Determining the Optimal Layout Design for Public Bicycle System within the Attractive Scope of a Metro Station

Abstract

Public bicycle acts as a seamless feeder mode in combination with the citywide public transit, as well as a competitor for the inner-city short trips. The primary objective of this study is to address the layout planning of public bicycle system within the attracted scope of a metro station. Based on the land use function, population, and bicycle mode share, bicycle rental stations are divided into three types, namely, the metro station, district station, and resident station, and later the quantity of bicycle facilities in each rental station is estimated. Then, the service stations are selected from these bicycle rental stations to provide the service of periodical bicycle redistribution. An improved immune algorithm is proposed to determine the number and locations of service stations and the optimal route options for the implement of redistributing strategy. Finally, a case study of Nanjing Tianyin Road metro station is conducted to illustrate the proposed model and clarify some of its implementation details.

A model for the layout of bike stations in public bike‐sharing systems

“…Constraint (21) ensures that in each period, the total number of bikes returned to a bike station should not be more than the number of vacant parking lockers that the station can provide. Constraint (22) places a limit on the minimum number of bikes present at each selected bike station at the beginning of operation periods K.…”

“…(ii) The proposed model implements a multi-periodic analysis to meet the fluctuating demand for both bikes and vacant lockers at each station. In contrast, some studies [17,[19][20][21][22] did not implement multi-periodic analysis, so the dynamic process of borrowing and returning bikes of users over time cannot be revealed. (iii) Models from existing studies [16][17][18][19][20] allow that some users' needs for borrowing or returning bikes will not be met and introduce penalty costs in the objective function for uncovered demands.…”

“…Chen et al . addressed the layout planning of a public bike system within the attracted scope of a metro station, in which bike stations are classified into three types (metro station, district station, and resident station); however, their main work was to select service stations from these bike stations to provide services such as investigating instances of theft and vandalism in addition to bike redistribution among stations. Zheng et al .…”

“…In addition, they cannot satisfy the needs for picking up and dropping off bikes from all travel demands and so consider penalty costs for uncovered demands. Chen et al [21] addressed the layout planning of a public bike system within the attracted scope of a metro station, in which bike stations are classified into three types (metro station, district station, and resident station); however, their main work was to select service stations from these bike stations to provide services such as investigating instances of theft and vandalism in addition to bike redistribution among stations. Zheng et al [22] developed a bi-level optimization model involving model spilt and traffic assignment for locating bike rental stations, but it did not implement multi-periodic analysis and numerical tests.…”

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

“…The trip distance between one’s home or office and a transit station is a major barrier to the promotion of transit ridership in large cities [7,8]; a longer distance may encourage the use of a private vehicle rather than transit [9]. Bicycle–transit integration can solve the “last-mile” issue, because cycling can cover larger areas than walking to efficiently feed transit passengers to and from metro stations [9,10,11,12]. To promote both cycling and transit use, bicycle–transit integration has already been advocated by many governments in both developed countries such as the United States, Australia, the Netherlands, and Denmark and developing countries such as Brazil, Colombia, and China [13].…”