Built environment and public bike usage for metro access: A comparison of neighborhoods in Beijing, Taipei, and Tokyo

Lin, Jen-Jia; Zhao, Pengjun; Takada, Kazuyuki; Li, Shengxiao; Yai, Tetsuo; Chen, Chi-Hao

doi:10.1016/j.trd.2018.05.007

Cited by 93 publications

(28 citation statements)

References 31 publications

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“…A dense network with a high proportion of branches and less signalized intersections is more favored by DBS, contrary to that by the taxi users. This finding is partially consistent with Lin et al [65]'s study of SBBS conducted in Tokyo. Bicyclists may feel safer and more convenient with less vehicular traffic and less waiting time at intersections, thereby promoting DBS as a transfer mode.…”

Section: Discussionsupporting

confidence: 92%

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Chen

2020

Sustainability

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The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

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Section: Discussionsupporting

confidence: 92%

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Chen

2020

Sustainability

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“…A few years ago, only station-based bike-sharing was available, and people must use a smart card to unlock, use, and pay for it, and the associated registration process is overly complicated. But dockless bike-sharing has gradually replaced station-based bike-sharing in numerous Chinese cities [38][39][40][41]. The implementation of smartphone payments has been the norm in today's China, substantially simplifying the previously complicated registration process.…”

Section: Discussionmentioning

confidence: 99%

Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making

et al. 2019

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Transit offers stop-to-stop services rather than door-to-door services. The trip from a transit hub to the final destination is often entitled as the “last-mile” trip. This study innovatively proposes a hybrid approach by combining the data mining technique and multiple attribute decision making to identify the optimal travel mode for last-mile, in which the data mining technique is applied in order to objectively determine the weights. Four last-mile travel modes, including walking, bike-sharing, community bus, and on-demand ride-sharing service, are ranked based upon three evaluation criteria: travel time, monetary cost, and environmental performance. The selection of last-mile trip modes in Chengdu, China, is taken as a typical case example, to demonstrate the application of the proposed approach. Results show that the optimal travel mode highly varies by the distance of the “last-mile” and that bike-sharing serves as the optimal travel mode if the last-mile distance is no more than 3 km, whilst the community bus becomes the optimal mode if the distance equals 4 and 5 km. It is expected that this study offers an evidence-based approach to help select the reasonable last-mile travel mode and provides insights into developing a sustainable urban transport system.

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“…However, a study for Nanjing found that there is no effects of land use variables on the use of metro-bikeshare [15]. In a recent study, Lin et al [27] presented a comparison study to clarity the associations of built environments with shared bike in Beijing, Taipei and Tokyo and found that the associations differ from one another despite their geographical and cultural proximity. This suggests that the use behavior of shared bike may vary across different built environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China

Liu

Feng

et al. 2020

Sustainability

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Promoting a transition in individuals’ travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro–bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro–bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro–bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers’ use frequency of integrated metro–bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.

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Built environment and public bike usage for metro access: A comparison of neighborhoods in Beijing, Taipei, and Tokyo

Cited by 93 publications

References 31 publications

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making

Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China

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