Understanding dockless bike-sharing spatiotemporal travel patterns: Evidence from ten cities in China

Meng, Fanyun; Zheng, Longxi; Ding, Tongqiang; Wang, Zhuorui; Zhang, Yanlin; Li, Wenqing

doi:10.1016/j.compenvurbsys.2023.102006

Cited by 13 publications

(3 citation statements)

References 43 publications

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“…In addition, at middle and low latitudes, LST characteristics have a significant impact on cyclists' travel [45]. Under summer high-temperature conditions, the LST may become the most important factor limiting cyclists' activities, especially because cycling is a popular outdoor sport and cyclists are more likely to experience limitations due to the LSTs [46].…”

Section: Climate Adaptation and Urban Planning Strategies Influence C...mentioning

confidence: 99%

Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior

Qin,

Xu,

Huang

2024

Sustainability

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Cycling is a flexible way of traveling that can promote the development of urban public transportation. Previous studies on the influence of cycling have focused more on the cyclists themselves, ignoring the influences of the features of natural environments, such as streetscapes and land surface temperatures (LSTs), on cycling behavior. Therefore, in this study, street view image data and Landsat 8 imagery were utilized to extract streetscape and LST features; in particular, a framework was established for a single-indicator analysis and a multiple-indicator interaction analysis based on the random forest model with GeoDetector. The model was used to explore the effects of streetscapes and surface temperatures on cycling behavior. The results of this study for the main urban area of Beijing show that (1) high-density buildings and high population activity exacerbated the heat island effect at the city center and certain areas in the east, with the highest LST reaching 46.93 °C. In contrast, the greenery and water bodies in the northwestern and northeastern areas reduced the LST, resulting in a minimum temperature of 11.61 °C. (2) The optimal analysis scale was a 100 m buffer pair, and the regression fitting accuracy reached 0.83, confirming the notable influences of streetscape and LST characteristics on cycling behavior. (3) The random forest (RF) model results show that the importance of LST features and vegetation and sky conditions exceeded 0.07, and a reasonable sky openness and open building ventilation became the first choices for promoting cycling behavior. (4) According to the GeoDetector model, the LST features alone exhibited an importance of more than 0.375 for cycling behavior, while interactions with streetscapes greatly reduced the negative effect of LST on cycling behavior. The interaction between walls and plants reached 0.392, while the interaction between multiple environmental factors and greenery and favorable ventilation counteracted the negative impact of high-temperature heat waves on the residents’ choice of bicycles.

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Section: Climate Adaptation and Urban Planning Strategies Influence C...mentioning

confidence: 99%

Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior

Qin,

Xu,

Huang

2024

Sustainability

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“…Zhao and Li showed that distance is the most crucial influence on transfer trips between metro and home or workplace [23]. A further study shows that 85% of DBS travel distances are within 2 km [14], and travelers prefer to choose busses for a longer travel time [24]. In China, commuters with school-or work-related trips are more likely to use DBS, while female, older, and low-income ones are less like to use it to connect metro [25].…”

Section: Introductionmentioning

confidence: 97%

“…The emerging DBS greatly promotes the use of metros and even alleviates road congestion [6]. In the past several years, there have been numerous studies on the connections between DBS and metro, focusing on the spatiotemporal characteristics [7,8], impact factors analysis [9][10][11], accessibility [12,13], network-based features [14,15], etc. Besides the metro, DBS could also connect with buses for first-and-last mile, which increases bus ridership [16,17].…”

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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Analyzing the micro-mobility patterns of shared dockless bike and e-bike systems through multi-scale complex networks

Shi,

He,

Zhang

2024

Transportation

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Understanding dockless bike-sharing spatiotemporal travel patterns: Evidence from ten cities in China

Cited by 13 publications

References 43 publications

Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior

Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior

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

Analyzing the micro-mobility patterns of shared dockless bike and e-bike systems through multi-scale complex networks

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