# Understanding the Operational Efficiency of Bicycle-Sharing Based on the Influencing Factor Analyses: A Case Study in Nanjing, China

Authors: Beibei Hu; Yunfeng Gao; Jiechen Yan; Yue Sun; Yang Ding; Ji Bian; Xianlei Dong; Huijun Sun
Journal: Journal of Advanced Transportation
 (2021)

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## Abstract

With the expansion of urban scale and the growth of urban population, the bicycle-sharing system has been greatly helping grease the wheels of convenience and diversity of citizens' travel. Nevertheless, there are a set of additional problems, including imbalance of supply and demand at rental stations and low utilization of system operation, which have disrupted the travel experience of consumers, the profitability of businesses, and the coordination of government. In this study, we take Nanjing as an example to measure the operating efficiency of bicycle-sharing by calculating the capacity utilization rate (CUR). Afterwards, based on the IC card data of bicycle-sharing users, we statistically analyzed the traffic inflow and outflow at rental stations. Besides, this paper discusses the factors influencing the use of bicycle-sharing, by introducing the method of sampling stepwise regression into the study of rental situation and geographical environment. The results are as follows: (1) demand for bicycle-sharing is higher on weekdays than on weekends, especially during the morning and evening rush hours. (2) The daily average capacity utilization rate of bicycle-sharing is less than 0.08, indicating that the system is not efficient enough. During morning and evening rush hours, only less than 10% of rental stations have high inflow and outflow, and there is an imbalance of inflow and outflow for the same rental station at different times of the day. (3) The stepwise regression results show that the inflow and outflow of bicycle-sharing rental stations are mainly affected by the distribution of traffic, education, entertainment, medical, and other functional zones near the stations. These findings could provide relevant government departments and enterprises with strategies and suggestions for the efficient and healthy operation of the urban bicycle-sharing system.


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## Body

## 1. Introduction

As a part of public transportation, bicycle-sharing meets the traffic needs of urban life and provides a feasible way for short-distance commuting, which has a profound impact on urban transportation and residents' travel. That transportation mode first appeared in Europe and was introduced in China's big cities such as Beijing, Wuhan, and Hangzhou in 2007. In those cities, relevant government departments or enterprises have set up bicycle-sharing rental stations in populated areas, e.g., residential quarter, business zones, service, and transportation areas to provide commuting services for residents and charge consumers according to their travel time [1, 2]. Furthermore, due to the development of modern communication and Internet technology, the operation and management of bicycle-sharing have been gradually improved and widely promoted to other cities. For instance, a few years later, more than 200 cities in China, including Nanjing, Chengdu, and Chongqing, adopted this novel mode of transportation [2]. Indeed, bike-sharing is seen by travelers in China as an environmentally friendly, flexible, and inexpensive mode of public transportation that optimizes urban public transportation systems and contributes to the evolution to a low-carbon and green lifestyle.Bicycle-sharing not only makes people’s short-distance trip feasible but also improves the efficiency of the urban public transportation system. Studies show that bicycle-sharing could relieve the traffic pressure of buses [3]. In comparison with taxi, it enables people to reach their destinations faster in terms of short trip, especially in the densely-populated and road-clogged sections [4, 5]. However, with more and more public bikes on the road, bicycle-sharing have also given rise to some social problems like unreasonable distribution of rental stations in cities, lack of cycling safety protection, and incomplete supporting laws and policies [6, 7]. It is often difficult to lease or return the bicycle when there is a heavy traffic [8]. Despite the fact that the utilization rate of bicycle-sharing is relatively high during rush hours in the morning and evening, but it is lower overall, because there is a significant “tidal” phenomenon of bicycle-sharing using.For the sake of solving these problems, some scholars studied the bicycle-sharing’s travel mode from the perspective of travelers’ behavior, such as the frequency changing of residents using bicycle-sharing, users’ travel purpose, the spatiotemporal distribution of rental station [9], the renting tide ratio of rental stations, and the turnover rate of pile sites [10]. It is found that residents use bicycle-sharing twice a day on average, mainly for commuting and shopping, and the properties of land use would have a significant impact on the demand of rental stations [9]. At the same time, traffic travel was generally influenced by different factors, such as spatial, temporal, and environmental ones [11–13]. Some scholars also researched the demand law and the use of bicycle-sharing from the perspective of influential factors. For example, they used the graph convolutional neural network model to actively learn the relationship hidden in rental stations and accurately predict the hourly demand of each rental station in the network [14]. By combining the demand for bike-sharing trips with external factors that influence the use of bike-sharing, scientists could estimate the potential demand for bike-sharing at different spatial and temporal dimensions [15]. The demand of bicycle-sharing is affected by a series of external factors, including time periods (rush hours in the morning and evening, workdays or not) [16], traffic at around rental stations [17], weather and climate conditions [18], properties of land use in the vicinity [19, 20], geographical environment (such as terrain) [19], population density [20], and rental stations’ locations [21] (such as the distance from the city center [20]).The imbalance of supply and demand in bicycle-sharing market is also a common concern, which mainly expresses in the excessive redundancy of bicycle-sharing at rental stations [22], the mismatching of inflow and outflow [23], the excessive fluctuation of demand at rental stations in different time dimensions [24], etc. On the one hand, scholars solved the problem by optimizing the schedule of bicycle-sharing system [25, 26] or enhancing the accuracy of bicycle-sharing positioning [27, 28]. For example, they built a mixed integer linear programming (MILP) model with minimum total cost and optimized the search algorithm to realize the substitution and overall schedule of multitype bicycle-sharing at rental stations [29]. Or, they innovated the dynamic positioning method of bicycle-sharing and predicted the number of bicycle-sharing left and users arriving at rental stations and so on, in order to optimize the vehicle scheduling path and realize the optimal allocation of the system [30, 31]. On the other hand, some scholars also established a bicycle-sharing network model from the perspective of system to explore the network structure characteristics of bicycle-sharing [32, 33] and optimized the system’s network flow from the perspective of structure to balance the supply and demand of bicycle-sharing [23, 34].Previous researchers have studied the travel characteristics, network structure characteristics, and the imbalance of supply and demand of bicycle-sharing and made great achievements. However, there is still little research on the operational efficiency on bicycle-sharing, which is time-varying and affected by various factors. In this paper, the time-varying law of the operational efficiency of bicycle-sharing is calculated and analyzed based on the statistical analysis of users’ travel characteristics. At the same time, a sampling regression model is proposed to quantify the key factors affecting the demand of bicycle-sharing, providing theoretical support for balancing the supply and demand and optimizing the management of urban bicycle-sharing market.This study is organized as follows: in Section2, the research data and methods are introduced. In Section 3, the analysis is provided in terms of the bicycle-sharing users’ characteristics, the operational efficiency, and the impact of urban POI distribution on its demand. In the last section, we summarize and highlight our key findings and further provide policy advise to relevant government and businesses.


## 2. Data and Methodology

### 2.1. Data

The research data obtained from Nanjing Bicycle-sharing Co. Ltd. include two parts: one is the lease and return records of bicycle-sharing in Nanjing from March 1, 2016, to March 31, 2016, each of which contains six fields: users’ account, users’ ID number, the lease station’s number, lease time, the return station’s number, and return time, as shown in Table1; the other is the bicycle-sharing rental stations’ information (a total of 641 rental stations), containing six fields: area name, the rental station’s number, the rental station’s name, rental station address, longitude, and latitude, as shown in Table 2.Table 1
Data fields and descriptions of bicycle-sharing user’s leasing and returning.
Data fieldExampleData fieldExampleID_FACENJHX00168946LEASE_TIME2016/3/9 17 : 38 : 15CERT_ID32010519870328XXXXRT_NID12161LEASE_NID11016RT_TIME2016/3/9 17 : 58 : 13Table 2
Data fields and descriptions of bicycle-sharing rental stations.
Data fieldExampleData fieldExampleAdministrative districtGulou districtLEASE_AddressXikang roadLEASE_NID011016Longitude118.7704LEASE_NameEast gate of hohai universityLatitude32.0611After data cleaning, outliers and irrational data are removed. Among them, outliers refer to the data with missing ID number or abnormal date of birth; irrational data refer to the data that the return time earlier than the lease time. The study area of this paper is six urban areas of Nanjing, China, including Gulou District, Jianye District, Xuanwu District, Yuhua District, Qinhuai District, and Qixia District, and the data with rental stations outside the six urban areas are deleted. Finally, 2,705,161 valid records are collected.


### 2.2. Methodology

#### 2.2.1. Measuring the Capacity Utilization Rate

Capacity utilization rate is an important indicator reflecting the operational demand and efficiency of bicycle-sharing. In essence, the operational efficiency of urban bicycle-sharing is the reflection of scale and technical efficiency, that is, the service level of bicycle-sharing demand can be satisfied under the condition that the service capacity of bicycle-sharing is determined. In other words, with the same investment of bicycle-sharing, the higher the satisfaction degree of bicycle-sharing demand is, the higher the operational efficiency will be; otherwise, the lower the operational efficiency will be. Capacity utilization rate of bicycle-sharing directly affects the utilization of public traffic resources and plans, operation, and management of the bicycle-sharing system, which is used to measure the operational efficiency of bicycle-sharing [35]. The capacity utilization rate (fh) is calculated as per the following equation:(1)fh=∑ti/TiN=∑fihN,where ti is the time period that the i-th bicycle-sharing is used, that is, the time interval from leasing to returning; Ti is the service time of the i-th bicycle-sharing, including the load time plus idle time; N is the number of bicycle-sharing; and fih is the capacity utilization rate of the i-th bicycle-sharing.When the contribution weight of each bicycle-sharing to the capacity utilization rate is the same, we can calculate the average capacity utilization rate (fh) using equation (1). When the contribution weight is different, we use equation (2) to calculate the weighted average capacity utilization rate (Fh) as follows:(2)Fh=∑ti∑Ti=∑wifih,where wi is the weight, wi=Ti/∑Ti. In this study, fh=Fh because Ti of each bicycle-sharing is equal. The larger the value of Fh, the longer the cumulative time for bicycle-sharing to be used and the shorter the idle time, indicating that bicycle-sharing can meet more travel demands and has a higher operational efficiency; otherwise, the operational efficiency is lower.


#### 2.2.2. Multifactor Influencing Model of Bicycle-Sharing

Next, after calculating the travel demand, we build a variable selection model based on Spike-slab sparse function and stepwise regression [36, 37] to study the relationship between the travel demand of bicycle-sharing and the properties of urban land use in different districts, which is mainly divided into two parts:First, the travel demand of bicycle-sharing rental stations (including the inflow and outflow of bicycle-sharing) is influenced by the properties of land use (POI distribution) around the rental stations [16]. We count the number of different functional zones in a circular buffer zone with a radius of one kilometer centered on the bicycle-sharing rental station, such as supermarkets, banks, and bus stations, according to the POI industry classification of Baidu Map and the geographic position (GPS) of rental stations. We take 17 functional zones divided by Baidu Map as the first-level indicators in the process of constructing the model. Then, we match the functional zones using keywords and count the distribution of the second-level functional zones (122 in total) around the bicycle-sharing rental stations. The classification of functional zones is shown in the Table 3.Table 3
Indicators of bicycle-sharing using.
First-level indicatorSecond-level indicatorEnterpriseFactory mine, company, gardening, parkShoppingConvenience store, supermarket, shopping mall, market, home appliance and digital, home building materials, shopTrafficSubway station, airport, service area, port, bus station, railway station, gas station, bridge, toll station, parking lot, coach stationEducationAdult education, college and university, science museum, research institution, overseas study agency, training institution, parent-child education, special education school, library, primary school, kindergarten, middle schoolFinanceATM, pawnshop, investment and finance management, credit union, bankHotelApartment hotel, express hotel, star hotelBeautyHairdressing, manicure, beauty, body careTourist attractionMuseum, zoo, scenic spot, garden, Church, aquarium, cultural relic and historic site, amusement park, arboretumCateringTeahouse, cake and dessert, bar, coffee house, foreign restaurant, snack, Chinese restaurantAuto serviceCar inspection field, car beauty, auto parts, car repair, car sales, car rentalReal estateOffices, dormitory, uptownLife serviceNewsstand, funeral service, lottery sales outlet, pet service, real estate agency, WC, public service, household service, ticket office, communications office, graphic printing shop, maintenance point, logistics company, laundry, post-office, photo studioCultural mediumRadio and television, art gallery, cultural center, journalism publication, art troupe, exhibition hallEntertainmentKTV, cinema, resort, troupe, theater, farmhouse, internet cafe, bath and massage, leisure square, play placeMedicalEmergency center, CDC, nursing home, physical examination organization, drugstore, clinic, special hospital, general hospitalExerciseExtreme sports site, fitness center, sports stadiumGovernment agencyParty group, welfare institution, government, public security agency, administrative unit, foreign-related institution, political education institution, central agencyWe record the observations of the number of 122 second-level functional zones at different bicycle-sharing rental stations as a matrix (X=xij). Meanwhile, we calculate the daily average demand (daily inflow and daily outflow) of bicycle-sharing at each rental station and named yi, where i=1,2,…,641 refers to bicycle-sharing rental stations, and j=1,2,…,122 refers to the second-level functional zones (POI). The functional relationship between the daily average demand of bicycle-sharing at each rental station (dependent variable) and the distribution of properties of land use in the area (i.e., the distribution of functional zones and independent variable) is shown in equation (3) as follows:(3)yi=α+β1xi,1+β2xi,2+⋯+β122xi,122+εi,where i=1,2,…,641, α is the intercept term, ε is the residual term, and βj is the regression coefficient of the j-th variable in the regression equation. For convenience, we will consider this model as inflow model and outflow model in the following description if yi refers to daily inflow and outflow of bicycle-sharing at rental stations, respectively.Second, we build a variable selection model based on Spike-slab sparse function and stepwise regression to solve equation (3). Ordinary OLS cannot estimate the influence coefficients of various independent variables on the dependent variable accurately, because equation (3) has many independent variables and there is multicollinearity among the variables. At the same time, we are inclined to keep factors as many as possible in the model unless the variable does not affect the demand of bicycle-sharing. The solution steps are as follows:Step 1: obtain a sample of independent variable (X) by random sampling based on the Spike-slab sparse function, denoted asXγStep 2: construct a stepwise regression model, usingXγ to regress the dependent variable (y), and get the regression results at the specified significance level (the significance level in this paper is 0.05);Step 3: repeat Step 1 and Step 2 enough times and average all regression results that pass the test to obtain the estimated values of all parameters in equation (3).Here, are the specific steps. First, we record the coefficients of independent variable (X) as a column vector (β=βj). We build γ=γj according to β, where γj=0 if βj=0; otherwise γj=1 if βj≠0. We usually can define γ based on the Bernoulli distribution, just as shown in equation (4) as follows:(4)γ∼pjγj1−pj1−γj,j=1,2,…,122,where pj can be set a prior subjectively. For example, we can define pj=m/n when we would like the expected number of independent variables to be m (in this paper m=45) for each stepwise regression model, where n is the total number of independent variables, that is 122. We obtain a priori γ by sampling according to equation (4). Then, we select the corresponding variable (xj) with βj≠0 according to γj=1 and record it as a set (Xγ), which is the independent variable sample of the current stepwise regression.Besides, we build a stepwise regression model and estimate the values of the parameters in model (y=fXγ) to obtain the estimated values of the regression coefficients (β and α) corresponding to the current Xγ. The stepwise regression method can ensure that the variables retained in the model are independent variables that have a significant effect on the dependent variables and eliminate multicollinearity.Finally, we repeat sampling enough times (such as 10,000 times) to ensure model convergence. We record the regression parameters of thei-th stepwise regression as φi=α,βi, and then we can obtain a series of fitting results φi. We calculate the mean of all regression coefficients and use it as the final estimated coefficients of independent variables, that is, φ¯=∑i=1Nφi/N. Therefore, the model of the relationship between the inflow and outflow of bicycle-sharing at rental station (i) and the properties of land use of rental station (i) is shown in equation (5) as follows:(5)y^i=α¯+β¯1xi,1+β¯2xi,2+⋯+β¯122xi,122.


## 2.1. Data

The research data obtained from Nanjing Bicycle-sharing Co. Ltd. include two parts: one is the lease and return records of bicycle-sharing in Nanjing from March 1, 2016, to March 31, 2016, each of which contains six fields: users’ account, users’ ID number, the lease station’s number, lease time, the return station’s number, and return time, as shown in Table1; the other is the bicycle-sharing rental stations’ information (a total of 641 rental stations), containing six fields: area name, the rental station’s number, the rental station’s name, rental station address, longitude, and latitude, as shown in Table 2.Table 1
Data fields and descriptions of bicycle-sharing user’s leasing and returning.
Data fieldExampleData fieldExampleID_FACENJHX00168946LEASE_TIME2016/3/9 17 : 38 : 15CERT_ID32010519870328XXXXRT_NID12161LEASE_NID11016RT_TIME2016/3/9 17 : 58 : 13Table 2
Data fields and descriptions of bicycle-sharing rental stations.
Data fieldExampleData fieldExampleAdministrative districtGulou districtLEASE_AddressXikang roadLEASE_NID011016Longitude118.7704LEASE_NameEast gate of hohai universityLatitude32.0611After data cleaning, outliers and irrational data are removed. Among them, outliers refer to the data with missing ID number or abnormal date of birth; irrational data refer to the data that the return time earlier than the lease time. The study area of this paper is six urban areas of Nanjing, China, including Gulou District, Jianye District, Xuanwu District, Yuhua District, Qinhuai District, and Qixia District, and the data with rental stations outside the six urban areas are deleted. Finally, 2,705,161 valid records are collected.


## 2.2. Methodology

### 2.2.1. Measuring the Capacity Utilization Rate

Capacity utilization rate is an important indicator reflecting the operational demand and efficiency of bicycle-sharing. In essence, the operational efficiency of urban bicycle-sharing is the reflection of scale and technical efficiency, that is, the service level of bicycle-sharing demand can be satisfied under the condition that the service capacity of bicycle-sharing is determined. In other words, with the same investment of bicycle-sharing, the higher the satisfaction degree of bicycle-sharing demand is, the higher the operational efficiency will be; otherwise, the lower the operational efficiency will be. Capacity utilization rate of bicycle-sharing directly affects the utilization of public traffic resources and plans, operation, and management of the bicycle-sharing system, which is used to measure the operational efficiency of bicycle-sharing [35]. The capacity utilization rate (fh) is calculated as per the following equation:(1)fh=∑ti/TiN=∑fihN,where ti is the time period that the i-th bicycle-sharing is used, that is, the time interval from leasing to returning; Ti is the service time of the i-th bicycle-sharing, including the load time plus idle time; N is the number of bicycle-sharing; and fih is the capacity utilization rate of the i-th bicycle-sharing.When the contribution weight of each bicycle-sharing to the capacity utilization rate is the same, we can calculate the average capacity utilization rate (fh) using equation (1). When the contribution weight is different, we use equation (2) to calculate the weighted average capacity utilization rate (Fh) as follows:(2)Fh=∑ti∑Ti=∑wifih,where wi is the weight, wi=Ti/∑Ti. In this study, fh=Fh because Ti of each bicycle-sharing is equal. The larger the value of Fh, the longer the cumulative time for bicycle-sharing to be used and the shorter the idle time, indicating that bicycle-sharing can meet more travel demands and has a higher operational efficiency; otherwise, the operational efficiency is lower.


### 2.2.2. Multifactor Influencing Model of Bicycle-Sharing

Next, after calculating the travel demand, we build a variable selection model based on Spike-slab sparse function and stepwise regression [36, 37] to study the relationship between the travel demand of bicycle-sharing and the properties of urban land use in different districts, which is mainly divided into two parts:First, the travel demand of bicycle-sharing rental stations (including the inflow and outflow of bicycle-sharing) is influenced by the properties of land use (POI distribution) around the rental stations [16]. We count the number of different functional zones in a circular buffer zone with a radius of one kilometer centered on the bicycle-sharing rental station, such as supermarkets, banks, and bus stations, according to the POI industry classification of Baidu Map and the geographic position (GPS) of rental stations. We take 17 functional zones divided by Baidu Map as the first-level indicators in the process of constructing the model. Then, we match the functional zones using keywords and count the distribution of the second-level functional zones (122 in total) around the bicycle-sharing rental stations. The classification of functional zones is shown in the Table 3.Table 3
Indicators of bicycle-sharing using.
First-level indicatorSecond-level indicatorEnterpriseFactory mine, company, gardening, parkShoppingConvenience store, supermarket, shopping mall, market, home appliance and digital, home building materials, shopTrafficSubway station, airport, service area, port, bus station, railway station, gas station, bridge, toll station, parking lot, coach stationEducationAdult education, college and university, science museum, research institution, overseas study agency, training institution, parent-child education, special education school, library, primary school, kindergarten, middle schoolFinanceATM, pawnshop, investment and finance management, credit union, bankHotelApartment hotel, express hotel, star hotelBeautyHairdressing, manicure, beauty, body careTourist attractionMuseum, zoo, scenic spot, garden, Church, aquarium, cultural relic and historic site, amusement park, arboretumCateringTeahouse, cake and dessert, bar, coffee house, foreign restaurant, snack, Chinese restaurantAuto serviceCar inspection field, car beauty, auto parts, car repair, car sales, car rentalReal estateOffices, dormitory, uptownLife serviceNewsstand, funeral service, lottery sales outlet, pet service, real estate agency, WC, public service, household service, ticket office, communications office, graphic printing shop, maintenance point, logistics company, laundry, post-office, photo studioCultural mediumRadio and television, art gallery, cultural center, journalism publication, art troupe, exhibition hallEntertainmentKTV, cinema, resort, troupe, theater, farmhouse, internet cafe, bath and massage, leisure square, play placeMedicalEmergency center, CDC, nursing home, physical examination organization, drugstore, clinic, special hospital, general hospitalExerciseExtreme sports site, fitness center, sports stadiumGovernment agencyParty group, welfare institution, government, public security agency, administrative unit, foreign-related institution, political education institution, central agencyWe record the observations of the number of 122 second-level functional zones at different bicycle-sharing rental stations as a matrix (X=xij). Meanwhile, we calculate the daily average demand (daily inflow and daily outflow) of bicycle-sharing at each rental station and named yi, where i=1,2,…,641 refers to bicycle-sharing rental stations, and j=1,2,…,122 refers to the second-level functional zones (POI). The functional relationship between the daily average demand of bicycle-sharing at each rental station (dependent variable) and the distribution of properties of land use in the area (i.e., the distribution of functional zones and independent variable) is shown in equation (3) as follows:(3)yi=α+β1xi,1+β2xi,2+⋯+β122xi,122+εi,where i=1,2,…,641, α is the intercept term, ε is the residual term, and βj is the regression coefficient of the j-th variable in the regression equation. For convenience, we will consider this model as inflow model and outflow model in the following description if yi refers to daily inflow and outflow of bicycle-sharing at rental stations, respectively.Second, we build a variable selection model based on Spike-slab sparse function and stepwise regression to solve equation (3). Ordinary OLS cannot estimate the influence coefficients of various independent variables on the dependent variable accurately, because equation (3) has many independent variables and there is multicollinearity among the variables. At the same time, we are inclined to keep factors as many as possible in the model unless the variable does not affect the demand of bicycle-sharing. The solution steps are as follows:Step 1: obtain a sample of independent variable (X) by random sampling based on the Spike-slab sparse function, denoted asXγStep 2: construct a stepwise regression model, usingXγ to regress the dependent variable (y), and get the regression results at the specified significance level (the significance level in this paper is 0.05);Step 3: repeat Step 1 and Step 2 enough times and average all regression results that pass the test to obtain the estimated values of all parameters in equation (3).Here, are the specific steps. First, we record the coefficients of independent variable (X) as a column vector (β=βj). We build γ=γj according to β, where γj=0 if βj=0; otherwise γj=1 if βj≠0. We usually can define γ based on the Bernoulli distribution, just as shown in equation (4) as follows:(4)γ∼pjγj1−pj1−γj,j=1,2,…,122,where pj can be set a prior subjectively. For example, we can define pj=m/n when we would like the expected number of independent variables to be m (in this paper m=45) for each stepwise regression model, where n is the total number of independent variables, that is 122. We obtain a priori γ by sampling according to equation (4). Then, we select the corresponding variable (xj) with βj≠0 according to γj=1 and record it as a set (Xγ), which is the independent variable sample of the current stepwise regression.Besides, we build a stepwise regression model and estimate the values of the parameters in model (y=fXγ) to obtain the estimated values of the regression coefficients (β and α) corresponding to the current Xγ. The stepwise regression method can ensure that the variables retained in the model are independent variables that have a significant effect on the dependent variables and eliminate multicollinearity.Finally, we repeat sampling enough times (such as 10,000 times) to ensure model convergence. We record the regression parameters of thei-th stepwise regression as φi=α,βi, and then we can obtain a series of fitting results φi. We calculate the mean of all regression coefficients and use it as the final estimated coefficients of independent variables, that is, φ¯=∑i=1Nφi/N. Therefore, the model of the relationship between the inflow and outflow of bicycle-sharing at rental station (i) and the properties of land use of rental station (i) is shown in equation (5) as follows:(5)y^i=α¯+β¯1xi,1+β¯2xi,2+⋯+β¯122xi,122.


## 2.2.1. Measuring the Capacity Utilization Rate

Capacity utilization rate is an important indicator reflecting the operational demand and efficiency of bicycle-sharing. In essence, the operational efficiency of urban bicycle-sharing is the reflection of scale and technical efficiency, that is, the service level of bicycle-sharing demand can be satisfied under the condition that the service capacity of bicycle-sharing is determined. In other words, with the same investment of bicycle-sharing, the higher the satisfaction degree of bicycle-sharing demand is, the higher the operational efficiency will be; otherwise, the lower the operational efficiency will be. Capacity utilization rate of bicycle-sharing directly affects the utilization of public traffic resources and plans, operation, and management of the bicycle-sharing system, which is used to measure the operational efficiency of bicycle-sharing [35]. The capacity utilization rate (fh) is calculated as per the following equation:(1)fh=∑ti/TiN=∑fihN,where ti is the time period that the i-th bicycle-sharing is used, that is, the time interval from leasing to returning; Ti is the service time of the i-th bicycle-sharing, including the load time plus idle time; N is the number of bicycle-sharing; and fih is the capacity utilization rate of the i-th bicycle-sharing.When the contribution weight of each bicycle-sharing to the capacity utilization rate is the same, we can calculate the average capacity utilization rate (fh) using equation (1). When the contribution weight is different, we use equation (2) to calculate the weighted average capacity utilization rate (Fh) as follows:(2)Fh=∑ti∑Ti=∑wifih,where wi is the weight, wi=Ti/∑Ti. In this study, fh=Fh because Ti of each bicycle-sharing is equal. The larger the value of Fh, the longer the cumulative time for bicycle-sharing to be used and the shorter the idle time, indicating that bicycle-sharing can meet more travel demands and has a higher operational efficiency; otherwise, the operational efficiency is lower.


## 2.2.2. Multifactor Influencing Model of Bicycle-Sharing

Next, after calculating the travel demand, we build a variable selection model based on Spike-slab sparse function and stepwise regression [36, 37] to study the relationship between the travel demand of bicycle-sharing and the properties of urban land use in different districts, which is mainly divided into two parts:First, the travel demand of bicycle-sharing rental stations (including the inflow and outflow of bicycle-sharing) is influenced by the properties of land use (POI distribution) around the rental stations [16]. We count the number of different functional zones in a circular buffer zone with a radius of one kilometer centered on the bicycle-sharing rental station, such as supermarkets, banks, and bus stations, according to the POI industry classification of Baidu Map and the geographic position (GPS) of rental stations. We take 17 functional zones divided by Baidu Map as the first-level indicators in the process of constructing the model. Then, we match the functional zones using keywords and count the distribution of the second-level functional zones (122 in total) around the bicycle-sharing rental stations. The classification of functional zones is shown in the Table 3.Table 3
Indicators of bicycle-sharing using.
First-level indicatorSecond-level indicatorEnterpriseFactory mine, company, gardening, parkShoppingConvenience store, supermarket, shopping mall, market, home appliance and digital, home building materials, shopTrafficSubway station, airport, service area, port, bus station, railway station, gas station, bridge, toll station, parking lot, coach stationEducationAdult education, college and university, science museum, research institution, overseas study agency, training institution, parent-child education, special education school, library, primary school, kindergarten, middle schoolFinanceATM, pawnshop, investment and finance management, credit union, bankHotelApartment hotel, express hotel, star hotelBeautyHairdressing, manicure, beauty, body careTourist attractionMuseum, zoo, scenic spot, garden, Church, aquarium, cultural relic and historic site, amusement park, arboretumCateringTeahouse, cake and dessert, bar, coffee house, foreign restaurant, snack, Chinese restaurantAuto serviceCar inspection field, car beauty, auto parts, car repair, car sales, car rentalReal estateOffices, dormitory, uptownLife serviceNewsstand, funeral service, lottery sales outlet, pet service, real estate agency, WC, public service, household service, ticket office, communications office, graphic printing shop, maintenance point, logistics company, laundry, post-office, photo studioCultural mediumRadio and television, art gallery, cultural center, journalism publication, art troupe, exhibition hallEntertainmentKTV, cinema, resort, troupe, theater, farmhouse, internet cafe, bath and massage, leisure square, play placeMedicalEmergency center, CDC, nursing home, physical examination organization, drugstore, clinic, special hospital, general hospitalExerciseExtreme sports site, fitness center, sports stadiumGovernment agencyParty group, welfare institution, government, public security agency, administrative unit, foreign-related institution, political education institution, central agencyWe record the observations of the number of 122 second-level functional zones at different bicycle-sharing rental stations as a matrix (X=xij). Meanwhile, we calculate the daily average demand (daily inflow and daily outflow) of bicycle-sharing at each rental station and named yi, where i=1,2,…,641 refers to bicycle-sharing rental stations, and j=1,2,…,122 refers to the second-level functional zones (POI). The functional relationship between the daily average demand of bicycle-sharing at each rental station (dependent variable) and the distribution of properties of land use in the area (i.e., the distribution of functional zones and independent variable) is shown in equation (3) as follows:(3)yi=α+β1xi,1+β2xi,2+⋯+β122xi,122+εi,where i=1,2,…,641, α is the intercept term, ε is the residual term, and βj is the regression coefficient of the j-th variable in the regression equation. For convenience, we will consider this model as inflow model and outflow model in the following description if yi refers to daily inflow and outflow of bicycle-sharing at rental stations, respectively.Second, we build a variable selection model based on Spike-slab sparse function and stepwise regression to solve equation (3). Ordinary OLS cannot estimate the influence coefficients of various independent variables on the dependent variable accurately, because equation (3) has many independent variables and there is multicollinearity among the variables. At the same time, we are inclined to keep factors as many as possible in the model unless the variable does not affect the demand of bicycle-sharing. The solution steps are as follows:Step 1: obtain a sample of independent variable (X) by random sampling based on the Spike-slab sparse function, denoted asXγStep 2: construct a stepwise regression model, usingXγ to regress the dependent variable (y), and get the regression results at the specified significance level (the significance level in this paper is 0.05);Step 3: repeat Step 1 and Step 2 enough times and average all regression results that pass the test to obtain the estimated values of all parameters in equation (3).Here, are the specific steps. First, we record the coefficients of independent variable (X) as a column vector (β=βj). We build γ=γj according to β, where γj=0 if βj=0; otherwise γj=1 if βj≠0. We usually can define γ based on the Bernoulli distribution, just as shown in equation (4) as follows:(4)γ∼pjγj1−pj1−γj,j=1,2,…,122,where pj can be set a prior subjectively. For example, we can define pj=m/n when we would like the expected number of independent variables to be m (in this paper m=45) for each stepwise regression model, where n is the total number of independent variables, that is 122. We obtain a priori γ by sampling according to equation (4). Then, we select the corresponding variable (xj) with βj≠0 according to γj=1 and record it as a set (Xγ), which is the independent variable sample of the current stepwise regression.Besides, we build a stepwise regression model and estimate the values of the parameters in model (y=fXγ) to obtain the estimated values of the regression coefficients (β and α) corresponding to the current Xγ. The stepwise regression method can ensure that the variables retained in the model are independent variables that have a significant effect on the dependent variables and eliminate multicollinearity.Finally, we repeat sampling enough times (such as 10,000 times) to ensure model convergence. We record the regression parameters of thei-th stepwise regression as φi=α,βi, and then we can obtain a series of fitting results φi. We calculate the mean of all regression coefficients and use it as the final estimated coefficients of independent variables, that is, φ¯=∑i=1Nφi/N. Therefore, the model of the relationship between the inflow and outflow of bicycle-sharing at rental station (i) and the properties of land use of rental station (i) is shown in equation (5) as follows:(5)y^i=α¯+β¯1xi,1+β¯2xi,2+⋯+β¯122xi,122.


## 3. Results

### 3.1. Overview

We describe the age and duration distribution of bicycle-sharing users in each administrative district as shown in Figure1. From the age distribution of users, there is no significant difference in different administrative districts. The users are mostly between 25 and 55 years old, among whom 25–40 years old account for the highest proportion. It can be seen that the main bicycle-sharing users are office workers (25–55 years old), while teenagers and the elderly use less. From the duration of users, it is roughly the same in different administrative districts. The time in each trip mainly lasts for 0–20 minutes. With the increase of the use duration, the proportion decreases gradually. For example, 57.5% of users in Jianye District spend less than 10 minutes on bicycle-sharing. People tend to use bicycle-sharing for short trips, which has more advantages in urban short trips of residents.Figure 1
Distribution of (a) age and (b) use duration of bicycle-sharing users.
(a)(b)Figure2 shows the daily variation tendency of people using bicycle-sharing to travel on workdays and weekends. On the whole, as shown in Figure 2(a), people are affected by on and off duty on workdays, for which the demand of bicycle-sharing is higher during rush hours in the morning and evening and shows obvious travel peaks. In contrast, on weekends, travel peaks are delayed and not obvious during the daytime (compared with peak hours in the morning and evening on workdays). Compared with workdays, its travel demand is higher during off-peak hours (mainly refers to 10 : 00–17 : 00). This is because on workdays, people mostly work and rarely travel during this time. On weekends, people spend more time on outdoor activities such as shopping and entertainment, leading to a greater demand for bicycle-sharing. From 22 : 00 to 6 : 00 the next day, people travel less; thus, there is relatively low demand for bicycle-sharing.Figure 2
Daily variation tendency of bicycle-sharing travel demand in (a) different time periods and (b) different administrative districts.
(a)(b)The daily variation tendency of bicycle-sharing travel demand in the six major urban areas of Nanjing is roughly the same, but there is a difference in total demand, as shown in Figure2(b). For example, there are 176 bicycle-sharing rental stations in Jianye District and 192 in Gulou District, where people have a greater demand for bicycle-sharing. There are 61 bicycle-sharing rental stations in Xuanwu District and 41 in Qixia District, where people have a relatively low demand for bicycle-sharing.We count the inflow and outflow of bicycle-sharing rental stations during rush hours in the morning (7 : 00–10 : 00) and evening (17 : 00–19 : 00). The results are shown in Table4 and Figure 3. During rush hours in the morning and evening, the total outflow of rental stations is greater than the total inflow. The proportion of rental stations where the inflow is greater than or equal to the outflow is not significantly different from that of rental stations where the outflow is greater than the inflow, and the latter is slightly higher. Only 5.15% of inflow and outflow of the rental stations is relatively high during rush hours in the morning (the sum of the inflow and outflow is greater than 150), and there is only 6.86% during rush hours in the evening. For the same rental station, there is a difference in the inflow and outflow of almost all stations. It can be seen that there is an imbalance of supply and demand for bicycle-sharing at rental stations, which will also make bicycle-sharing system inefficient.Table 4
Statistical analysis of rental stations’ inflow and outflow during rush hours.
PeriodTotal inflowTotal outflowProportion of rental stations where inflow ≥ outflow (%)Proportion of rental stations where outflow > inflow (%)Morning peak182321977744.1555.85Evening peak219952343649.3050.70Figure 3
Inflow and outflow of rental stations during rush hours in the (a) morning and (b) evening.
(a)(b)


### 3.2. Daily Variation Tendency of Capacity Utilization Rate

The capacity utilization rate of bicycle-sharing reflects its operational efficiency, which is not only related to the number of bicycle-sharing at rental stations but also significantly related to the population density around the rental stations and the travel environment (such as temperature). We calculate the average daily capacity utilization rate of bicycle-sharing in the six major urban areas of Nanjing from March 1 to March 31 using equation (1), and the results are shown in Figure 4. On the whole, the average daily capacity utilization rate of bicycle-sharing in all urban areas is roughly the same and is relatively low overall. This indicates to a certain extent that the cumulative using time of bicycle-sharing is short, most shared bicycles are not being used for a long time, and the operational efficiency of the system is low.Figure 4
Daily variation tendency of capacity utilization rate of bicycle-sharing.Specifically, the six urban areas are ranked in descending order of the average capacity utilization rate of bicycle-sharing: Jianye District, Qinhuai District, Gulou District, Xuanwu District, Yuhua District, and Qixia District. In particular, the average daily capacity utilization rate of bicycle-sharing declined rapidly on March 8th, which is related to the weather conditions (as shown by the dotted line in Figure4). It can also be seen from Figure 4 that the temperature change in Nanjing in March is roughly the same as the average daily capacity utilization rate change of bicycle-sharing. When the weather gets warmer, people travel more and bicycle-sharing demand increases; when the weather gets colder, people travel less and bicycle-sharing demand decreases, which prove that the travel by bicycle-sharing are affected by weather conditions. People will reduce outdoor activities or travel by a better closed means of transportation instead of by bicycle-sharing due to the bad weather.The number of rental stations and population density in the six major urban areas will also affect the capacity utilization rate of bicycle-sharing. Table5 shows the correlation between the capacity utilization rate and average temperature, number of rental stations, and population density. It can be seen from Table 5 that the number of rental stations and population density are positively correlated with the average capacity utilization rate. In other words, if the number of rental stations is increased in the current situation, the convenience of leasing and returning bicycle-sharing will be improved, and people will increase the use of bicycle-sharing, which can increase the average capacity utilization rate of bicycle-sharing. At the same time, if the population density around the rental stations is increased, the number of bicycle-sharing users and the average capacity utilization rate of bicycle-sharing will also increase.Table 5
Correlation analysis between the capacity utilization rate and factors.
Correlative factorAverage temperatureThe number of rental stationsPopulation densityPearson correlation coefficient0.555∗∗0.787∗∗0.514∗∗P value (two-sided test)0.0010.0000.004Note.∗∗indicates a correlation at the significance level of 0.01.Figure5 shows the daily variation tendency of the average capacity utilization rate of bicycle-sharing in various administrative districts of Nanjing. On the whole, the average capacity utilization rate can be shown obviously in morning peak (7 : 00–10 : 00), midday peak (13 : 00–15 : 00), and evening peak (17 : 00–19 : 00), which reaches the highest point at 9 : 00, 14 : 00, and 18 : 00, respectively. During rush hours in the morning and evening, urban traffic volume increases so that road congestion is serious. People tend to ride bicycle-sharing to go to the bus stations or subway stations which makes the capacity utilization rate of bicycle-sharing relatively high during commuting hours in the morning and evening. During the midday peak, especially between 13 : 00 and 14 : 00, some office workers go out to work, which leads to an increase of traffic volume and use of bicycle-sharing. From 23 : 00 to 6 : 00, the next day, people hardly travel by bicycle-sharing, and the average capacity utilization rate tends to zero.Figure 5
Average capacity utilization rate of bicycle-sharing in different time periods in different urban areas.


### 3.3. The Impact of Urban POI Distribution on the Demand of Bicycle-Sharing

We should determine the number of independent variables (m) in each stepwise regression equation firstly when solving equation (3). On the one hand, with the increase of the number of independent variables (m), the regression model’s goodness of fit (R2) has an increasing trend, as shown in Table 6. But, the time complexity of the variable selection model will increase rapidly, and the multicollinearity of the independent variables will also become serious. Therefore, we subjectively set the number of sampled independent variables to 45 in each stepwise regression based on these considerations. We estimate equation (3) using the daily inflow and outflow data of bicycle-sharing rental stations, respectively. The goodness of fit (R2) for inflow model and outflow model should be 0.318 and 0.306, respectively (Table 6). The error term fluctuates around 0, resulting in passing the ADF test (Table 7). Figures 6(a) and 6(b) compare the difference between the daily inflow and daily outflow observations and the estimates obtained by inflow model and outflow model for all bicycle-sharing rental stations, respectively. It can be seen that the properties of land use around the rental stations can be partially explained by the inflow and outflow of bicycle-sharing, and the model fits relatively well.Table 6
The number of randomly selected variables (k) and the goodness of fit (R2) of regression.
k202530354045R2 of inflow model0.2100.2400.2650.2850.3030.318R2 of outflow model0.2040.2320.2560.2760.2920.306Table 7
ADF test results of the error term.
ModelTest statistic1% critical value5% critical valueP valueInflow model−22.785−3.430−2.8600.000Outflow model−22.414−3.430−2.8600.000Figure 6
Comparison between estimated value of the model and real value.
(a)(b)The estimated parameters of the model are shown in the Tables8 and 9. In fact, most estimates of the coefficients are relatively small, indicating that most functional zones have a weak impact on the inflow and outflow of bicycle-sharing rental stations. Table 10 lists the top 10 functional zones that have the most positive or negative impact on the inflow and outflow of bicycle-sharing rental stations (the absolute value of variable coefficient is the largest). Table 11 lists the top 10 functional zones that have the least positive or negative impact on the inflow and outflow of bicycle-sharing rental stations (the absolute value of variable coefficient is the smallest).Table 8
Parameters and coefficients’ estimated values in inflow model.
ParameterEstimated valueParameterEstimated valueParameterEstimated valueα183.3969β41-6.9867β820.0000β1−2.7587β424.9349β83−7.1934β22.2415β43−9.2338β846.9266β319.2002β447.1613β857.7667β4−5.8607β456.2249β86−11.6402β57.0240β467.4451β87−8.1153β6−5.3885β47−3.2830β8812.2151β76.3915β48−3.9637β897.7834β88.9447β49−3.3035β906.9951β93.6900β506.6822β91−9.3924β10−2.0702β5113.0168β929.0235β11−3.7502β52−23.3411β933.6491β1213.6733β53−5.1993β94−8.0170β136.4144β5410.5969β95−11.4879β14149.9000β555.8249β9645.6391β15−13.2663β56−7.9995β97−7.6196β16−3.3590β572.3723β983.9504β172.8811β58−6.6928β99−3.8184β18−9.4576β595.3987β1008.0614β194.1217β604.1922β101−5.3234β200.0000β613.2082β102−7.6699β21−3.2024β624.9134β103−5.8581β22−33.6326β63−8.4073β104−10.0074β2313.1721β6415.8291β105−8.4513β247.2430β65−7.5476β10627.2642β2521.4611β660.2386β10713.8721β26−4.1276β67−4.1264β108−6.2522β2737.6117β688.5244β1092.7554β28−3.5742β695.4584β1108.4834β29−11.5428β70−5.0782β111−9.8788β30−46.8318β71−3.0379β11235.5544β310.9829β72−10.1715β1139.7980β329.3570β738.9434β114−1.2067β337.7032β74−3.0838β115−6.3799β34−3.2671β75−3.3837β1164.3948β35−5.1777β762.1052β1177.2570β36−8.6607β77−3.8903β118−1.8437β377.8410β78−9.8123β1191.2370β38−8.3185β7918.4531β120−14.0662β393.8894β80−11.1164β1219.0092β40−3.2873β815.0392β122−6.2830Table 9
Parameters and coefficients’ estimated values in the outflow model.
ParameterEstimated valueParameterEstimated valueParameterEstimated valueα282.0370θ41−7.3218θ820.0000θ1−2.7247θ421.9573θ83−7.5080θ2−0.1385θ43−9.5385θ847.0023θ319.8124θ447.4760θ858.4361θ4−6.0150θ456.3188θ86−11.7815θ56.9068θ468.2213θ87−8.3869θ6−4.5390θ47−2.5987θ8812.1632θ76.8820θ48−4.1913θ897.9017θ88.9013θ49−3.2394θ907.3889θ93.5766θ506.9138θ91−9.1555θ10−1.9898θ5112.8864θ927.3187θ11−4.3748θ52−24.1010θ933.6142θ1213.2462θ53−5.1009θ94−8.1516θ137.1805θ5411.0108θ95−12.0123θ14149.6667θ559.8946θ9645.9434θ15−12.9182θ56−8.2992θ97−8.7558θ16−3.3860θ572.2150θ983.6624θ172.9523θ58−6.9695θ99−8.0618θ18−9.3741θ595.4592θ1008.5201θ193.9422θ604.3497θ101−5.5351θ200.0000θ612.8405θ102−8.1175θ21−3.1948θ625.0488θ103−6.3701θ22−33.9958θ63−11.0726θ104−15.0849θ2312.6726θ6416.2320θ105−8.6648θ247.6257θ65−7.7795θ10628.3063θ2524.1864θ66−2.5659θ10713.8782θ26−4.4776θ67−4.3022θ108−6.3529θ2737.9816θ688.8685θ1094.0970θ28−3.9973θ695.6673θ1108.6896θ29−12.3609θ70−5.1174θ111−10.6633θ30−48.0445θ71−1.9990θ11237.8580θ311.4996θ72−10.7866θ11310.2339θ329.4674θ739.0976θ114−0.3291θ337.7704θ74−3.5439θ115−6.6536θ34−5.8020θ75−4.5094θ1162.5143θ35−5.2800θ76−0.6945θ1177.5772θ36−8.1847θ77−3.8980θ118−2.5149θ378.0315θ78−10.0159θ1192.8593θ38−8.4529θ7918.8821θ120−13.8277θ394.0684θ80−11.0471θ1219.8436θ40−2.6815θ815.1629θ122−6.4383Table 10
Top 10 variables with the largest absolute value of coefficient in model.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueTrafficService area150TrafficService area150Coach station−34Coach station−34EducationSpecial education school−47EducationSpecial education school−48Overseas study agency38Overseas study agency38Science museum21Science museum24EntertainmentResort46EntertainmentResort46ExerciseExtreme sports site36ExerciseExtreme sports site38MedicalNursing home27MedicalNursing home28Tourist attractionAquarium-23Tourist attractionAquarium-24EnterpriseGardening19EnterpriseGardening20Table 11
Top 10 variables with the smallest absolute value of coefficient in model.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueLife serviceGraphic printing shop0Life serviceGraphic printing shop0Real estate agency2Real estate agency−1TrafficToll station0TrafficToll station0Auto serviceCar repair0EnterpriseCompany0EducationLibrary1ExerciseSports stadium0ExerciseSports stadium−1EducationLibrary1Government agencyAdministrative unit1HotelStar hotel2Public security agency−2ShoppingHome building materials−2ShoppingHome building materials−2Real estateUptown−2EnterpriseCompany2CateringCake and dessert2It can be seen from Table10 that the functional zones which have a significant impact on the inflow and outflow of bicycle-sharing rental stations are exactly the same. For the same functional zone, the influence difference in the inflow and outflow is small. People choose different trip modes for different trip purposes. The first-level indicators with the significant impact are traffic, education, entertainment, exercise, medical, tourist attraction, and enterprise. The study found that the functional zones with a large demand of bicycle-sharing have the following characteristics: higher population density and mobility, difficulties in parking, and all closely related to residents’ lives. However, in the same functional zone, the impact of different facilities on the inflow and outflow of bicycle-sharing may vary greatly. For example, in the traffic functional zone, the number of service areas has a high positive impact on the inflow and outflow of bicycle-sharing (the coefficient value is positive), while the number of coach stations has a negative impact on it (the coefficient value is negative).From the functional zones that have a small impact on the inflow and outflow of bicycle-sharing at rental stations (Table11), the first-level indicators with few impacts are mainly life service, autoservice, government agency, shopping, and hotel. These functional zones are mainly service-oriented. Although they are closely related to residents’ lives, the population density is small and there are generally no serious parking problems, which lead to the low demand of bicycle-sharing. These functional zones have few impacts on the inflow and outflow of bicycle-sharing, but the same facilities have significantly different effects on the inflow and outflow of bicycle-sharing. For example, the number of real estate agencies in the life service functional zone has a positive impact on the inflow of bicycle-sharing (the coefficient value is positive) and has a negative impact on the outflow (the coefficient value is negative).Then, we discuss the top 10 functional zones that have the most positive and negative impacts on the inflow and outflow of bicycle-sharing rental stations during rush hours in the morning and evening, as shown in Tables12 and 13.Table 12
Top 10 variables of the largest absolute value of coefficient in model during rush hours in the morning.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueGovernment agencyCentral agency15EntertainmentResort13EducationSpecial education school−14EducationSpecial education school−12Overseas study agency9Overseas study agency10Science museum7TrafficCoach station−9TrafficCoach station−10Government agencyCentral agency−8MedicalNursing home8Tourist attractionArboretum−7Emergency center−6Aquarium−6Tourist attractionArboretum8MedicalNursing home7Aquarium−7Emergency center−5EntertainmentFarmhouse−6Life serviceHousehold service5Table 13
Top 10 variables of the largest absolute value of coefficient in model during rush hours in the evening.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueTrafficService area34Government agencyCentral agency16Coach station−−8EducationSpecial education school−15EntertainmentResort12Overseas study agency11EducationSpecial education school−11Science museum8Overseas study agency10EntertainmentResort14MedicalNursing home6Farmhouse−6Tourist attractionAquarium−5TrafficCoach station−10Arboretum−5Tourist attractionArboretum9Life serviceHousehold service5Aquarium−7EnterpriseGardening4MedicalNursing home8It can be seen from Tables12 and 13 that the functional zones that have a significant effect on the inflow of bicycle-sharing at rental stations during rush hours in the morning are consistent with the functional zones that have a significant effect on the outflow of bicycle-sharing at rental stations during rush hours in the evening. On the contrary, the functional zones of the outflow during rush hours in the morning are consistent with that of the inflow during rush hours in the evening. At the same time, we notice that the functional zones regarding education, traffic, medical, tourist attraction, and entertainment are also the ones that have a significant impact on the bicycle-sharing demand model. The impact of different facilities on the inflow and outflow of bicycle-sharing may vary greatly. For example, during rush hours in the evening, in the education functional zone, the number of overseas study agencies has a positive impact on the inflow and outflow of bicycle-sharing (the coefficient value is positive), while the number of special education schools has a negative impact on it (the coefficient value is negative). However, in the same first-level functional zone, the impact of its facilities on the inflow and outflow of bicycle-sharing may be different. For example, the number of arboretums in the functional zone of tourist attraction during rush hours in the morning has a positive impact on the inflow of bicycle-sharing (the coefficient value is positive) and a negative impact on the outflow (the coefficient value is negative). Therefore, we can make a preliminary prediction of the inflow and outflow of bicycle-sharing at rental stations with the distribution of functional zones around the stations, so as to realize a reasonable dispatch of vehicles among the rental stations.


## 3.1. Overview

We describe the age and duration distribution of bicycle-sharing users in each administrative district as shown in Figure1. From the age distribution of users, there is no significant difference in different administrative districts. The users are mostly between 25 and 55 years old, among whom 25–40 years old account for the highest proportion. It can be seen that the main bicycle-sharing users are office workers (25–55 years old), while teenagers and the elderly use less. From the duration of users, it is roughly the same in different administrative districts. The time in each trip mainly lasts for 0–20 minutes. With the increase of the use duration, the proportion decreases gradually. For example, 57.5% of users in Jianye District spend less than 10 minutes on bicycle-sharing. People tend to use bicycle-sharing for short trips, which has more advantages in urban short trips of residents.Figure 1
Distribution of (a) age and (b) use duration of bicycle-sharing users.
(a)(b)Figure2 shows the daily variation tendency of people using bicycle-sharing to travel on workdays and weekends. On the whole, as shown in Figure 2(a), people are affected by on and off duty on workdays, for which the demand of bicycle-sharing is higher during rush hours in the morning and evening and shows obvious travel peaks. In contrast, on weekends, travel peaks are delayed and not obvious during the daytime (compared with peak hours in the morning and evening on workdays). Compared with workdays, its travel demand is higher during off-peak hours (mainly refers to 10 : 00–17 : 00). This is because on workdays, people mostly work and rarely travel during this time. On weekends, people spend more time on outdoor activities such as shopping and entertainment, leading to a greater demand for bicycle-sharing. From 22 : 00 to 6 : 00 the next day, people travel less; thus, there is relatively low demand for bicycle-sharing.Figure 2
Daily variation tendency of bicycle-sharing travel demand in (a) different time periods and (b) different administrative districts.
(a)(b)The daily variation tendency of bicycle-sharing travel demand in the six major urban areas of Nanjing is roughly the same, but there is a difference in total demand, as shown in Figure2(b). For example, there are 176 bicycle-sharing rental stations in Jianye District and 192 in Gulou District, where people have a greater demand for bicycle-sharing. There are 61 bicycle-sharing rental stations in Xuanwu District and 41 in Qixia District, where people have a relatively low demand for bicycle-sharing.We count the inflow and outflow of bicycle-sharing rental stations during rush hours in the morning (7 : 00–10 : 00) and evening (17 : 00–19 : 00). The results are shown in Table4 and Figure 3. During rush hours in the morning and evening, the total outflow of rental stations is greater than the total inflow. The proportion of rental stations where the inflow is greater than or equal to the outflow is not significantly different from that of rental stations where the outflow is greater than the inflow, and the latter is slightly higher. Only 5.15% of inflow and outflow of the rental stations is relatively high during rush hours in the morning (the sum of the inflow and outflow is greater than 150), and there is only 6.86% during rush hours in the evening. For the same rental station, there is a difference in the inflow and outflow of almost all stations. It can be seen that there is an imbalance of supply and demand for bicycle-sharing at rental stations, which will also make bicycle-sharing system inefficient.Table 4
Statistical analysis of rental stations’ inflow and outflow during rush hours.
PeriodTotal inflowTotal outflowProportion of rental stations where inflow ≥ outflow (%)Proportion of rental stations where outflow > inflow (%)Morning peak182321977744.1555.85Evening peak219952343649.3050.70Figure 3
Inflow and outflow of rental stations during rush hours in the (a) morning and (b) evening.
(a)(b)


## 3.2. Daily Variation Tendency of Capacity Utilization Rate

The capacity utilization rate of bicycle-sharing reflects its operational efficiency, which is not only related to the number of bicycle-sharing at rental stations but also significantly related to the population density around the rental stations and the travel environment (such as temperature). We calculate the average daily capacity utilization rate of bicycle-sharing in the six major urban areas of Nanjing from March 1 to March 31 using equation (1), and the results are shown in Figure 4. On the whole, the average daily capacity utilization rate of bicycle-sharing in all urban areas is roughly the same and is relatively low overall. This indicates to a certain extent that the cumulative using time of bicycle-sharing is short, most shared bicycles are not being used for a long time, and the operational efficiency of the system is low.Figure 4
Daily variation tendency of capacity utilization rate of bicycle-sharing.Specifically, the six urban areas are ranked in descending order of the average capacity utilization rate of bicycle-sharing: Jianye District, Qinhuai District, Gulou District, Xuanwu District, Yuhua District, and Qixia District. In particular, the average daily capacity utilization rate of bicycle-sharing declined rapidly on March 8th, which is related to the weather conditions (as shown by the dotted line in Figure4). It can also be seen from Figure 4 that the temperature change in Nanjing in March is roughly the same as the average daily capacity utilization rate change of bicycle-sharing. When the weather gets warmer, people travel more and bicycle-sharing demand increases; when the weather gets colder, people travel less and bicycle-sharing demand decreases, which prove that the travel by bicycle-sharing are affected by weather conditions. People will reduce outdoor activities or travel by a better closed means of transportation instead of by bicycle-sharing due to the bad weather.The number of rental stations and population density in the six major urban areas will also affect the capacity utilization rate of bicycle-sharing. Table5 shows the correlation between the capacity utilization rate and average temperature, number of rental stations, and population density. It can be seen from Table 5 that the number of rental stations and population density are positively correlated with the average capacity utilization rate. In other words, if the number of rental stations is increased in the current situation, the convenience of leasing and returning bicycle-sharing will be improved, and people will increase the use of bicycle-sharing, which can increase the average capacity utilization rate of bicycle-sharing. At the same time, if the population density around the rental stations is increased, the number of bicycle-sharing users and the average capacity utilization rate of bicycle-sharing will also increase.Table 5
Correlation analysis between the capacity utilization rate and factors.
Correlative factorAverage temperatureThe number of rental stationsPopulation densityPearson correlation coefficient0.555∗∗0.787∗∗0.514∗∗P value (two-sided test)0.0010.0000.004Note.∗∗indicates a correlation at the significance level of 0.01.Figure5 shows the daily variation tendency of the average capacity utilization rate of bicycle-sharing in various administrative districts of Nanjing. On the whole, the average capacity utilization rate can be shown obviously in morning peak (7 : 00–10 : 00), midday peak (13 : 00–15 : 00), and evening peak (17 : 00–19 : 00), which reaches the highest point at 9 : 00, 14 : 00, and 18 : 00, respectively. During rush hours in the morning and evening, urban traffic volume increases so that road congestion is serious. People tend to ride bicycle-sharing to go to the bus stations or subway stations which makes the capacity utilization rate of bicycle-sharing relatively high during commuting hours in the morning and evening. During the midday peak, especially between 13 : 00 and 14 : 00, some office workers go out to work, which leads to an increase of traffic volume and use of bicycle-sharing. From 23 : 00 to 6 : 00, the next day, people hardly travel by bicycle-sharing, and the average capacity utilization rate tends to zero.Figure 5
Average capacity utilization rate of bicycle-sharing in different time periods in different urban areas.


## 3.3. The Impact of Urban POI Distribution on the Demand of Bicycle-Sharing

We should determine the number of independent variables (m) in each stepwise regression equation firstly when solving equation (3). On the one hand, with the increase of the number of independent variables (m), the regression model’s goodness of fit (R2) has an increasing trend, as shown in Table 6. But, the time complexity of the variable selection model will increase rapidly, and the multicollinearity of the independent variables will also become serious. Therefore, we subjectively set the number of sampled independent variables to 45 in each stepwise regression based on these considerations. We estimate equation (3) using the daily inflow and outflow data of bicycle-sharing rental stations, respectively. The goodness of fit (R2) for inflow model and outflow model should be 0.318 and 0.306, respectively (Table 6). The error term fluctuates around 0, resulting in passing the ADF test (Table 7). Figures 6(a) and 6(b) compare the difference between the daily inflow and daily outflow observations and the estimates obtained by inflow model and outflow model for all bicycle-sharing rental stations, respectively. It can be seen that the properties of land use around the rental stations can be partially explained by the inflow and outflow of bicycle-sharing, and the model fits relatively well.Table 6
The number of randomly selected variables (k) and the goodness of fit (R2) of regression.
k202530354045R2 of inflow model0.2100.2400.2650.2850.3030.318R2 of outflow model0.2040.2320.2560.2760.2920.306Table 7
ADF test results of the error term.
ModelTest statistic1% critical value5% critical valueP valueInflow model−22.785−3.430−2.8600.000Outflow model−22.414−3.430−2.8600.000Figure 6
Comparison between estimated value of the model and real value.
(a)(b)The estimated parameters of the model are shown in the Tables8 and 9. In fact, most estimates of the coefficients are relatively small, indicating that most functional zones have a weak impact on the inflow and outflow of bicycle-sharing rental stations. Table 10 lists the top 10 functional zones that have the most positive or negative impact on the inflow and outflow of bicycle-sharing rental stations (the absolute value of variable coefficient is the largest). Table 11 lists the top 10 functional zones that have the least positive or negative impact on the inflow and outflow of bicycle-sharing rental stations (the absolute value of variable coefficient is the smallest).Table 8
Parameters and coefficients’ estimated values in inflow model.
ParameterEstimated valueParameterEstimated valueParameterEstimated valueα183.3969β41-6.9867β820.0000β1−2.7587β424.9349β83−7.1934β22.2415β43−9.2338β846.9266β319.2002β447.1613β857.7667β4−5.8607β456.2249β86−11.6402β57.0240β467.4451β87−8.1153β6−5.3885β47−3.2830β8812.2151β76.3915β48−3.9637β897.7834β88.9447β49−3.3035β906.9951β93.6900β506.6822β91−9.3924β10−2.0702β5113.0168β929.0235β11−3.7502β52−23.3411β933.6491β1213.6733β53−5.1993β94−8.0170β136.4144β5410.5969β95−11.4879β14149.9000β555.8249β9645.6391β15−13.2663β56−7.9995β97−7.6196β16−3.3590β572.3723β983.9504β172.8811β58−6.6928β99−3.8184β18−9.4576β595.3987β1008.0614β194.1217β604.1922β101−5.3234β200.0000β613.2082β102−7.6699β21−3.2024β624.9134β103−5.8581β22−33.6326β63−8.4073β104−10.0074β2313.1721β6415.8291β105−8.4513β247.2430β65−7.5476β10627.2642β2521.4611β660.2386β10713.8721β26−4.1276β67−4.1264β108−6.2522β2737.6117β688.5244β1092.7554β28−3.5742β695.4584β1108.4834β29−11.5428β70−5.0782β111−9.8788β30−46.8318β71−3.0379β11235.5544β310.9829β72−10.1715β1139.7980β329.3570β738.9434β114−1.2067β337.7032β74−3.0838β115−6.3799β34−3.2671β75−3.3837β1164.3948β35−5.1777β762.1052β1177.2570β36−8.6607β77−3.8903β118−1.8437β377.8410β78−9.8123β1191.2370β38−8.3185β7918.4531β120−14.0662β393.8894β80−11.1164β1219.0092β40−3.2873β815.0392β122−6.2830Table 9
Parameters and coefficients’ estimated values in the outflow model.
ParameterEstimated valueParameterEstimated valueParameterEstimated valueα282.0370θ41−7.3218θ820.0000θ1−2.7247θ421.9573θ83−7.5080θ2−0.1385θ43−9.5385θ847.0023θ319.8124θ447.4760θ858.4361θ4−6.0150θ456.3188θ86−11.7815θ56.9068θ468.2213θ87−8.3869θ6−4.5390θ47−2.5987θ8812.1632θ76.8820θ48−4.1913θ897.9017θ88.9013θ49−3.2394θ907.3889θ93.5766θ506.9138θ91−9.1555θ10−1.9898θ5112.8864θ927.3187θ11−4.3748θ52−24.1010θ933.6142θ1213.2462θ53−5.1009θ94−8.1516θ137.1805θ5411.0108θ95−12.0123θ14149.6667θ559.8946θ9645.9434θ15−12.9182θ56−8.2992θ97−8.7558θ16−3.3860θ572.2150θ983.6624θ172.9523θ58−6.9695θ99−8.0618θ18−9.3741θ595.4592θ1008.5201θ193.9422θ604.3497θ101−5.5351θ200.0000θ612.8405θ102−8.1175θ21−3.1948θ625.0488θ103−6.3701θ22−33.9958θ63−11.0726θ104−15.0849θ2312.6726θ6416.2320θ105−8.6648θ247.6257θ65−7.7795θ10628.3063θ2524.1864θ66−2.5659θ10713.8782θ26−4.4776θ67−4.3022θ108−6.3529θ2737.9816θ688.8685θ1094.0970θ28−3.9973θ695.6673θ1108.6896θ29−12.3609θ70−5.1174θ111−10.6633θ30−48.0445θ71−1.9990θ11237.8580θ311.4996θ72−10.7866θ11310.2339θ329.4674θ739.0976θ114−0.3291θ337.7704θ74−3.5439θ115−6.6536θ34−5.8020θ75−4.5094θ1162.5143θ35−5.2800θ76−0.6945θ1177.5772θ36−8.1847θ77−3.8980θ118−2.5149θ378.0315θ78−10.0159θ1192.8593θ38−8.4529θ7918.8821θ120−13.8277θ394.0684θ80−11.0471θ1219.8436θ40−2.6815θ815.1629θ122−6.4383Table 10
Top 10 variables with the largest absolute value of coefficient in model.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueTrafficService area150TrafficService area150Coach station−34Coach station−34EducationSpecial education school−47EducationSpecial education school−48Overseas study agency38Overseas study agency38Science museum21Science museum24EntertainmentResort46EntertainmentResort46ExerciseExtreme sports site36ExerciseExtreme sports site38MedicalNursing home27MedicalNursing home28Tourist attractionAquarium-23Tourist attractionAquarium-24EnterpriseGardening19EnterpriseGardening20Table 11
Top 10 variables with the smallest absolute value of coefficient in model.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueLife serviceGraphic printing shop0Life serviceGraphic printing shop0Real estate agency2Real estate agency−1TrafficToll station0TrafficToll station0Auto serviceCar repair0EnterpriseCompany0EducationLibrary1ExerciseSports stadium0ExerciseSports stadium−1EducationLibrary1Government agencyAdministrative unit1HotelStar hotel2Public security agency−2ShoppingHome building materials−2ShoppingHome building materials−2Real estateUptown−2EnterpriseCompany2CateringCake and dessert2It can be seen from Table10 that the functional zones which have a significant impact on the inflow and outflow of bicycle-sharing rental stations are exactly the same. For the same functional zone, the influence difference in the inflow and outflow is small. People choose different trip modes for different trip purposes. The first-level indicators with the significant impact are traffic, education, entertainment, exercise, medical, tourist attraction, and enterprise. The study found that the functional zones with a large demand of bicycle-sharing have the following characteristics: higher population density and mobility, difficulties in parking, and all closely related to residents’ lives. However, in the same functional zone, the impact of different facilities on the inflow and outflow of bicycle-sharing may vary greatly. For example, in the traffic functional zone, the number of service areas has a high positive impact on the inflow and outflow of bicycle-sharing (the coefficient value is positive), while the number of coach stations has a negative impact on it (the coefficient value is negative).From the functional zones that have a small impact on the inflow and outflow of bicycle-sharing at rental stations (Table11), the first-level indicators with few impacts are mainly life service, autoservice, government agency, shopping, and hotel. These functional zones are mainly service-oriented. Although they are closely related to residents’ lives, the population density is small and there are generally no serious parking problems, which lead to the low demand of bicycle-sharing. These functional zones have few impacts on the inflow and outflow of bicycle-sharing, but the same facilities have significantly different effects on the inflow and outflow of bicycle-sharing. For example, the number of real estate agencies in the life service functional zone has a positive impact on the inflow of bicycle-sharing (the coefficient value is positive) and has a negative impact on the outflow (the coefficient value is negative).Then, we discuss the top 10 functional zones that have the most positive and negative impacts on the inflow and outflow of bicycle-sharing rental stations during rush hours in the morning and evening, as shown in Tables12 and 13.Table 12
Top 10 variables of the largest absolute value of coefficient in model during rush hours in the morning.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueGovernment agencyCentral agency15EntertainmentResort13EducationSpecial education school−14EducationSpecial education school−12Overseas study agency9Overseas study agency10Science museum7TrafficCoach station−9TrafficCoach station−10Government agencyCentral agency−8MedicalNursing home8Tourist attractionArboretum−7Emergency center−6Aquarium−6Tourist attractionArboretum8MedicalNursing home7Aquarium−7Emergency center−5EntertainmentFarmhouse−6Life serviceHousehold service5Table 13
Top 10 variables of the largest absolute value of coefficient in model during rush hours in the evening.
Inflow modelOutflow modelFirst-level indicatorSecond-level indicatorCoefficient valueFirst-level indicatorSecond-level indicatorCoefficient valueTrafficService area34Government agencyCentral agency16Coach station−−8EducationSpecial education school−15EntertainmentResort12Overseas study agency11EducationSpecial education school−11Science museum8Overseas study agency10EntertainmentResort14MedicalNursing home6Farmhouse−6Tourist attractionAquarium−5TrafficCoach station−10Arboretum−5Tourist attractionArboretum9Life serviceHousehold service5Aquarium−7EnterpriseGardening4MedicalNursing home8It can be seen from Tables12 and 13 that the functional zones that have a significant effect on the inflow of bicycle-sharing at rental stations during rush hours in the morning are consistent with the functional zones that have a significant effect on the outflow of bicycle-sharing at rental stations during rush hours in the evening. On the contrary, the functional zones of the outflow during rush hours in the morning are consistent with that of the inflow during rush hours in the evening. At the same time, we notice that the functional zones regarding education, traffic, medical, tourist attraction, and entertainment are also the ones that have a significant impact on the bicycle-sharing demand model. The impact of different facilities on the inflow and outflow of bicycle-sharing may vary greatly. For example, during rush hours in the evening, in the education functional zone, the number of overseas study agencies has a positive impact on the inflow and outflow of bicycle-sharing (the coefficient value is positive), while the number of special education schools has a negative impact on it (the coefficient value is negative). However, in the same first-level functional zone, the impact of its facilities on the inflow and outflow of bicycle-sharing may be different. For example, the number of arboretums in the functional zone of tourist attraction during rush hours in the morning has a positive impact on the inflow of bicycle-sharing (the coefficient value is positive) and a negative impact on the outflow (the coefficient value is negative). Therefore, we can make a preliminary prediction of the inflow and outflow of bicycle-sharing at rental stations with the distribution of functional zones around the stations, so as to realize a reasonable dispatch of vehicles among the rental stations.


## 4. Conclusions

In this paper, we analyze the usage status and user group characteristics of urban bicycle-sharing statistically and describe the daily variation tendency of the capacity utilization rate of bicycle-sharing by using the data of the Nanjing bicycle-sharing system in March 2016. Then, we establish the variable selection model based on the Spike-slab sparse function and stepwise regression and investigate the influence of urban land use properties (POI distribution) on the demand of bicycle-sharing. The conclusions are as follows:Bicycle-sharing is mainly used by office workers for short trips. Users are aged between 25 and 55, and the travel time is concentrated between 0 and 20 minutes. The total demand of bicycle-sharing for users during rush hours on the workday morning and evening is higher than that on weekends significantly. Compared with workdays, there are more travel demands during nonpeak hours (10 : 00–17 : 00) at weekends. The overall capacity utilization rate of bicycle-sharing is relatively low, and most of bicycle-sharing have been idle for a long time, which indicate that the system is inefficient. The capacity utilization rate of bicycle-sharing is affected by factors such as the number of rental stations, population density, and travel environment (air temperature). When the population around the rental stations or the number of stations is larger, the capacity utilization rate of bicycle-sharing will be higher, which will help improve the operational efficiency. But for now, it seems that only less than 10% of the bicycle-sharing rental stations have relatively high inflow and outflow during rush hours in the morning and evening. Moreover, the difference between the inflow and outflow at the same rental station is large during rush hours in the morning and evening, which will lead to an imbalance between supply and demand and the low operational efficiency of the bicycle-sharing system.In this paper, we explore the impact of urban land use properties on the inflow and outflow of bicycle-sharing at rental stations and find that functional zones such as traffic, education, entertainment, exercise, and medical zones around the rental stations have a significant impact on the demand of bicycle-sharing for users, while life service, auto service, government agency, shopping, and hotel zones have a relatively weak impact. In particular, the inflow and outflow of rental stations during rush hours in the morning and evening are mainly affected by the functional zones such as government agency, education, traffic, medical, tourist attraction, and entertainment zones near the stations. Therefore, when adding new bicycle-sharing rental stations, we not only consider the geographical distribution of rental stations but also focus on those functional zone facilities that have a significant positive impact on bicycle-sharing demand, such as service area, resort, and overseas study agency, in order to meet the trip demand of the people in the vicinity and improve operational efficiency.Understanding the existing deficiencies and reasons of bicycle-sharing system has a great guiding significance to improve its operational efficiency. We should alleviate the imbalance of supply and demand of bicycle-sharing rental stations, increase the capacity utilization rate, and schedule bicycle-sharing among different rental stations reasonably. On the one hand, the relevant departments should predict the inflow and outflow of rental stations and schedule the vehicles at rental stations reasonably in accordance with the distribution of functional zones around the stations and the variation tendency of people’s demand of bicycle-sharing in different time periods. On the other hand, the relevant institutions or enterprises should perfect policy guidance and strengthen the quality promotion and safety guarantee of cyclists, to provide greater convenience for users and to promote the sustainable development of the urban bicycle-sharing system.



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*Source: 8818548-2021-01-27.xml*