Measuring the Destination Accessibility of Cycling Transfer Trips in Metro Station Areas: A Big Data Approach

Abstract

Cycling is a green, sustainable, and healthy choice for transportation that has been widely advocated worldwide in recent years. It can also encourage the use of public transit by solving the “last-mile” issue, because transit passengers can cycle to and from transit stations to achieve a combination of speed and flexibility. Cycling as a transfer mode has been shown to be affected by various built environment characteristics, such as the urban density, land-use mix, and destination accessibility, that is, the ease with which cyclists can reach their destinations. However, cycling destination accessibility is loosely defined in the literature and the methods of assessing cycling accessibility is often assumed to be equivalent to walking accessibility using the same decay curves, such as the negative exponential function, which ignores the competitive relationship between cycling and walking within a short distance range around transit stations. In this study, we aim to fill the above gap by measuring the cycling destination accessibility of metro station areas using data from more than three million bicycle-metro transfer trips from a dockless bicycle-sharing program in Shenzhen, China. We found that the frequency of bicycle-metro trips has a positive association with a trip distance of 500 m or less and a negative association with a trip distance beyond 500 m. A new cycling accessibility metric with a lognormal distribution decay curve was developed by considering the distance decay characteristics and cycling’s competition with walking. The new accessibility model outperformed the traditional model with an exponential decay function, or that without a distance decay function, in predicting the frequency of bicycle-metro trips. Hence, to promote bicycle-metro integration, urban planners and government agencies should carefully consider the destination accessibility of metro station areas.

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

“…According to some recent studies, for example, Li, Zhu and Guo [33] set a threshold of 100m around the metro station to recognize DBS transfer trips, and Wu et al [54] considered that DBS pick-up or drop-off within 100 m of metro stations' entrances were DBS-metro integration. What's more, DBS users are more likely to park and fetch bicycles as near as possible to the station entrance, while taxis cannot always make a stop very close to the entrance due to some road parking restrictions.…”

Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City

“…where is the cumulative percentage of cycling trips with a distance of at least , and is a nonnegative parameter representing distance induced decay. This specification has been widely applied, and parameters has been estimated for trips corresponding to different modes and trip purposes ( Iacono et al, 2008 , Iacono et al, 2010 , Wu et al, 2019 ). Building up on the work of Iacono et al (2008) , which divided cycling trips into work, school, shopping and recreation trips and modelled their distance exponential decay respectively, and empirical cycling trip purpose distribution in New York City from survey ( NYCDOT, 2017 ), we determined a combined value for all bike trips should be .…”

Identification of Metro-Bikeshare Transfer Trip Chains by Matching Docked Bikeshare and Metro Smartcards

“…The metro-bikeshare trips were recognized if the dockless shared bikes are located within 100 m around the metro station. Wu et al selected the dockless bikeshare trips within 100 m originated from any metro station entrance and measured the cycling destination accessibility of metro station areas [34]. Ni et al compared the temporal-spatial distribution of two modes (dockless bikeshare and taxi) as first mile/last mile connectors to metros and found out that socio-demographic and built-environment factors impacted their usage [35].…”