Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China

Li, Shaoying; Zhuang, Caigang; Tan, Zhangzhi; Gao, Feng; Lai, Zhipeng; Wu, Zhifeng

doi:10.1016/j.jtrangeo.2021.102974

Cited by 63 publications

(28 citation statements)

References 46 publications

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“…Secondly, spatial models have never been applied in studies of association between urban greenness and dockless bike sharing usage, hence resulting in the lack of the exploration of the spatially varying impacts of urban greenness on bike sharing usage [4,5]. The main purpose of the research is to explore and compare the spatial associations between eye-level and overhead level greenness, and the usage of dockless bike sharing on weekdays, weekend, and holidays in a center area of Shenzhen, China [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57].…”

Section: Related Workmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

“…Shenzhen is one of the most developed and innovative cities in China, and it also enjoys early, stable, and continuous operation of dockless bike sharing. Therefore, Shenzhen's bike sharing system has become a research focus among bike sharing researchers [2,4,5,39]. To explore the association between urban greenness and dockless bike sharing usage for metro-bike transfer travel, 1 km radii buffers around metro stations (Figure 2a) were applied in this study, based on the framework for inferring the travel purpose of bike sharing users in the gravity model and Bayesian rules proposed by Li et al (2021) [39].…”

Section: Study Areamentioning

confidence: 99%

“…Therefore, Shenzhen's bike sharing system has become a research focus among bike sharing researchers [2,4,5,39]. To explore the association between urban greenness and dockless bike sharing usage for metro-bike transfer travel, 1 km radii buffers around metro stations (Figure 2a) were applied in this study, based on the framework for inferring the travel purpose of bike sharing users in the gravity model and Bayesian rules proposed by Li et al (2021) [39]. Then, the overlapping problem is avoided using the Thiessen-polygon method [4].…”

Section: Study Areamentioning

confidence: 99%

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How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Gao

Tan

et al. 2021

IJGI

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Dockless bike sharing plays an important role in residents’ daily travel, traffic congestion, and air pollution. Recently, urban greenness has been proven to be associated with bike sharing usage around metro stations using a global model. However, their spatial associations and bike sharing usage on public holidays have seldom been explored in previous studies. In this study, urban greenness was obtained objectively using eye-level greenness with street-view images by deep learning segmentation and overhead view greenness from the normalized difference vegetation index (NDVI). Geographically weighted regression (GWR) was applied to fill the research gap by exploring the spatially varying association between dockless bike sharing usage on weekdays, weekends, and holidays, and urban greenness indicators as well as other built environment factors. The results showed that eye-level greenness was positively associated with bike sharing usage on weekdays, weekends, and holidays. Overhead-view greenness was found to be negatively related to bike usage on weekends and holidays, and insignificant on weekdays. Therefore, to promote bike sharing usage and build a cycling-friendly environment, the study suggests that the relevant urban planner should pay more attention to eye-level greenness exposure along secondary roads rather than the NDVI. Most importantly, planning implications varying across the study area during different days were proposed based on GWR results. For example, the improvement of eye-level greenness might effectively promote bike usage in northeastern and southern Futian districts and western Nanshan on weekdays. It also helps promote bike usage in Futian and Luohu districts on weekends, and in southern Futian and southeastern Nanshan districts on holidays.

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Section: Related Workmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

See 2 more Smart Citations

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Gao

Tan

et al. 2021

IJGI

Self Cite

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“…Currently, the rapid development and wide applications of geospatial big data have greatly contributed to the literature in human behavior, spatio-temporal mobility and population distribution, such as ridesourcing and taxi trip records [14][15][16], smart cards dataset [17][18][19], bike sharing GPS records [20][21][22][23][24], cellphone dataset [25,26] and social media data [27][28][29][30][31][32]. Numerous studies have been conducted and have found that diversity was one of the most important characteristics of residents' density distribution and mobility patterns [4].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects

Deng

Gao

et al. 2021

IJGI

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Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban vibrancy. This study aims to investigate the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator. This study first integrated application (App) use and mobility patterns from cellphone data to portray residents’ occupations and evaluate the OMI in Guangzhou. Then, the mechanism of OMI distribution was analyzed with the GeoDetector model. Next, an optimized GeoDetector-based index, interactive effect variation ratio (IEVR) was proposed to quantify the variation between individual and interactive effects of factors. The results showed that land use mixture was the dominating factor, and that land use mixture, building density, floor area ratio, road density affected the OMI distribution directly. Some interesting findings were uncovered by IEVR. The influences of cultural inclusiveness and metro accessibility were less important in factor detector result, while they were found to be the most influential in an indirect way interacting with other built environment factors. The results suggested that both “hardware facilities” (land use mixture, accessibility) and “soft facilities” (cultural inclusiveness) should be considered in planning a harmonious urban employment space and sustainable city.

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Hybrid GA-SVR: An Effective Way to Predict Short-Term Traffic Flow

Tan

Zheng

Huang

et al. 2022

Lecture Notes in Computer Science

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Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China

Cited by 63 publications

References 46 publications

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects

Hybrid GA-SVR: An Effective Way to Predict Short-Term Traffic Flow

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