Mobility pattern recognition based prediction for the subway station related bike-sharing trips

Liu, Ying; Zhi, Danyue; Sun, Huijun; Qi, Geqi

doi:10.1016/j.trc.2021.103404

Cited by 29 publications

(6 citation statements)

References 54 publications

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“…3 displays the classification results by creating three-dimensional space coordinate systems. Furthermore, some indicators (Lv et al, 2021) are further calculated to find the characteristics of passenger flow from various aspects corresponding to the classified HSR stations. For example, the peak hour coefficient can be used to describe the proportion of peak flow throughout the day, and the equilibrium coefficient represents the ratio of the average hourly HSR passenger flow during peak hours (i.e., 8 a.m. to 10 a.m., and 5 p.m. to 7 p.m.) to that in other periods.…”

Section: Classification Results and Spatiotemporal Characteristics Of...mentioning

confidence: 99%

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Yue

Chen

Feng

et al. 2023

Sustainable Cities and Society

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Section: Classification Results and Spatiotemporal Characteristics Of...mentioning

confidence: 99%

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Yue

Chen

Feng

et al. 2023

Sustainable Cities and Society

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“…Setting the service threshold of metro stations is essential. Existing studies have shown that 500 m is the best fit distance to discuss the connection relationship between the metro and bike‐sharing (Li, Chen, et al, 2021; Lv et al, 2021). Taking this empirical distance as a reference, the number of connecting trajectories at different distances was counted.…”

Section: Datasets and Methodologymentioning

confidence: 99%

Identifying metro station types based on transfer purposes: An application of bike‐sharing data in Xiamen, China

Shao

Jin

et al. 2023

Canadian Geographies / Géographies canadiennes

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With the networking of urban rail transit and the large‐scale development of bike‐sharing, metro and bike‐sharing connection has become the preferred way of daily travel for residents of Xiamen. Current studies mainly identify metro station types based on node and place orientation, lacking behaviour‐based investigation. To fill this gap, this study aims to explore the classification of metro stations based on transfer purposes by combining bike‐sharing and point of interest data in Xiamen, using buffer analysis, kernel density estimation, and DBSCAN clustering algorithm comprehensively. The results indicate the following. (1) Distinct transfer purposes have significant agglomeration characteristics and present poly‐centric spatial pattern, an authentic portrayal of Xiamen's land use function. (2) The heterogeneity of connection flow between different transfer purposes and metro stations is apparent. The distribution of flow and flow direction within the same transfer purpose is also in non‐equilibrium. (3) Based on traffic connection analysis, metro stations are divided into seven types: transportation hub, employment‐oriented, residence‐oriented, job‐housing balance, school‐oriented, traffic‐tourism integration, and business connection types. The obtained results assist in improving the transportation connection environment, perfecting urban land use planning, and enhancing low‐carbon and green travel.

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“…A substantial body of literature examines the integration of DBS and metro transit, spanning diverse topics that include travel behavior of multimodal DBS-metro trips [7], accessibility analyses between DBS and metro networks [8], bike parking capacity surrounding metro stations [9], and forecasting methodologies for DBS-metro transfer demand [10]. Tis literature review concentrates on integrating DBS with metro transit systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification Dockless Bike‐Sharing and Metro Transfer Travelers through Mobility Chain

Li,

Yan,

Tang

et al. 2024

Journal of Advanced Transportation

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The burgeoning dockless bike-sharing system presents a promising solution to the first- and last-mile transportation challenge by connecting trip origins/destinations to metro stations. However, the differentiation between metro passengers and DBS riders, as they belong to distinct systems, hinders the precise identification of DBS-metro transfers. This study introduces an innovative method employing mobility chains to establish spatiotemporal relationships, including spatiotemporal conflicts and similarities, among potential users from both systems. This significantly enhances the precision of user matching. An empirical study in Chengdu validates the method’s increased accuracy and examines travel patterns, yielding the following insights: (1) Introduction of the mobility chain reduces average matched pairs by 28.27% and improves accuracy by 18.36%. The addition of spatial-temporal similarity further boosts accuracy by 19.32%. (2) Median distances for DBS-metro access and egress transfers are approximately 950 meters. Short trips of 650–750 meters are prevalent, while trips exceeding 1.5 kilometers lead passengers to opt for alternative modes. (3) Temporal patterns reveal weekday peaks at 8:00, 9:00, and 17:00. On weekends, transfers are uniformly distributed, mainly within urban areas. Suburban stations exhibit reduced weekend activity. These findings can provide valuable insights for enhancing DBS bicycle redistribution, promoting transportation mode integration, and fostering urban transportation’s sustainable development.

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Mobility pattern recognition based prediction for the subway station related bike-sharing trips

Cited by 29 publications

References 54 publications

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Classification and determinants of high-speed rail stations using multi-source data: A case study in Jiangsu Province, China

Identifying metro station types based on transfer purposes: An application of bike‐sharing data in Xiamen, China

Identification Dockless Bike‐Sharing and Metro Transfer Travelers through Mobility Chain

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