An Association Rule Based Method to Integrate Metro-Public Bicycle Smart Card Data for Trip Chain Analysis

Zhao, De; Wang, Wei; Ong, G. B.; Ji, Yanjie

doi:10.1155/2018/4047682

Cited by 18 publications

(15 citation statements)

References 31 publications

(46 reference statements)

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“…Based on this, multi-ring buffers were created at the 409 metro entrances in ArcGIS. Generally, 300 m is used as the maximum transfer distance for public bike to access/egress metro stations [35][36][37]. Considering that individuals can pick up or drop off free-floating shared bikes close to the metro stations, a total of 25 buffers were constructed from 0 m to 250 m at an interval of 10 m for each metro entrance.…”

Section: Before Designing the Questionnairementioning

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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Section: Before Designing the Questionnairementioning

confidence: 99%

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

Liu

Feng

et al. 2020

Sustainability

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“…Ma et al [36] analyzed the distribution of bikeshare travel distance and travel time of metro-bikeshare users and found that the main function of bikeshare integrated with the metro is commuting, rather than entertainment or exercising. In addition, in Zhao's [37] study, some interesting findings of metro-bikeshare transfer were derived; for instance, the first-mile trips during the morning peak had the same spatial pattern as the last-mile trips during the evening peak. Also, the distance of the first-mile or last-mile bikeshare trips was shorter than that of the other bikeshare trips.…”

Section: Travel Characteristics Of Metro-bikeshare Integrationmentioning

confidence: 99%

“…The Stations profile includes station ID, station name, and the longitude/latitude of the docking station. We applied two matching rules to recognize metro-bikeshare transfer trips: a maximum transfer time of 10 min and a maximum transfer distance of 300 m [37,43]. After applying the two rules of maximum travel time and distance, 12,331 metro-bikeshare transfer trips made by 3836 passengers at 39 transfer pairs were generated.…”

Section: Dependent Variable: Activity Space Of Bikeshare Around the Mmentioning

confidence: 99%

“…This paper aims to explore how the social-demographic, travel-related, and built environment factors affect the activity spaces. Social-demographic and travel-related variables were extracted from the recognized metro-bikeshare transfer data, and the built environment variables were measured by GIS from the 2016 statistical yearbook of Nanjing [37]. The social-demographic and travel-related variables include the proportion of males, the proportion of local residents, the proportions of different age groups, the proportion of travel trips in peak hours, and the average travel distance using the metro.…”

Section: Explanatory Variable: Social-demographic Travel-related Anmentioning

confidence: 99%

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Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

Jin

et al. 2018

Sustainability

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Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

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“…For urban transit system operation at the bus stop, transport infrastructure geometry patterns [15, 16], traffic flow characteristics [17–19], and driver behaviour [20–22] near bus stop are analysed for modelling the travelling time [23–26], dwelling time [27–29], arrival pattern [30, 31], bunching characteristics [32, 33], capacity, and level of service (LOS) [34, 35]. They are modelled through different kinds of data such as automated vehicle location (AVL) data [36, 37] and smart card data [37–39]. Transit travelling delay occurred at the downstream signalised intersection is usually shorter than upstream of it [15], therefore, the impact of near‐side bus stop should be drawn more attention.…”

Section: Introductionmentioning

confidence: 99%