicles (EV), the United States is in the process of investing over USD 7.5 billion in EV charging stations, and Indiana has been allocated $100 million to invest in their EV charging network. In contrast to traditional “gas stations”, EV charging times, depending on the charger power delivery rating, can require considerably longer dwell times. As a result, drivers tend to pair charging with other activities. This study looks at two EV public charging locations and monitors driver activity while charging, charge time, and station utilization over a 2-month period in Lafayette, Indiana. Over 4000 charging sessions at stations with varying power levels (350 kW, 150 kW, and 50 kW) were monitored, and the median charge time was between 28 and 36 min. A large variation in station utilization was observed at Electrify America charging stations that had a range of stations with 350 kW, 150 kW, and 50 kW available. The highest utilization rates by hour of day on average were observed at 25% at the 150 kW Tesla station. Driver activity during charging influenced dwell times, with the average dwell time of drivers who waited in their vehicles to charge being 10 min shorter than those who would travel to the shops. Rain in the forecast also impacted the number of users per day. Although there are no published metrics for EV utilization and associated driver activities, we believe examining this relationship will produce best practices for planning future investments in EV charging infrastructure as public and private sector partners develop a nationwide charging network.

show abstract

Section: Analysis Of Usage With the Impact Of Rainmentioning

confidence: 99%

Evaluation of Electric Vehicle Charging Usage and Driver Activity

Mahlberg,

Desai,

Bullock

2023

WEVJ

View full text Add to dashboard Cite

show abstract

“…Te current study falls within the third stream of research. Tis group of research eforts focused on analyzing real-world dockless shared e-scooter trip data [6,[30][31][32][33][34][35][36]. Previous studies in this stream of research investigated the primary purpose of using e-scooter and found that these emerging mobility systems are mostly used for leisure rather than for commuting purposes [6,31].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previously published studies on shared dockless e-scooters found that many factors increased e-scooter demand including commercial and industrial presence, population density, land use mix, access to transit, bike score, central business district locations, student populated regions, and weather conditions [19,30,31,39,40]. Te methodological approaches employed to study e-scooter data include negative binomial count models, linear mixed models, and spatial regression models (and variants such as spatial error and autoregressive error models) [30,31,33,34,39,40].…”

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

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.

show abstract

“…Hosseinzadeh et al [74] examined how various factors affect shared micromobility services. Their two main goals were, on the one hand, to thoroughly examine how various weather-related factors, major holidays, and special events affect micromobility services, and, on the other hand, to compare the effects of these factors on e-scooters and bikeshares in Louisville, Kentucky.…”

Section: Factors Influencing the Adoption Of Micromobility In Urban T...mentioning

confidence: 99%

Micromobility in Urban Trail Paths: Expanding and Strengthening the Planning of 15-Minute Cities

Vizmpa,

Botzoris,

Lemonakis

et al. 2023

Land

View full text Add to dashboard Cite

Contemporary urban planning models include urban trail paths. These are paths that create active transportation corridors within a city’s built environment, providing more sustainable travel, especially for short trips. The benefits of their use are plentiful, including improvements in commuters’ health, reductions in energy footprint, and socio-economic benefits for the entire society. In modern urban planning approaches such as the “15-minute city”, urban trail paths serve as connectors, facilitating access to amenities beyond the close-proximity concept of a “neighborhood”. They act as a way of connecting residents to other 15-minute cities/neighborhoods via safe routes, reducing extensive car use. Micromobility constitutes a novel approach to short trips with proven results. This paper explores the possibility of introducing micromobility as a means of connecting 15-minute cities/neighborhoods through urban trail paths. Through a literature review, an analysis is conducted of the opportunities arising from the introduction of micromobility, as well as on the factors influencing its sustained use in urban mobility and the public realm.

show abstract

Factors influencing shared micromobility services: An analysis of e-scooters and bikeshare

Cited by 41 publications

References 42 publications

Evaluation of Electric Vehicle Charging Usage and Driver Activity

Evaluation of Electric Vehicle Charging Usage and Driver Activity

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

Micromobility in Urban Trail Paths: Expanding and Strengthening the Planning of 15-Minute Cities

Contact Info

Product

Resources

About