The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou

Li, Shaoying; Lyu, Dijiang; Li, Xiaoping; Tan, Zhangzhi; Gao, Feng; Huang, Guanping; Wu, Zhifeng

doi:10.1016/j.cities.2019.102580

Cited by 85 publications

(57 citation statements)

References 41 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%

“…Multi-collinearity commonly exists in various linear regression models, which may generate misleading and insignificant results. Hence, backward stepwise linear regression was first applied in this study to solve the problem of multi-collinearity and select significant variables with strong explanatory power for the three models [44,53,54]. In addition, sixteen candidate variables were standardized with zero mean standardization to ensure that the mean was 0 and the standard deviation was 1 [53,54].…”

Section: Stepwise Linear Regression and Variable Selectionmentioning

confidence: 99%

“…Hence, backward stepwise linear regression was first applied in this study to solve the problem of multi-collinearity and select significant variables with strong explanatory power for the three models [44,53,54]. In addition, sixteen candidate variables were standardized with zero mean standardization to ensure that the mean was 0 and the standard deviation was 1 [53,54]. Table 3 shows the results of three backward stepwise linear regression models corresponding to weekdays, weekends, and holidays.…”

Section: Stepwise Linear Regression and Variable Selectionmentioning

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

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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: Stepwise Linear Regression and Variable Selectionmentioning

confidence: 99%

Section: Stepwise Linear Regression and Variable Selectionmentioning

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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“…Numerous studies on the relationship between urban rail transit ridership and influencing factors have been conducted [13][14][15]. In a study by Kuby [16], cross-sectional boarding data for 268 stations in nine US cities were collected and analyzed.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices

Zhu

Zeng

Gong

et al. 2021

IJERPH

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With the continuous improvement of the operation line network of urban rail transit, analyzing influencing factors of transfer passenger flow of urban rail transit is critical to improve the transfer demand analysis of urban rail transit. Using data collected from questionnaires, transfer passenger flow surveys and smart cards, this study proposes an approach base on nested logit passenger flow assignment model considering transfer choice behaviours of passengers. The transfer passenger flow at seven transfer stations in Nanjing is obtained. Subsequently, this study investigates the potential influencing factors of transfer passenger flow, including the node degree, geographic location (located in the city center, urban fringe, suburbs or suburban fringe), economic location (distance from the city center) and transportation locations (if it is close to a transportation hub or in combination with the hub) of rail transit transfer stations. The results indicate that a positive correlation between the transfer passenger flow and the node degrees of transfer stations. However, the relationship between transfer passenger flow and the economic, geographic, and transportation locations of transfer stations is not clear. The finding have reference value for the network design of rail transit transfer stations and transfer facilities, and provide reference for the analysis of passenger flow under network operation.

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“…In relation, they are strongly tied to the collection and availability of big data, which are highly uneven across the world and among different cities within the same country (e.g. Becker et al, 2011;Long et al, 2012;Ferreira et al, 2013;Loo and Wang, 2017;Zhang et al, 2017;Zhou et al, 2018;Xu et al, 2019;Zhao and Hu, 2019;Li et al, 2020;Xu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach

et al. 2021

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With the emergence of the Internet of Things (IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model (HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people's eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced (38.4%) and work-oriented (61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people's behavior in urban mobility research.

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The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou

Cited by 85 publications

References 41 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?

Analyzing Influencing Factors of Transfer Passenger Flow of Urban Rail Transit: A New Approach Based on Nested Logit Model Considering Transfer Choices

Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach

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