Differences in first-mile and last-mile behaviour in candidate multi-modal Boston bike-share micromobility trips

Romm, Daniel; Verma, Priyanka; Karpinski, Elizabeth; Sanders, Tracy L; McKenzie, Grant

doi:10.1016/j.jtrangeo.2022.103370

Cited by 12 publications

(2 citation statements)

References 8 publications

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“…As described in the introduction, the trip for metro access and egress (refers to taxi-metro integrated use) bridges the frst-and-last-mile gaps. Te feeder mode choice for metro access and egress is also afected by the characteristics of travel [5,22]. Walking, bicycling, and bus are the most common feeding mode for metro access and egress [8,23,24].…”

Section: Te Feeder Mode Formentioning

confidence: 99%

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

Chen

Hao

et al. 2023

Journal of Advanced Transportation

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It is very common that taxi is used as a feeder mode to/from a metro station especially in third-tier cities of China. But research on taxi-metro integrated use is rather limited. This paper investigates the relationship between feeder-related built environment and taxi-metro integrated use by using taxi trajectory data in Lanzhou, China. Firstly, regression models are developed to explore the transfer distance of access/egress trips during peak hours. Then, the catchment area is delineated for collecting feeder-related environment variables. Finally, several negative binomial regressions are employed to examine the relationships between feeder-related built environment and taxi-metro integrated use during peak hours on weekdays. The results reveal that (a) people prefer to use taxi as feeder mode for metro access during morning peak hours and for metro egress during evening peak hours; (b) the transfer distance of taxi-metro integrated use is about 3.8 km; (c) higher mixed land use generates more taxi-metro integrated use during evening peak hours. Higher proportion of residential land use attracts more taxi-metro integrated use for metro access during morning peak hours. Those findings will help transport planners to develop tailored land-use interventions to improve transit accessibility and promote the sustainable multimodal travel.

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Section: Te Feeder Mode Formentioning

confidence: 99%

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

Chen

Hao

et al. 2023

Journal of Advanced Transportation

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“…Shared micro-mobility services have been proliferating worldwide in the past few years, including dockless escooters, dockless and docked bikes and e-bikes, etc. Due to their environmentally-friendly and flexible characteristics, shared micro-mobility services have been widely used to supplement public transport by dealing with first and last-mile problems (Qin et al, 2018;Romm et al, 2022). However, there are also many problems that are brought by them, such as disorderly placement of vehicles, and overproduction of facilities leading to resource wasting.…”

Section: Introductionmentioning

confidence: 99%

Modeling the choice of shared micro-mobility services using XGBoost machine learning algorithm

Ren¹,

Zhao²,

Mansourian³

2023

AGILE GIScience Ser.

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Abstract. In recent years, shared micro-mobility services (e.g., bikes, e-bikes, and e-scooters) have been popularized at a rapid pace worldwide, which provide more choices for people’s short and medium-distance travel. Accurately modeling the choice of these shared micro-mobility services is important for their regulation and management. However, little attention has been paid to modeling their choice, especially with machine learning. In this paper, we explore the potential of the XGBoost model to model the three types of shared micro-mobility services, including docked bike, docked e-bike, and dockless e-scooter, in Zurich, Switzerland. The model achieves an accuracy of 72.6%. Moreover, the permutation feature importance is implemented to interpret the model prediction. It is found that trip duration, trip distance, and difference in elevation present higher feature importance in the prediction. The findings are beneficial for urban planners and operators to further improve the shared micro-mobility services toward sustainable urban mobility.

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Optimizing intermodal commuting by way of detours and breaks: Evidence of micromobility users in France

Moinse,

L'Hostis

2024

Journal of Transport Geography

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Differences in first-mile and last-mile behaviour in candidate multi-modal Boston bike-share micromobility trips

Cited by 12 publications

References 8 publications

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

Modeling the choice of shared micro-mobility services using XGBoost machine learning algorithm

Optimizing intermodal commuting by way of detours and breaks: Evidence of micromobility users in France

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