Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations

Yu, Senbin; Liu, Gehui; Yin, Congru

doi:10.1016/j.scs.2021.103162

Cited by 34 publications

(11 citation statements)

References 60 publications

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“…The attribute fields include bicycle ID, locating time, longitude and latitude, and date. Previous studies have shown that the integration of bike‐sharing and metro mainly occurs in peak hours (Bi et al, 2021; Guo & He, 2020), especially in the morning peak hours on weekdays (Yu et al, 2021). Exploring the travel behaviour in regular periods can effectively reflect the overall picture of urban residents' travel.…”

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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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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“…Travelers can rent and return it anywhere via smart phone. Recently, the integrated use of DBS and the metro has become a hot research topic [8,12,29]. Shao et al divided metro stations into seven types based on the characteristics of connection flow between bike-sharing and the metro [65].…”

Section: Integrated Use Of Dbs and The Metromentioning

confidence: 99%

“…Lin et al proposed 50 m as buffer distance, and Xing et al suggested 100 m as the upper bound [27,28]. Yu et al studied the spatiotemporal travel demand of DBS-metro with buffer distances from 30 m to 100 m [29]. Besides DBS, there are other travel modes that are used to transfer to the metro, such as walking, e-bikes, and busses.…”

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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“…Yu et al analyzed DBS accessibility from metro stations by extracting trips originating within 100 meters of any metro entrance. Tey then assessed the reachability of cycling destinations surrounding metro station areas based on this trip data [18]. Liu et al determined that a transfer span of 150 meters represents an acceptable threshold between DBS and metro connectivity.…”

Section: Identifcation Of Potential Dbs-metro Transfer Tripsmentioning

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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Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations

Cited by 34 publications

References 60 publications

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

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

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

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

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