Effects of Free-Floating Shared Bicycles on Urban Public Transportation

Cao, Min; Ma, Shangjing; Huang, Mong‐Han; Lv, Guonian; Chen, Min

doi:10.3390/ijgi8080323

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…This fact is in line with the original intention of using shared bicycles to solve the first/last mile transportation problem. The results verify our previous research on the effects of free-floating shared bicycles on urban public transportation [36].…”

Section: Discussion Of Od Flowssupporting

confidence: 89%

“…Moreover, the proportions of various POIs in the areas covered by the top 1000 vectors during the morning rush hour and evening rush hour were calculated, as shown in Figure 13 The results indicate that people who are related to those places have a contrasting migration trend during the morning and evening rush hours, which is consistent with the tidal characteristics of shared bicycles reported in another article [36]. The results indicate that people who are related to those places have a contrasting migration trend during the morning and evening rush hours, which is consistent with the tidal characteristics of shared bicycles reported in another article [36].…”

Section: Discussion Of Od Flowssupporting

confidence: 77%

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Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition

Cao

Cai

et al. 2019

IJGI

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Recently, an increasing number of cities have deployed bicycle-sharing systems to solve the first/last mile connection problem, generating a large quantity of data. In this paper, singular value decomposition (SVD) was used to extract the main features of the cycling flow from the origin and destination (OD) data of shared bicycles in Beijing. The results show that (1) pairs of OD flow clusters can be derived from the pairs of vectors after SVD, and each pair of clusters represents a small part of an area with dockless shared bicycles; (2) the spatial clusters derived from the top vectors of SVD are highly coincident with the hot spot areas in the heatmap of shared bicycles; (3) approximately 30% of the study area accounts for nearly 80% of bike riding; (4) nearly 70% of the clustered area derived from the top 1000 vectors of SVD is associated with subway stations; and (5) the types of point of interest (POI) differ between the origin area and destination area for the clustered area of the top 1000 vectors.However, there is limited research on the cycling flow between origin and destination (OD) pairs for dockless shared bicycles. Research on cycling flow could present the spatial distribution and movements of sharing bicycles on a smaller scale [10]. In this paper, the OD flow is defined as the cycling flow between a pair of OD points. The analysis of the OD flows of taxi traces [11], mobile phones [12,13], and Integrated Circuit (IC) cards [14,15] can help to explore human mobility and location characteristics and provide necessary data for traffic planning, road construction, as well as traffic control and management [16,17]. An analysis of cycling flow between OD pairs is required for the spatial distribution and movements for dockless shared bicycles.Many studies have focused on finding clusters of origin and destination points by considering the attributes and spatial distributions of the OD points [18]. Among them, spatial statistics-based methods such as Moran's I [19], hierarchical clustering methods such as K-nearest-neighbors [20], and density-based clustering methods including Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [21] can be used for clustering origin and destination points. Some studies have proposed many methods for extracting OD clusters using long-distance OD data, such as taxi trips. Bicycles are not suitable for long-distance commuting due to the restrictions of the bicycle itself. Some methods include, for example, the simple line clustering method [22], based on the OD line to find spatial linkage. However, this research attempts to construct an OD matrix to specify the travel demand from origin points to destination points microscopically from the perspective of short-distance commuting.Usually, an OD matrix is a large sparse matrix [23]. A dimensionality reduction method, which is called feature extraction, is necessary on such a large matrix to simplify the data without losing too much information [24]. Many methods are used to mine information from the OD ma...

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Section: Discussion Of Od Flowssupporting

confidence: 89%

Section: Discussion Of Od Flowssupporting

confidence: 77%

Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition

Cao

Cai

et al. 2019

IJGI

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“…The higher incidence of bicycle passengers was connected to areas with high job density and around food vendors. Cao et al (2019) used the kernel density estimation method to analyse the spatial distribution of shared bicycles. From the perspective of spatial distribution, the distribution of shared bicycles correlates with the urban public transport system.…”

Section: Temporal Variables and Spatial Variablesmentioning

confidence: 99%

An environmental benefit analysis of bike sharing in New York City

Chen

Zhang

Coffman

et al. 2022

Cities

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“…Most literatures are dedicated to evaluating the benefits of shared transportation for traffic conditions (Alexander & González, 2015; Li, Hong, & Zhang, 2016; Shmueli, Mazeh, Radaelli, Pentland, & Altshuler, 2015) and exploring travel patterns (Hochmair, 2016; Qian, Zhan, & Ukkusuri, 2015; Xu et al., 2019). The travel patterns of shared taxis differ significantly from the traditional patterns in supplementing distant commuting, while the travel patterns of shared bikes are highly associated with public transport stations (Cao, Ma, Huang, Lü, & Chen, 2019; Shen, Liu, & Chen, 2017).…”

Section: Related Workmentioning

confidence: 99%

A multilayer mobility network approach to inferring urban structures using shared mobility and taxi data

Zhang

Liu

Senousi

2021

Transactions in GIS

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Developing data‐driven approaches to understanding urban structures is important for urban planning. However, it is still challenging to combine different transport datasets into a unified framework and reveal the dynamics of urban structures with the emergence of shared mobility. In this study, we propose two empirical multilayer networks to infer and profile urban structures. First, a temporal network is constructed using traditional taxi data over years to reveal the urban structures. Second, a multimodal network is constructed using shared mobility and traditional taxi data over a year to reveal the urban structures. The proposed networks are tested in New York City using a large volume of shared bike, shared vehicle, and traditional taxi data. The multilayer network centralities and community detection enable us to profile the characteristics of the urban flows and urban structure. The analytical results allow us to acquire a better understanding of urban structures from a multilayer perspective, and also provide a geocomputation framework that is useful for urban and geographic researchers.

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Effects of Free-Floating Shared Bicycles on Urban Public Transportation

Cited by 13 publications

References 38 publications

Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition

Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition

An environmental benefit analysis of bike sharing in New York City

A multilayer mobility network approach to inferring urban structures using shared mobility and taxi data

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