1. Introduction
In recent years, bike-sharing has grown significantly in many Chinese cities, as it caters to the public transport policies of convenience, sustainability, and energy saving [
1,
2]. Essentially, bike-sharing is an oriented production-service system (PSS), whereby the ownership of bicycles is retained by providers (e.g., Ofo, Mobike, 99bicycle, and Wisdom-Enjoyed Cycling) who sell the functions of the bikes. Bike-sharing has benefits for short-distance travel and connecting “the last kilometer” in a given city [
3], which is especially evident in the vicinity of rail transit stations. Bike-sharing is a convenient way for residents to travel, but it also suffers from some shortcomings, such as unreasonable bicycle parking and the failure to transfer bikes in time. Thus, a better understanding of the spatiotemporal characteristics of bike-sharing is needed and could provide management and operational support for enterprises and government departments [
4,
5,
6]. Station service areas are especially interesting for their unique characteristics that affect bike-sharing usage.
Studies on bike-sharing mainly focus on demand forecasting [
7,
8,
9,
10], pricing schemes [
11,
12,
13], bike-sharing systems (BSS) [
3,
14,
15], and spatiotemporal characteristics [
16,
17,
18]. Demand forecasting, the preliminary work of bike-sharing, determines the number of shared bikes to some extent. Bike-sharing operation costs comprise the system operation, administration, marketing, and utility costs associated with hardwired stations. Pricing schemes are directly related to passenger flow and operation [
11]. Bike-sharing systems (BSS) are a topic worth studying, especially in terms of user satisfaction [
19], bike-sharing services [
20], rebalancing operations [
21], and the like. Compared with demand forecasting, pricing schemes, and BSS, the spatiotemporal characteristics of bike-sharing usage are fundamental to understand the operation and management of bike-sharing services. While analyzing the interaction between bike-sharing and rail transit, we may also explore the influence of land-use attributes on bike-sharing usage in terms of transit-oriented development (TOD).
The passenger flow [
1] and built environment [
22] are important influences on the spatiotemporal characteristics of bike-sharing usage. The greater the passenger flow, the more shared bikes are used; however, the effects of passenger-flow volume have been rarely studied even though user attributes, travel characteristics, and user preferences have been frequently analyzed. Analysis of the user attributes (gender, age, income, car ownership, etc.) related to bike-sharing was necessary to understand travel demands and improve customer satisfaction [
23]. The travel characteristics (travel time, travel distance, travel purpose, etc.) of bike-sharing are of great importance in terms of demand forecasting and the planning of bike-sharing operations. Giving full consideration to user preferences could effectively increase operation costs.
In regard to the built environment, Tran et al. analyzed the factors influencing the usage flow of a bike-sharing system in Lyon, France, and found that the network density of bike-sharing and the station capacity were plausibly correlated to the bike-sharing usage [
24]. Mateo-Babiano et al. analyzed Brisbane’s CityCycle scheme in Australia [
25]. Inner-city stations near to off-road infrastructure saw the most active bike-sharing usage, with CityCycle more heavily used on weekends for recreational purposes. From the survey data of 90 randomly selected residents, the bicycle fare, existence of separated bicycle lane, bicycle quality, pavement quality, proximity of bicycle stations to bus stops, bicycle training programs, and gender and employment statuses of the respondents significantly influenced public preferences regarding BSS in Mashhad [
26]. Bike-sharing linked to the bus rapid transit system played an important role, and minor changes could improve their multimodality [
27]. Bike-sharing affected the built transportation system [
27]. However, the built transportation system also affected bike-sharing usage. Wang et al. analyzed the effects of nearby businesses and jobs on trips to and from stations using bike-sharing [
28]. Bike-sharing programs were, theoretically, best suited to locations with higher population densities and more destinations that could be easily accessed. Built-environment variables, including station attributes and accessibility, cycling infrastructure, public transport facilities, and land-use characteristics, were all considered in analyzing the spatial correlations of bike-sharing usage between nearby stations [
29].
Clearly, few previous studies have analyzed the spatiotemporal characteristics of bike-sharing usage in terms of passenger flow and the built environment. Rail transit stations are an important part of urban transportation, and the unique characteristics of their service areas impact on bike-sharing usage. This paper aims to analyze the spatiotemporal characteristics of bike-sharing usage around rail transit stations using data from Beijing, China. Specifically, the contributions of this work can be summarized as follows: (i) on the basis of analysis of the influencing factors, a geographic weighted regression (GWR) model is built to capture the particular spatiotemporal characteristics of bike-sharing usage around rail transit stations considering the variables of passenger flow and the built environment; (ii) referring to bike-sharing in Beijing, China as a case study, we analyze the influence of the passenger flow into and out of the stations, land use, bus lines, and road-network characteristics on the bike-sharing usage in terms of time and space.
The remainder of this paper comprises the following:
Section 2 gives the data description;
Section 3 describes the analysis methodology;
Section 4 we give the model effectiveness analysis, and the influence of the passenger flow, land use, bus lines and road-network characteristics on the bike-sharing usage are analyzed; and
Section 5 concludes our work and declares further study.
2. Data
We selected bike-sharing in Beijing, China as our case study. The data observation points were all rail transit stations in Beijing, China, as shown in
Figure 1. Wang et al. studied the attraction range of Beijing rail transit and other modes of transportation, concluding that areas within 500 m of rail transit stations are walkable [
30]. Ji et al. counted the cumulative percentages of “Metro-Bikeshare” and “Bikeshare-Metro” by transfer distance, finding that more than 90% of transfer trips were finished within 300 m [
31]. Accordingly, we used data within a 500 m range around rail transit stations in our work. Bike-sharing usage records, the passenger flow, and the built environment were considered to analyze spatiotemporal characteristics, as described in detail hereafter.
2.1. Bike-Sharing Usage Records
Bike-sharing usage records from 19 April 2018, are shown in
Figure 2. It is clear that the use characteristics of bike-sharing differ by period. We selected the morning peak (8:00~9:00), off peak (12:00~13:00), and evening peak (18:00~19:00) for further analysis in an attempt to ensure comprehensiveness and reliability. The bike-sharing usage records during the morning and evening peaks clearly exceeded those for the off peak, as expected.
We obtained the bike-sharing usage records on a workday (19 April 2018) from four companies (Ofo, Mobike, 99bicycle and Wisdom-Enjoyed Cycling)—a total of 2,272,490 usage records, with Ofo and Mobike accounting for 39.62% and 60.02% of the total, respectively. Specific data information is shown in
Table 1.
Each usage record contained a great deal of information, including the record number, corporate identity, bike ID, record time, rental time, latitude and longitude of the bike lease, longitude and latitude of the bike return, leasing price, and usage status. These data provided the basis for the travel-characteristics analysis of bike-sharing. Data cleaning was needed, because some collected data were obviously illogical. Ultimately, 2,041,720 usage records remained for further analysis, accounting for 89.85% of the original data.
To reduce analysis error, stations reporting fewer than 200 bike-sharing usage records per day were excluded, so that a total of 207 stations were finally studied and analyzed.
2.2. Passenger Flow
Because the passenger flow into and out of rail transit stations is an important influence on the use of bike-sharing, we obtained statistical data concerning the passenger flow into and out of rail transit stations from the metro operating company of Beijing. The date of the statistical passenger flow data, 19 April 2018, was the same as for the bike-sharing usage records. Statistical data for the passenger flow into and out of stations during the morning peak (8:00~9:00) are shown in
Figure 3 and
Figure 4, respectively.
2.3. Built Environment
Bike-sharing usage is also influenced by the built environment around rail transit stations. Many scholars have considered attribute variables of the built environment for studying the use characteristics of bike-sharing around rail transit stations [
25,
29]. The use of bike-sharing does have an interactive relationship with the surrounding built environment. In our work, we select for analysis 14 kinds of attribute variables relating to the built environment within 500 m of a station: the child population density, youth population density, middle-aged population density, aging population density, residential land area, working land area, recreational land area, connecting bus line, collinear bus line, non-motorized lane density, motor-vehicle lane density, number of road intersections, number of vehicle parking spaces, and number of shared bike racks. The specific attribute variables are shown in
Table 2.
5. Conclusions
Bike-sharing greatly increases the convenience of travel for residents, especially when connecting stations and other places. Based on historical bike-sharing usage records, we used a GWR model to analyze the spatiotemporal characteristics of bike-sharing for the entire rail transit network of Beijing, China. This study can be summarized as follows:
The bike-sharing usage around rail transit stations is mainly affected by the passenger flow into and out of stations, land use, bus lines, and road-network characteristics. We built a GWR model to capture the spatiotemporal characteristics of the bike-sharing usage around rail transit stations considering the passenger flow and built environment variables;
From the time perspective, the characteristics of the bike-sharing usage around rail transit stations during the morning and evening peak hours show clear differences. The bike-sharing usage during the morning peak is affected by the passenger flow into the station, working land area, collinear bus lines, and number of road intersections. The bike-sharing usage during evening peak is affected by the passenger flow out of the station, residential land area, connecting bus line, and motor-vehicle lane density;
From the spatial perspective, the bike-sharing usage around rail transit stations has obvious partition characteristics. The bike-sharing usage around rail transit stations near the north fourth ring road of Beijing, is heavily affected by the passenger flow. In the north and south of Beijing, bike-sharing usage is mainly affected by working land area and residential land area. In the Chaoyang district, the bike-sharing usage is more sensitive to connecting bus lines and collinear bus lines. The effect of the number of road intersections is mainly reflected in the northern suburbs, and the effect of the motor-vehicle lane density is mainly reflected in the central and western regions of Beijing.
This work can provide technical support for the operations and management of bike-sharing services while serving as a reference for future studies on the connection between bike-sharing and other transportation modes and the influence of TOD development on bike-sharing. This method can be applied to the analysis of bike-sharing usage in other cities with the appropriate adjustments.
In our work, we only analyzed the spatiotemporal characteristics of bike-sharing usage around rail transit stations in Beijing, China, which was limited by our obtained data. The current work could be extended by obtaining more data from other cities. The spatiotemporal characteristics of bike-sharing usage in multiple cities could be compared, and more findings would be given.