The spatially varying effects of built environment characteristics on the integrated usage of dockless bike-sharing and public transport

Zhou, Xiao; Dong, Quanhua; Huang, Zhou; Yin, Ganmin; Zhou, Guoqing; Liu, Yu

doi:10.1016/j.scs.2022.104348

Cited by 37 publications

(9 citation statements)

References 59 publications

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“…As can be seen from the formula, the regression coefficients vary with location, so GWR can effectively model spatial non-stationarity. Although GWR is efficient, it implicitly assumes that each independent variable has the same spatial scale, which may not be true in some cases (Mansour et al, 2021; Zhou et al, 2023). Later, a multiscale geographically weighted regression (MGWR) is proposed to allow the relationship between the independent and dependent variables to vary at different spatial scales (Fotheringham et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Huang

Yin

Xia

et al. 2023

Environment and Planning B: Urban Analytics and City Science

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Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning.

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Section: Methodsmentioning

confidence: 99%

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Huang

Yin

Xia

et al. 2023

Environment and Planning B: Urban Analytics and City Science

Self Cite

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“…Transportation facilities, including bus and metro stops, are positively related to DBS usage [9,36]. Zhou et al studied the integrated use of DBS and public transport (bus and the metro) and found that population density, number of workplaces, and number of shopping and eating places plays a key role in the integrated use of DBS and the bus system [47]. The outbreak of COVID-19 significantly impacted the use of DBS, which sharply reduced the use of DBS and changed the distributions of DBS [43,48].…”

Section: Impact Factors Of Dbs Usementioning

confidence: 99%

“…Identifying the integrated DBS and metro trips is the key issue in this study. Generally, integrated trips include two patterns: (1) cycling to a public transport station; and (2) cycling from a public transport station [47]. In this paper, we call the first pattern and second pattern arrival-transfer trips and departure-transfer trips, respectively.…”

Section: Identification Of Integrated Dbs and Metro Tripsmentioning

confidence: 99%

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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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“… 16 Cycle4Change: Bike sharing program promoted by the Ministry of Housing and Urban Affairs, Govt of India, under Smart City Mission during Covid-19 lockdown. Accessed at https://smartnet.niua.org/indiacyclechallenge/ on 27th December 2022 [ 67 ]. discusses integration of public transport with bike-sharing.…”

mentioning

confidence: 99%

The role of urban transport in delivering Sustainable Development Goal 11: Learning from two Indian cities

Mahadevia,

Mukhopadhyay,

Lathia

et al. 2023

Heliyon

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The spatially varying effects of built environment characteristics on the integrated usage of dockless bike-sharing and public transport

Cited by 37 publications

References 59 publications

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

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

The role of urban transport in delivering Sustainable Development Goal 11: Learning from two Indian cities

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