Understanding spatial-temporal travel demand of private and shared e-bikes as a feeder mode of metro stations

Liu, Siming; Zhang, Fan; Ji, Yanjie; Ma, Xinwei; Liu, Yong; Li, Shuo; Zhou, Xizhen

doi:10.1016/j.jclepro.2023.136602

Cited by 19 publications

(4 citation statements)

References 25 publications

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“…The results showed that the bike catchment areas are positively associated with metro service, frequent morning trips, diverse users and large distances to the city centre and terminal stations, but negatively associated with the density of metro stations. Liu et al [23] developed a photovoltaic gradient booster model to investigate the influence of various factors on the demand for two types of electric bicycles charging stations in metro stations, promoting the integration of electric bicycles and subway transportation. Li et al [24] analysed the reasons for the imbalance in bike sharing supply by comparing the travel patterns of bikes during weekdays and weekends.…”

Section: Bike-sharing-metro Integrationmentioning

confidence: 99%

Investigating the Multiscale Impact of Environmental Factors on the Integrated Use of Dockless Bike-Sharing and Urban Rail Transit

Liu,

Zhao,

Jiang

et al. 2023

PROMTT

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Dockless bike-sharing (DBS) is an effective solution to the “first and last mile” problem in urban transportation. It can be integrated with urban rail transit (URT) to provide passengers with more convenient travel services. This study focuses on the integrated use of DBS and URT in Shenzhen, utilising a multi-buffer zone approach to identify DBS data within URT station catchment areas. By employing ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models, the spatiotemporal heterogeneity of integrated use and its relationship with environmental factors surrounding URT stations were examined. The empirical findings highlight the superiority of the MGWR model in accurately explaining spatial relationships compared to the OLS and GWR models. Furthermore, the study reveals that the impact of built environment factors on integrated use varies during morning and evening peak periods, as well as in terms of access and egress. Specifically, factors such as catering, shopping, companies, residential buildings, bus stops, minor roads, transfer stations and population density were found to influence the integrated use of DBS and URT. These findings not only contribute to the promotion of the DBS-URT integration but also promote the overall development of urban transportation.

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Section: Bike-sharing-metro Integrationmentioning

confidence: 99%

Investigating the Multiscale Impact of Environmental Factors on the Integrated Use of Dockless Bike-Sharing and Urban Rail Transit

Liu,

Zhao,

Jiang

et al. 2023

PROMTT

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“…Electric micromobility vehicles (EMVs) have been shown to be viable, low-cost alternatives for lowering emissions for urban travel (Liao and Correia, 2022;Torkayesh and Deveci, 2021;Vanus and Bilik, 2022). EMVs, such as electric bicycles (e-bikes) or electric mopeds, are suitable for shortand medium-distance travel in urban areas because of their small size and ease of charging through dedicated recharging stations or removable batteries Liu et al, 2023;Zhang et al, 2021Zhang et al, , 2022aZhang et al, , 2022bZhou et al, 2022). E-bikes, which are a subgroup of EMVs, have become increasingly popular in North America and Europe in recent years (Bigazzi and Wong, 2020;de Kruijf et al, 2021).…”

Section: Background and Motivationmentioning

confidence: 99%

Battery swapping demand simulation for electric micromobility vehicles considering multi-source information interaction and behavior decision

Zhang

Lyu

et al. 2023

Journal of Cleaner Production

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“…Besides DBS, there are other travel modes that are used to transfer to the metro, such as walking, e-bikes, and busses. For longer distances, travelers prefer to use e-bikes and busses to connect with metro stations [30,31].…”

Section: Introductionmentioning

confidence: 99%

Exploring Travel Mobility in Integrated Usage of Dockless Bike-Sharing and the Metro Based on Multisource Data

Zhang,

Cui,

Liu

et al. 2024

IJGI

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Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model.

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Understanding spatial-temporal travel demand of private and shared e-bikes as a feeder mode of metro stations

Cited by 19 publications

References 25 publications

Investigating the Multiscale Impact of Environmental Factors on the Integrated Use of Dockless Bike-Sharing and Urban Rail Transit

Investigating the Multiscale Impact of Environmental Factors on the Integrated Use of Dockless Bike-Sharing and Urban Rail Transit

Battery swapping demand simulation for electric micromobility vehicles considering multi-source information interaction and behavior decision

Exploring Travel Mobility in Integrated Usage of Dockless Bike-Sharing and the Metro Based on Multisource Data

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