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Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China
Journal of Cleaner Production 244 (2020) 118764
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China Mingzhuang Hua a, b, c, Xuewu Chen a, b, c, *, Shujie Zheng a, Long Cheng d, Jingxu Chen e a
Jiangsu Key Laboratory of Urban ITS, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China c School of Transportation, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China d Department of Geography, Ghent University, Krijgslaan 281 S8, Ghent, 9000, Belgium e Department of Logistics & Maritime Studies, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 January 2019 Received in revised form 16 September 2019 Accepted 5 October 2019 Available online 9 October 2019
In recent years, free-floating bike sharing (FFBS) has been rapidly promoted in China, attracting numerous users and consuming many resources. The extensive supply and inefficient repositioning of FFBS are challenges for sustainable development, and put forward higher requirements for parking planning. This paper estimates the parking demand of all three FFBS companies by combining the journey data of Mobike in Nanjing and the Nanjing FFBS bike survey. Three clustering methods were applied to determine the virtual stations of bike aggregation. The maximum number of bikes in a day is recognized as the parking demand in each virtual station. The results show that more than half of bikes are in low turnover rate, and the management of FFBS should be improved. The K-means method turns out to observe the best clustering result for determining virtual stations. The demand for parking spaces of all FFBS companies in Nanjing is estimated accordingly. In addition, the relations of bicycle supply, repositioning and parking are discussed, showing the impact of parking demand on greenhouse gas (GHG) emissions. The research could help to propose an appropriate plan for meeting FFBS parking demand, and have enlightening sight of emissions reduction and sustainable development in the FFBS services. © 2019 Elsevier Ltd. All rights reserved.
Handling Editor: Zhifu Mi Keywords: Free-floating bike sharing Journey data Parking demand Station planning Nanjing
1. Introduction Free-floating bike sharing (FFBS) is regarded as a new type of bike sharing, which has the peculiarities of sustainable development and environmental protection. Free-floating bikes could be picked up and returned anytime and anywhere, without the limitation of stations or docks. In the second half of 2016, the FFBS services such as Mobike and ofo, were put into operation in China’s first-tier cities. The number of covered cities and operated bikes were rapidly growing because of FFBS’s convenience and popularity. In 2017, FFBS had a total fleet of 23 million bikes, covering 200 cities in China. The total registered users reached 221 million
* Corresponding author. Jiangsu Key Laboratory of Urban ITS, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China. E-mail addresses: [email protected] (M. Hua), [email protected] (X. Chen), [email protected] (S. Zheng), [email protected] (L. Cheng), jingxu. [email protected] (J. Chen). https://doi.org/10.1016/j.jclepro.2019.118764 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
and the total riding distance in the whole year was about 30 billion kilometers in the same year (China Academy of Information and Communications Technology and Beijing Mobike Technology Co., ltd., 2018). Along with FFBS expanding, problems such as over-supply of bikes, the mess caused by disorder parking, the lack of effective repositioning become increasingly serious. The FFBS parking issue has caused widespread concern of the government and society, which involves occupying urban space and affecting traffic order. Especially, the FFBS parking issue has close relations with bicycle supply and repositioning, which involves resources consuming and greenhouse gas (GHG) emission. The sustainable development of FFBS services calls for effective planning and management based on parking analysis. 1.1. Literature review Many studies have suggested different improvements of
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traditional stationed bike sharing from operation and planning, which are crucial parts of emissions reduction in bike sharing service. Zhang et al. (2019a) found that optimal layout of bicycle stations in Tokyo can reduce carbon dioxide (CO2) emissions by 3100e3800 tonnes, while bicycle production and rebalancing would generate vast quantities of emissions. Some authors have investigated the travel patterns of users (Caulfield et al., 2017), the travel demand forecasting (Yang et al., 2016), and the optimal stations location (Wang et al., 2016; Park and Sohn, 2017). Zhang et al. (2015) analyzed bike sharing systems in five Chinese cities to explore the influences of transport planning, system design and business model on sustainability. Kabak et al. (2018) combined different decision-making methods with a geographic information system (GIS) to demonstrate the superiority of the suggested locations. Zhang et al. (2017) suggested that adding a new empty station within 300 m can improve the travel demand at the station, indicating the positive effect of adding parking supply on bike sharing usage. Sun et al. (2017) used spatiotemporally varying measurements to investigate the impacts of environmental characteristics, suggesting that improving bike sharing facilities can increase the usage of bike sharing. With global positioning system (GPS) devices increasingly affordable, the FFBS without docks comes true. The big data mining can help to solve the challenges of system operation and facility construction (Fishman, 2016). Big data of FFBS are mainly GPS data (collected through smart locks and FFBS apps) and journey data. Big data mining of FFBS makes it possible for more effective planning and operation, including parking management and emissions reduction. Free-floating bike sharing (FFBS) is a new kind of bike sharing, so the existing studies are relatively limited. The literature on the FFBS covers issues such as development factors, usage patterns, operation management and emissions reduction. Ma et al. (2018) examined the influence of commercial, political and social factors in addressing the public problems of the FFBS from a collaborative perspective. Shi et al. (2018) employed the social network analysis (SNA) method to investigate the critical factors in FFBS. Shen et al. (2018) adopted spatial autoregressive models to examine the spatiotemporal usage patterns of FFBS in Singapore. Liu et al. (2018) developed a combined model to infer bike distribution in new cities by studying from cities populated with FFBS bikes. On the basis of simulation experiments, Caggiani et al. (2018) constructed a modeling framework for the dynamic repositioning model of FFBS systems. In their study, the journey data was clustered at spatiotemporal scale to form virtual stations. Zhang and Mi (2018) estimated the benefits of FFBS on the GHG emissions in Shanghai. In 2016, FFBS in Shanghai decreased petrol use and CO2 emissions by 8358 and 25,240 tonnes, respectively. The research about the FFBS facility construction is even less, especially for parking facility. Bao et al. (2017) used the trajectories of Mobike in Shanghai to solve the issues on non-motor vehicle lane planning and construction. They suggested the greedy algorithm as the solution methodology by considering finance, construction and efficiency constraints. Based on the partial journey data of Mobike in Beijing provided by the Mobike Big Data Challenge 2017, Deng et al. (2017) divided the riding zones into five types, such as tidal type and one-way type, and then proposed corresponding optimization advice on parking facilities. van Waes et al. (2018) found that the business model of station-based bike sharing is better institutionalized but harder to spread, while that of FFBS can scale up better if geo-fencing technologies are well implemented. 1.2. Objectives and contributions The spatial objects of FFBS research are mainly grids or traffic
analysis zones (TAZ), which cannot describe the travel demand of users accurately. Ai et al. (2018) employed the deep learning approach to address the spatiotemporal dependences, the research subjects of which are grids (4 km 4 km). Reiss and Bogenberger (2015) created a demand model and an operator-based strategy to obtain an optimal distribution of bikes within 40 TAZs of the operating area in Munich. Xu et al. (2018) developed the long shortterm memory neural networks to predict the trip demand at 118 TAZs in Nanjing (the area of each TAZ is several square kilometers). Zhang et al. (2019b) used Mobike trips data to support FFBS electric fence (the uniform cells are 50 m 50 m), while the influence of other companies such as ofo is not considered. Few studies divided the spatial extent intensively based on FFBS travel characteristics, and this paper fills this research gap with the cluster analysis of journey data. FFBS has been rapidly promoted in the past two years, and there is a preliminary exploration on the operation and planning of FFBS. However, there is few targeted researches on FFBS parking and it is difficult to acquire city-scale data for analysis. The free-floating characteristic makes parking problem more complicated than general bike sharing system. It is necessary to accurately and comprehensively understand the parking demand pattern for facility planning. This paper estimates the parking demand of all FFBS companies with the journey data of Mobike in Nanjing and the Nanjing FFBS bike survey. This paper aims to save the operational resources and promote the sustainable development, by optimizing FFBS bicycle supply and repositioning based on parking demand analysis. The contributions of this study include: (a) The service characteristics of FFBS system are explored based on the journey data. (b) This paper accurately estimates the parking demand of FFBS for facility planning, such as electric fences (ECNS Wire, 2017) and parking zones. (c) The results may significantly guide the operation and planning of FFBS system, promoting emissions reduction and resource saving. This paper is divided into five sections. Following this introduction, the second section presents the data sources. The third section describes the methodology used for estimating the parking demand of FFBS. A case study with application and results from Nanjing, China are provided in the fourth section. In the end, the fifth section presents the conclusion of this study. 2. Data sources 2.1. The FFBS in Nanjing At the beginning of 2017, FFBS came into Nanjing. The FFBS systems attract many users because of convenience and stylish appearance. In April 2017, these companies competed to occupy the market and launch a “free-ride” promotion. At the end of July 2017, there were 12 companies in Nanjing, with a total number of 450,000 bicycles and a daily usage of about 2 million trips (Xinhua Daily, 2017). In August 2017, the joint meeting of Nanjing Transportation Bureau, Municipal Public Security Bureau, and Urban Management Bureau banned FFBS enterprises from allocating new bicycles. In the second half of 2017, small businesses shut down, leaving only three big companiesdMobike, ofo and Hellobike. The numbers of bikes of these three companies are relatively close. On the other hand, the rapid expansion of FFBS has led to worries about the accumulation in the hotspots, the mess caused by disordered bikes, and the relative limited management resources. According to the 39th Government Service Newsletter issued by the Nanjing Municipal Service Management Office, during July 2017, the Nanjing Municipal Hotline received 554 complaints about FFBS. Moreover, the complaints continued to increase in the following three months, of which more than 80% were about parking chaos.
M. Hua et al. / Journal of Cleaner Production 244 (2020) 118764
Fig. 1 shows two parking samples, reflecting the different orders of FFBS parking. 2.2. Data description and preprocessing Beijing Mobike Technology Co., Ltd. (one of the world’s largest FFBS operators) provided journey data in Nanjing from September 18th to 24th, 2017 (Monday to Sunday), in which latitude and longitude data was positioned under the world geodetic system (WGS 84). The dataset has 3.64 million journey records, with file size of 603 MB. The fields of the journey data include: journey id, user id, bike id, departure time, longitude of origin, latitude of origin, arrival time, longitude of destination, latitude of destination, date. The errors of the journey data mainly include the fact that 117,724 journey records have no destination positioning. Besides, trip origins or destinations in 424,696 journeys records are outside of the built-up area, while this paper only studies the parking demand in the built-up area. After these two kinds of data were eliminated, 3.09 million journey records were extracted, of which origins and destinations are all located in the built-up area in Nanjing. In order to achieve accurate clustering results with Euclidean distances, coordinate system transformation is needed to convert the latitude and longitude position from the world geodetic system (WGS84) to the WGS84 EASE-1 Grid Global projected coordinate system. WGS84 EASE-1 is the recommended coordinate system in the GIS software, ARCGIS, where EASE stands for Equal Area Scalable Earth. In June 2018, the School of Transportation at Southeast University launched the Nanjing FFBS bike survey. The numbers and integrity of bikes belonging to three enterprises (Mobike, ofo, and Hellobike) were investigated in 104 sites in Nanjing from June 25e26, 2018. The three companies account for the whole market of FFBS in Nanjing since the second half of 2017. The survey results found the bike distribution of these three FFBS companies. There are 3368 FFBS bikes investigated in this survey. 1366 bikes (40.6%) belong to ofo, 1100 bikes (32.7%) belong to Mobike, and 902 bikes (26.8%) belong to Hellobike. 88.2% of investigated ofo bikes are in good condition, the integrity ratio is 92.8% for Mobike, and the integrity ratio is 93.8% for Hellobike. The results show that ofo has an advantage in the number of bikes, while Mobike and Hellobike are better in bike quality. Because the Nanjing government banned the allocation of new FFBS bikes in August 2017, the Nanjing FFBS market has become gradually stabilized since then. So the journey data of Mobike in Nanjing in September 2017 and the Nanjing FFBS bike survey in June 2018 could be used for the combined analysis. Presently each FFBS company operates relatively independently, but comanagement can help to improve the service level of the whole system. Therefore, the bike survey data is a powerful complement
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to the Mobike journey data. The journey data of Mobike can be clustered to obtain the parking demand of Mobike, while FFBS survey results can be used to calculate the proportion of Mobike bikes of three companies in all locations. Combing the two datasets finally helps to analyze the whole parking demand of three FFBS companies in the built-up area of Nanjing. Different sizes of journey data were applied in the three core data analyzing process in this paper. (a) FFBS basic characteristics (using one-week Mobike journey data). (b) Virtual station clustering (using one-day Mobike journey data). (c) Parking facility demand calculation (using oneday Mobike journey data and the Nanjing FFBS bike survey). 3. Methodological framework Considering the complexity of the FFBS system, it is necessary to simplify the parking problem with journey data mining. To achieve this goal, the key is to convert the aggregates of bicycles in certain area into virtual stations. A virtual station is a set of trip origins within the acceptable walking distance. Because of the free-floating characteristic of FFBS, their quantity in each area is consistently changing, so it is difficult to estimate the parking demand directly using their number and location. A more effective method for analyzing user demand, allocating appropriate area and determining virtual stations should be developed. Cluster analysis or clustering is an unsupervised learning classification method which is commonly used in machine learning (Jain et al., 1999). The aim of clustering is to allocate elements of different patterns into a small number of homogeneous groups without any prior information. Considering that the FFBS bikes are free-floating and the trip origins are dispersed, cluster analysis can be used to determine both the location and scale of virtual stations in Nanjing. Each cluster is regarded as the area of corresponding virtual station, the centroid of each cluster can be regarded as the recommended location of this virtual station, and the maximum number of bikes in each cluster can be regarded as the design reference of the scale of the virtual station. The entire procedure of parking demand estimation is outlined in Fig. 2. The destination of last trip and the origin of the next trip of the same bike are mostly coincident even with some exceptions, so the origins of the trips are selected as the research objects of parking demand. Step 2: Select virtual stations candidates with clustering Clustering of journey data includes spatial clustering and spatiotemporal clustering. K-means clustering and density-based clustering are widely used in spatial clustering. Eventually, Kmeans clustering, density-based clustering and spatiotemporal clustering are individually applied to identify virtual stations.
Fig. 1. Parking samples of FFBS in China.
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The best clustering result is selected as the locations for virtual stations. The evaluation criteria of clustering results are silhouette coefficient and Calinski-Harabaz index (CH index). Silhouette coefficient is a measure of how similar an object is to its own cluster compared to other clusters, ranging from 1 to 1. A higher Silhouette coefficient relates to a model with better defined clusters. CH index evaluates the effect of clustering by the density within the cluster and the dispersion between clusters. When it comes to CH index evaluation, the Elbow Method is applied. The Elbow method (Thorndike, 1953) is that a number of clusters should be chosen if adding the number of clusters doesn’t give much better clustering, so the chosen point in the number-index curve likes the “Elbow” on the arm. Fig. 2. Flowchart of parking demand estimation methodology. Step 1: Select data of parking demand
K-means clustering aims to minimize the within-cluster sum of squares (WCSS), which is also the key evaluation criterion of this method (Macqueen, 1967) and suitable for the Elbow Method. Kmeans clustering requires only one parameter, which is the number of clusters, k. The algorithm involves the following steps: (a) Randomly create k initial cluster centroids. (b) Calculate the distances between the elements and the centroids and assign each element to the nearest cluster. (c) Take the arithmetic mean of the elements in each cluster as the new centroid. (d) Repeat step (b) and (c) until the clustering results no longer change. The distance between each element in (b) is Euclidean distance because it considers walking distance which is in accordance with the definition of virtual station. The distance in density-based clustering method and spatiotemporal clustering is also Euclidean distance. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is one of the most common density-based clustering methods. The aim is to make the number of objects contained in a certain area not less than a given threshold (Ester, 1996). DBSCAN has two parameters as inputs, the radius (ε) and the minimum number of points (MinPts). The process of the DBSCAN algorithm adopts the following steps: (a) Find one core point as a cluster, which satisfies the MinPts in the neighborhood of radius ε. (b) Scan the points in the neighborhood from this core point and put these points into the cluster. Repeat this step until there is no point that can meet the requirements. (c) Delete every point in the previous cluster and repeat step (a) and (b) until there are no new core points in the dataset. (d) Points that are not included in any cluster compose the noise set. Taking the temporal patterns of FFBS into account, spatiotemporal clustering is used to figure out virtual stations. The spatiotemporal clustering consists of two layers. The first layer is to aggregate the temporal trends of the journey data, and the second layer is to aggregate the spatial characteristics based on the temporal clustering results. The process of spatiotemporal clustering is outlined as follows: (a) Allocate grids (50 m 50 m) as the initial station service zones, and the centroids of grids as the initial stations. (b) Draw the time curve of the number of bikes in each grid by analyzing the journey data, and then conduct the temporal clustering. The time curve is the time series variation curve of the number of FFBS bikes with a time span of one day. (c) Based on the results of temporal clustering, spatial clustering is performed on the centroids of each temporal cluster, and the virtual stations are finally determined. Step 3: Determine locations for virtual stations
Step 4: Calculate parking demand of virtual stations According to the results of the Nanjing FFBS bike survey referred in Section 2.2, we calculate the proportion of Mobike bikes in all FFBS bikes at investigation locations, that is, Mobike ratio. For example, there are 100 FFBS bikes in an investigation location, 20 of them are Mobike bikes, so the Mobike ratio of this place is 0.2. By taking the average value of each administrative district, the Mobike ratio in each virtual station can be obtained. The maximum value in the time curve of the number of Mobike bikes can be calculated in each virtual station. The parking demand of each virtual station can be evaluated with Mobike ratio and the maximum value of Mobike bikes, which is the foundation of parking facility planning. The formula computing the parking demand of virtual stations is as follows:
n N ¼ ,s,c k
(1)
where N is the parking demand of each virtual station; n is the maximum number of Mobike bikes in the day at this station; k is the Mobike ratio; s is the safety factor. Assume that the number of bikes increases to 1.1 times of the maximum value in an emergency, then s equals 1.1. c is the capacity factor. Assume that parking facility of this virtual station can accommodate 90 percent of the FFBS bikes, then c equals 0.9. Step 5: Recommend facility construction for parking planning The FFBS parking facility involves occupying urban space and financial support, so it must be carried out under the planning arrangement of the government department. Taking into account the FFBS parking planning goals, the locations in high demand should be selected to build parking facilities. And based on the different scales of parking demand, different forms of construction design are recommended. 4. Data analysis and results The mathematical formulation was coded in Spyder using Python and implemented on a workstation with Intel XeonW-2145 CPU @ 3.70 GHZ and 64.0 GB RAM. 4.1. Descriptive analysis After the elementary analysis of the journey data, it is found that Mobike has a total of 3.09 million trips in the built-up area of Nanjing in the week, 665,320 users and 204,087 bikes. For the amount of usage, it has an average daily order of about 440,000 trips, a weekly travel frequency of 5.5 times per person and a daily
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bike turnover rate of 2.5 times per bike. Fig. 3(a) shows that most bicycles of Mobike fleet are in a lowfrequency state. Specifically, more than half of the bikes are used less than twice a day. Most bikes are in low turnover rate, indicating that the operation effectiveness of FFBS should be reinforced, and parking facilities should be improved. In Fig. 3(b), one-third users have at least 5 trips per week, showing that the majority of the FFBS journeys come from the travel demand of these frequent users. The average travel time, the average travel distance, and the average riding speed of Mobike in Nanjing are 11.9 min, 2.2 km, and 11.0 km per hour respectively. Fig. 3(c) and (d) show that 65% of the travel time is less than 10 min, and the proportion of travel distance within 1 km reaches 62%, indicating that FFBS mainly serves shortdistance trips. Fig. 4 presents the spatial distribution of Mobike trips in Nanjing. The major hotspots are distributed in the central area, and the hotspots in the surrounding areas are fewer and mostly located in the vicinity of important business districts, subway stations and higher education institutions. The numbers of daily trips under different weather conditions are summarized in Table 1. The numbers of journeys on sunny and cloudy days are relatively high, while overcast and shower would partially reduce travel demand. There are few trips in bad weather such as heavy rain. The findings show that weather has a significant impact on the quantities of FFBS trips, and users are more inclined to travel in good weather.
4.2. Estimating parking demand of FFBS In order to avoid the effects of the weekend and represent average daily trips, Wednesday was selected to be studied. The origins of Mobike journeys in Nanjing on September 20, 2017 (Wednesday) compose the clustering input dataset. As a result, a total of 428,235 origins are selected. Table 2 and Fig. 5(a) show the results of K-means clustering. The
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silhouette coefficient is the largest when the number of clusters is set as 4000. And according to the Elbow Method of WCSS and CH index, the result of 4000 clusters performs well. In summary, the reasonable number of clusters for K-means clustering is 4000. The results of DBSCAN at different radius are shown in Table 3 and Fig. 5(b). The MinPts for each virtual station is set as 10, that is, at least 10 trips are generated each day within the service area of a virtual station. Setting 10 as the MinPts considers the experience that if the parking demand is less than 10 there is no need to build a station for it, these bikes can park in the place where private bikes used to park. After the MinPts is determined, a series values of radii are selected for comparative analysis. If the clustering evaluation index reaches the best at a certain radius, this radius is taken as the optimal radius. Density-based clustering has no WCSS, so it is replaced here by the CH index. When the radius reaches 20 m, the silhouette coefficient is the largest and the criterion of CH index is satisfied. So the density-based clustering result is the best with the radius of 20 m. When the radius is greater than 50 m, all samples in the city center are merged into one cluster, of which the clustering result is unrealistic. Density-based clustering has no defined cluster centroids as K-means clustering does, so the barycenter of each cluster is seen as the cluster centroid. The results of spatiotemporal clustering are as follows. Temporal clustering: The mean, the variance, the ratio between the number of bikes of day and night, and other indicators of the time curve were calculated, standardized and used as clustering criteria, and K-means clustering is performed. According to the Elbow Method and the silhouette coefficient, it finally forms 3 temporal clusters. The first temporal cluster is the general zone, in which the quantity of bikes remains relatively unchanged. The second cluster shows the feature of “morning-arrive and nightleave”, in which bikes during the daytime is much more than that at night. These zones are represented by the commercial areas and most subway stations. The third cluster shows the feature of “morning-leave and night-arrive”, in which bikes during the
Fig. 3. Operational characteristics of Mobike in Nanjing.
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Fig. 4. Spatial distribution of Mobike trips in Nanjing.
Table 1 Trips in different weather conditions in Nanjing. September 18th-25th, 2017
Weather condition
Number of daily trips (units: 10000 trips)
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Sunny Cloudy Shower Overcast Cloudy Shower Heavy Rain
53.7 52.4 42.8 45.8 55.8 46.7 12.2
Table 2 K-means clustering evaluation results. Number of clusters
Within-cluster sum of squares (WCSS)
Silhouette coefficient
CH index
Running time(s)
1000 2000 3000 4000 5000 6000 7000 8000 9000
18133 7831 4678 3252 2442 1927 1566 1314 1123
0.4440 0.4530 0.4574 0.4605 0.4598 0.4536 0.4521 0.4470 0.4434
731967 845301 941157 1012758 1076417 1134361 1193162 1241539 1287954
2009.3 4956.7 8113.9 10673.4 15557.8 15335.1 20985.3 26316.9 35975.6
daytime is much less than that at night. These zones usually consist of residential areas and a few subway stations. Spatial clustering: K-means clustering is conducted on the grid centroids of the three obtained temporal clusters to determine the virtual stations. The procedure is the same as that in Section 3. As a result, the first temporal cluster generates 2000 virtual stations, the second one generates 500 virtual stations, and the third one generates 1000 virtual stations. Totally, the spatiotemporal clustering generates 3500 virtual stations.
The clustering results of various methods are shown in Table 4. K-means clustering and spatiotemporal clustering both have their advantages and disadvantages. K-means clustering has the largest silhouette coefficient and CH index, which demonstrates it is the most suitable one for matching the distribution of FFBS bikes. However, some places with few FFBS bikes are recognized as clusters because they are in the remote areas, which is a disadvantage of K-means clustering. Spatiotemporal clustering can reveal the temporal mode of the bicycle flows, which might be an
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Fig. 5. Spatial clustering index evaluation.
Table 3 DBSCAN clustering evaluation results. Radius(m)
Number of clusters
Silhouette coefficient
CH index
Noise ratio
Running time(s)
5 10 15 20 25 30 40 50
1762 4022 4775 4777 4462 4057 3211 2347
0.7891 0.4427 0.2032 ¡0.1357 0.1371 0.1478 0.2479 0.3215
25 40 64 97 141 200 373 612
88.6% 65.1% 47.2% 35.6% 27.7% 21.9% 14.1% 9.6%
3.3 3.7 4.3 4.7 5.0 5.5 5.7 6.2
K-means clustering and DBSCAN are both spatial clustering methods, the clustering results under various parameters are shown in Fig. 5. The result of K-means clustering is much better than that of DBSCAN, and the appropriate number of virtual stations is 4000 in spatial clustering.
Table 4 Evaluation of various clustering results. Clustering methods
Number of clusters
Silhouette coefficient
CH index
Noise ratio (%)
K-means clustering DBSCAN Spatiotemporal clustering
4000 4777 3500
0.4605 0.1357 0.0937
1012758 97 28
0.0 35.6 21.5
indicator for the relocation of FFBS. The drawback of spatiotemporal clustering is that the silhouette coefficient and CH index of that are relatively low. When it comes to the parking management of FFBS virtual stations, this paper merely aims to accurately identify the maximum number of bikes that the virtual stations should accommodate. Therefore, K-means clustering is the best choice for estimating FFBS parking demand, the appropriate number of virtual stations is 4000. On Wednesday of the week, almost 100% trip origins and destinations can be located in one of the virtual stations. On other days of the week, about 90% trip origins and destinations can be located in one of the virtual stations. The virtual stations can ensure a high level of travel demand coverage, proving the validity of clustering results. Fig. 6 and Table 5 indicate that the Mobike ratios in Nanjing are unevenly distributed. The Mobike ratios in Gulou, Xuanwu, Qinhuai and Jiangning Districts are high, while those in Jianye, Qixia and Yuhuatai Districts are low. The Mobike ratio within each administrative district is comparatively consistent, and the average Mobike ratio in each district can be regarded as that in each virtual station in this district. The operation fleet of Mobike in Nanjing has 204,087 bikes, of which 156,822 bikes are parked in the virtual stations and 47,280 bikes are parked in remote areas outside the virtual stations. The turnover rate in virtual stations is usually high so more parking space than the current number of bikes is needed. The calculation shows that Mobike requires a total of about 208,000 parking spaces in the virtual stations, so a FFBS bike in the virtual stations requires
approximately 1.3 parking spaces. On the other hand, because the turnover rate in remote areas is low, it is assumed that a FFBS bike in remote areas only needs 1.0 parking space. The built-up areas include both the service area of virtual stations and remote areas. A total of about 255,000 parking spaces are required for Mobike in the built-up area of Nanjing. So the ratio between parking spaces and FFBS bikes in the built-up area of Nanjing is about 1.2, rather than 1.0 or 1.3. Assume that the bike proportion of other FFBS companies in remote areas is basically the same as that of Mobike. Using the maximum number of Mobike bikes and Mobike ratio, it can be calculated that the parking spaces required by all enterprises in the virtual stations are about 796,000. The total number of parking spaces required by all enterprises in the built-up area of Nanjing is about 977,000. According to Chinese design specifications, the area of one bicycle parking space should be 1.5 m2 at least (Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2013). Under the existing FFBS operational condition, the parking space required in Nanjing is 1,466,000 m2. On the basis of clustering, 4000 virtual stations are located in the built-up area of Nanjing. Regarding low-demand parking areas, 1185 virtual stations demand less than 100 parking spaces, 1851 stations demand 100e250 parking spaces. As for high-demand parking areas, 749 stations demand 250e500 parking spaces, and 215 stations demand above 500 parking spaces. In 2017, the Nanjing Urban Management Bureau formulated the “Work Plan for FFBS Parking Management”, and proposed to complete the construction of 1000 non-motor vehicle parking
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Fig. 6. Mobike ratios of survey locations in administrative districts.
Table 5 Mobike ratio and parking demand of FFBS in administrative districts of Nanjing. District
Mobike ratio
SD of Mobike ratio
Number of virtual stations
Number of virtual stations (>250 parking spaces)
Parking demand of virtual stations (units: 10000 parking spaces)
Gulou Xuanwu Qinhuai Jianye Qixia Yuhuatai Jiangning Total
0.289 0.343 0.301 0.192 0.174 0.250 0.313 e
0.16 0.11 0.19 0.11 0.15 0.13 0.16 e
469 464 546 361 642 401 1117 4000
152 101 179 186 195 70 81 964
12.5 8.3 12.6 11.4 14.8 6.7 13.3 79.6
stations. This paper finds that there are 964 virtual stations in the built-up area of Nanjing demanding more than 250 parking spaces. It is recommended to use the research results as a reference for the planning and construction of Nanjing non-motor vehicle parking stations. The specific conditions are shown in Table 5 and Fig. 7. Station locations in Fig. 7 are determined considering the results of cluster analysis, the computed parking demand of virtual stations as well as “Work Plan for FFBS Parking Management”. 4.3. FFBS sustainable development analysis based on parking demand The process of FFBS services is that the enterprises product and supply the bicycles, the users use and park the bicycles because of travel demand, and the bicycles stacked or parked in disorder are repositioned by the enterprises. In the operation process, bicycle supply and repositioning, which are closely related to parking, are the main parts that consume resources and generate GHG emissions. The bicycle supply of FFBS should be constrained by the supply
of parking space, and the FFBS fleet scale in Nanjing should be appropriately reduced to promote sustainable development. According to the data of the Nanjing Urban Management Bureau, Nanjing has 287,000 m2 of public parking resource for non-motor vehicle until 2018. However, the FFBS parking demand in Nanjing is 1,466,000 m2, which is far larger than parking supply. The ratio between parking spaces and FFBS bikes in Nanjing is about 1.2, and the area of one bicycle parking space should be 1.5 m2 at least. Based on the supply of public parking resource, the appropriate scale of FFBS fleet in Nanjing should be not more than 160,000 bikes. Actually, the bicycle supply of FFBS in Nanjing exceeds this parking limit to a large extent. Although the bike parking resources can be gradually increased in the future, the supply of FFBS bicycles should still be effectively reduced. In China, the GHG emissions in full life cycle of FFBS bicycle are 76 kg CO2 per bike (Chen and Chen, 2018). If the supply of FFBS bicycles can decrease by 100,000 bikes, 7600 tonnes of CO2 emissions would be reduced. In the FFBS operation, bicycle repositioning is an important source of generating GHG emissions. In the journey data of FFBS, if the last trip destination and the next trip origin of a bike are more
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Fig. 7. The planning suggestion for FFBS parking facility in Nanjing.
than 100 m away, it can be considered as one time of FFBS repositioning. Under this assumption, Mobike bikes were repositioned in 45,500 times in Nanjing within this week. So the current repositioning frequency of Mobike in Nanjing is about 6500 times per day. According to FFBS companies, one truck could reposition FFBS bikes in about 150 times per day, and the average mileage of each truck is about 50 km per day. The main repositioning vehicles for FFBS are small trucks with a carbon emission of 0.2577 kg/km (Chen and Chen, 2018). The resulting carbon emissions of Mobike repositioning in Nanjing are therefore 204 tonnes per year. The parking demand should be reasonably satisfied to reduce the repositioning FFBS and the resulting GHG emissions. Comparing the parking demand with the repositioning frequency in each virtual station, a significant positive correlation was observed. The linear regression model could be applied to describe the relationship, as follows:
r ¼ 0:352 þ 0:016n
(2)
where r represents Mobike repositioning frequency (units: repositioning times per day) in a virtual station; n means Mobike parking demand (the maximum number of Mobike bikes in the day, units: parking spaces) for this station; and R2 ¼ 0.27. Moreover, the Spearman correlation coefficient of 0.51 was estimated, and the Pearson correlation coefficient of 0.52 was derived (both coefficients are significant at the 0.001 level). There are about 1000 times per day of repositioning repeated in the same virtual stations, and the repeated repositioning could be avoided by adding corresponding parking spaces in these stations. If the 1000 parking spaces are increased in the reasonable places, the repositioning frequency would decrease by the same number and the resulting CO2 emissions will be reduced by 31 tonnes per year.
5. Conclusion and discussion In this paper, a methodological framework was proposed to support FFBS parking planning. The framework was applied in Nanjing with Mobike journey data and the Nanjing FFBS bike survey. The Mobike journey data were used to identify the virtual stations and the Mobike parking demand. Various clustering methods were adopted to identify the virtual stations with the trip origins. Combined with the Nanjing FFBS bike survey, this paper estimated the parking demand of all companies in the city. The relation of sustainable development and parking analysis was discussed accordingly. The significant findings are summarized as follows. (a) More than half of bikes are in low turnover rate, and the planning and operation of FFBS should be improved. (b) The K-means clustering performs the best in the cluster analysis, and the fitting number of virtual stations in Nanjing is 4000. The spatiotemporal clustering results reveal the temporal patterns of FFBS. (c) 204,087 bikes of Mobike in Nanjing need 255,000 parking spaces, and there are nearly a thousand virtual stations that could be used as reference for constructing parking facilities. (d) FFBS bicycle supply and repositioning can be improved based on parking analysis, to reduce GHG emissions and avoid resource waste. The research can effectively estimate the parking demand and provide some insights in FFBS management, which would help promote emissions reduction and sustainable development. This study can be applied in several aspects. First, the detailed parking design should be combined with land use, construction cost and parking demand. Zhang et al. (2019b) proposed an efficient method to select locations of FFBS electric fences, determining capacities of parking sites in proportion to the allocated travel demand. This paper determines capacities of each parking sites based on FFBS bike amount, which can more accurately estimate parking
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demand. To handle the space constraints of the virtual stations having high parking demand, different solutions could be considered, such as constructing mechanical stereo garages, reorganizing existing parking areas of private bicycles, integrating with stationed bike sharing parking facilities. Second, the FFBS bike over-supply problem can be avoided by considering the parking space constraint, which would both meet travel demand and avoid oversupply problem. This paper found that a FFBS bike requires approximately 1.2 parking spaces. The reasonable scale of the FFBS fleet could be calculated based on the available parking space. FFBS services consume enormous resources, and reducing the FFBS fleet scale contributes to sustainable development. Especially, the proper decrease of FFBS bicycle supply can contribute to thousands of tonnes of CO2 emissions reduction. Third, the GHG emissions could be reduced with the improvement of FFBS repositioning, which is based on the analysis of parking demand. There are correlation and causality between efficient repositioning and meeting parking demand of FFBS. To regulate the order of FFBS parking, other policies and technical improvements are also necessary. The FFBS supervision platform established by the government is an effective tool for FFBS management. The FFBS supervision platform supports real-time access to the bike positioning and journey data of all enterprises. With the virtual stations of this paper, The Nanjing supervision platform could monitor the detailed operation of FFBS services. Besides, the integrated repositioning system based on big data analysis also helps to mobilize unproductive resources and balance supply and demand. The existing repositioning mode mainly relies on manual input of identifying problems and developing plans. The government staff find chaotic parking and publish repositioning tasks in the WeChat group, then the enterprise employees undertake the tasks and move these bikes. This paper finds that nearly a thousand virtual stations have extremely high parking demand, which helps the integrated repositioning system to identify oversupply regions. Accessing and processing the data of the FFBS supervision platform, the integrated repositioning system automatically determines the reasonable rebalancing target and truck arrangement. In addition, electric fence technology needs to be further refined to be low-cost and high-precision. The GPS-based bike positioning has low accuracy, but requires no additional equipment. The Bluetooth-based bike positioning has high accuracy, but the construction and maintenance costs of the Bluetooth device are high. The user’s smartphone has high positioning accuracy, and it can be integrated with bike positioning to determine the proximity between bikes and electronic fences. Future research can be improved in some ways. This study only used the data from Mobike and estimated other companies’ parking demand with the FFBS bike survey. With the establishment of the FFBS supervision platform, it is possible to analyze the data of all companies to obtain more accurate and reliable results. In addition, the urban management department has set up no-parking areas considering various factors, and the research results can be used to meet the affected parking demand. Moreover, user incentive strategies can be combined with parking demand analysis to improve FFBS operation. For example, users riding the bikes away from where the parking demand exceeds the capacity constraint, would receive money or bonus points. Besides, more researches are needed to acquire optimized solutions of bike repositioning, solving FFBS parking problems and reducing GHG emissions in another way. Acknowledgments This research is sponsored by the National Natural Science Foundation of China (51338003, 71801041, 71901059) and Special
Research Fund Ghent University (BOF19/PDO/043). The authors appreciate Beijing Mobike Technology Co., Ltd. for providing the data used in this study. And the authors will also owe great appreciation to Mr. Yu Gu from Southeast University for assisting manuscript modification. References Ai, Y., Li, Z., Gan, M., Zhang, Y., Yu, D., Chen, W., Ju, Y., 2018. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput. Appl. 1e13 https://doi.org/10.1007/ s00521-018-3470-9. Bao, J., He, T., Ruan, S., Li, Y., Zheng, Y., 2017. Planning bike lanes based on sharingbikes’ trajectories. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17. ACM Press, New York, New York, USA, pp. 1377e1386. Caggiani, L., Camporeale, R., Ottomanelli, M., Szeto, W.Y., 2018. A modeling framework for the dynamic management of free-floating bike-sharing systems. Transp. Res. C Emerg. 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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China Mingzhuang Hua a, b, c, Xuewu Chen a, b, c, *, Shujie Zheng a, Long Cheng d, Jingxu Chen e a
Jiangsu Key Laboratory of Urban ITS, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China c School of Transportation, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China d Department of Geography, Ghent University, Krijgslaan 281 S8, Ghent, 9000, Belgium e Department of Logistics & Maritime Studies, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 January 2019 Received in revised form 16 September 2019 Accepted 5 October 2019 Available online 9 October 2019
In recent years, free-floating bike sharing (FFBS) has been rapidly promoted in China, attracting numerous users and consuming many resources. The extensive supply and inefficient repositioning of FFBS are challenges for sustainable development, and put forward higher requirements for parking planning. This paper estimates the parking demand of all three FFBS companies by combining the journey data of Mobike in Nanjing and the Nanjing FFBS bike survey. Three clustering methods were applied to determine the virtual stations of bike aggregation. The maximum number of bikes in a day is recognized as the parking demand in each virtual station. The results show that more than half of bikes are in low turnover rate, and the management of FFBS should be improved. The K-means method turns out to observe the best clustering result for determining virtual stations. The demand for parking spaces of all FFBS companies in Nanjing is estimated accordingly. In addition, the relations of bicycle supply, repositioning and parking are discussed, showing the impact of parking demand on greenhouse gas (GHG) emissions. The research could help to propose an appropriate plan for meeting FFBS parking demand, and have enlightening sight of emissions reduction and sustainable development in the FFBS services. © 2019 Elsevier Ltd. All rights reserved.
Handling Editor: Zhifu Mi Keywords: Free-floating bike sharing Journey data Parking demand Station planning Nanjing
1. Introduction Free-floating bike sharing (FFBS) is regarded as a new type of bike sharing, which has the peculiarities of sustainable development and environmental protection. Free-floating bikes could be picked up and returned anytime and anywhere, without the limitation of stations or docks. In the second half of 2016, the FFBS services such as Mobike and ofo, were put into operation in China’s first-tier cities. The number of covered cities and operated bikes were rapidly growing because of FFBS’s convenience and popularity. In 2017, FFBS had a total fleet of 23 million bikes, covering 200 cities in China. The total registered users reached 221 million
* Corresponding author. Jiangsu Key Laboratory of Urban ITS, Southeast University, Dongnandaxue Road #2, Nanjing, 211189, China. E-mail addresses: [email protected] (M. Hua), [email protected] (X. Chen), [email protected] (S. Zheng), [email protected] (L. Cheng), jingxu. [email protected] (J. Chen). https://doi.org/10.1016/j.jclepro.2019.118764 0959-6526/© 2019 Elsevier Ltd. All rights reserved.
and the total riding distance in the whole year was about 30 billion kilometers in the same year (China Academy of Information and Communications Technology and Beijing Mobike Technology Co., ltd., 2018). Along with FFBS expanding, problems such as over-supply of bikes, the mess caused by disorder parking, the lack of effective repositioning become increasingly serious. The FFBS parking issue has caused widespread concern of the government and society, which involves occupying urban space and affecting traffic order. Especially, the FFBS parking issue has close relations with bicycle supply and repositioning, which involves resources consuming and greenhouse gas (GHG) emission. The sustainable development of FFBS services calls for effective planning and management based on parking analysis. 1.1. Literature review Many studies have suggested different improvements of
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traditional stationed bike sharing from operation and planning, which are crucial parts of emissions reduction in bike sharing service. Zhang et al. (2019a) found that optimal layout of bicycle stations in Tokyo can reduce carbon dioxide (CO2) emissions by 3100e3800 tonnes, while bicycle production and rebalancing would generate vast quantities of emissions. Some authors have investigated the travel patterns of users (Caulfield et al., 2017), the travel demand forecasting (Yang et al., 2016), and the optimal stations location (Wang et al., 2016; Park and Sohn, 2017). Zhang et al. (2015) analyzed bike sharing systems in five Chinese cities to explore the influences of transport planning, system design and business model on sustainability. Kabak et al. (2018) combined different decision-making methods with a geographic information system (GIS) to demonstrate the superiority of the suggested locations. Zhang et al. (2017) suggested that adding a new empty station within 300 m can improve the travel demand at the station, indicating the positive effect of adding parking supply on bike sharing usage. Sun et al. (2017) used spatiotemporally varying measurements to investigate the impacts of environmental characteristics, suggesting that improving bike sharing facilities can increase the usage of bike sharing. With global positioning system (GPS) devices increasingly affordable, the FFBS without docks comes true. The big data mining can help to solve the challenges of system operation and facility construction (Fishman, 2016). Big data of FFBS are mainly GPS data (collected through smart locks and FFBS apps) and journey data. Big data mining of FFBS makes it possible for more effective planning and operation, including parking management and emissions reduction. Free-floating bike sharing (FFBS) is a new kind of bike sharing, so the existing studies are relatively limited. The literature on the FFBS covers issues such as development factors, usage patterns, operation management and emissions reduction. Ma et al. (2018) examined the influence of commercial, political and social factors in addressing the public problems of the FFBS from a collaborative perspective. Shi et al. (2018) employed the social network analysis (SNA) method to investigate the critical factors in FFBS. Shen et al. (2018) adopted spatial autoregressive models to examine the spatiotemporal usage patterns of FFBS in Singapore. Liu et al. (2018) developed a combined model to infer bike distribution in new cities by studying from cities populated with FFBS bikes. On the basis of simulation experiments, Caggiani et al. (2018) constructed a modeling framework for the dynamic repositioning model of FFBS systems. In their study, the journey data was clustered at spatiotemporal scale to form virtual stations. Zhang and Mi (2018) estimated the benefits of FFBS on the GHG emissions in Shanghai. In 2016, FFBS in Shanghai decreased petrol use and CO2 emissions by 8358 and 25,240 tonnes, respectively. The research about the FFBS facility construction is even less, especially for parking facility. Bao et al. (2017) used the trajectories of Mobike in Shanghai to solve the issues on non-motor vehicle lane planning and construction. They suggested the greedy algorithm as the solution methodology by considering finance, construction and efficiency constraints. Based on the partial journey data of Mobike in Beijing provided by the Mobike Big Data Challenge 2017, Deng et al. (2017) divided the riding zones into five types, such as tidal type and one-way type, and then proposed corresponding optimization advice on parking facilities. van Waes et al. (2018) found that the business model of station-based bike sharing is better institutionalized but harder to spread, while that of FFBS can scale up better if geo-fencing technologies are well implemented. 1.2. Objectives and contributions The spatial objects of FFBS research are mainly grids or traffic
analysis zones (TAZ), which cannot describe the travel demand of users accurately. Ai et al. (2018) employed the deep learning approach to address the spatiotemporal dependences, the research subjects of which are grids (4 km 4 km). Reiss and Bogenberger (2015) created a demand model and an operator-based strategy to obtain an optimal distribution of bikes within 40 TAZs of the operating area in Munich. Xu et al. (2018) developed the long shortterm memory neural networks to predict the trip demand at 118 TAZs in Nanjing (the area of each TAZ is several square kilometers). Zhang et al. (2019b) used Mobike trips data to support FFBS electric fence (the uniform cells are 50 m 50 m), while the influence of other companies such as ofo is not considered. Few studies divided the spatial extent intensively based on FFBS travel characteristics, and this paper fills this research gap with the cluster analysis of journey data. FFBS has been rapidly promoted in the past two years, and there is a preliminary exploration on the operation and planning of FFBS. However, there is few targeted researches on FFBS parking and it is difficult to acquire city-scale data for analysis. The free-floating characteristic makes parking problem more complicated than general bike sharing system. It is necessary to accurately and comprehensively understand the parking demand pattern for facility planning. This paper estimates the parking demand of all FFBS companies with the journey data of Mobike in Nanjing and the Nanjing FFBS bike survey. This paper aims to save the operational resources and promote the sustainable development, by optimizing FFBS bicycle supply and repositioning based on parking demand analysis. The contributions of this study include: (a) The service characteristics of FFBS system are explored based on the journey data. (b) This paper accurately estimates the parking demand of FFBS for facility planning, such as electric fences (ECNS Wire, 2017) and parking zones. (c) The results may significantly guide the operation and planning of FFBS system, promoting emissions reduction and resource saving. This paper is divided into five sections. Following this introduction, the second section presents the data sources. The third section describes the methodology used for estimating the parking demand of FFBS. A case study with application and results from Nanjing, China are provided in the fourth section. In the end, the fifth section presents the conclusion of this study. 2. Data sources 2.1. The FFBS in Nanjing At the beginning of 2017, FFBS came into Nanjing. The FFBS systems attract many users because of convenience and stylish appearance. In April 2017, these companies competed to occupy the market and launch a “free-ride” promotion. At the end of July 2017, there were 12 companies in Nanjing, with a total number of 450,000 bicycles and a daily usage of about 2 million trips (Xinhua Daily, 2017). In August 2017, the joint meeting of Nanjing Transportation Bureau, Municipal Public Security Bureau, and Urban Management Bureau banned FFBS enterprises from allocating new bicycles. In the second half of 2017, small businesses shut down, leaving only three big companiesdMobike, ofo and Hellobike. The numbers of bikes of these three companies are relatively close. On the other hand, the rapid expansion of FFBS has led to worries about the accumulation in the hotspots, the mess caused by disordered bikes, and the relative limited management resources. According to the 39th Government Service Newsletter issued by the Nanjing Municipal Service Management Office, during July 2017, the Nanjing Municipal Hotline received 554 complaints about FFBS. Moreover, the complaints continued to increase in the following three months, of which more than 80% were about parking chaos.
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Fig. 1 shows two parking samples, reflecting the different orders of FFBS parking. 2.2. Data description and preprocessing Beijing Mobike Technology Co., Ltd. (one of the world’s largest FFBS operators) provided journey data in Nanjing from September 18th to 24th, 2017 (Monday to Sunday), in which latitude and longitude data was positioned under the world geodetic system (WGS 84). The dataset has 3.64 million journey records, with file size of 603 MB. The fields of the journey data include: journey id, user id, bike id, departure time, longitude of origin, latitude of origin, arrival time, longitude of destination, latitude of destination, date. The errors of the journey data mainly include the fact that 117,724 journey records have no destination positioning. Besides, trip origins or destinations in 424,696 journeys records are outside of the built-up area, while this paper only studies the parking demand in the built-up area. After these two kinds of data were eliminated, 3.09 million journey records were extracted, of which origins and destinations are all located in the built-up area in Nanjing. In order to achieve accurate clustering results with Euclidean distances, coordinate system transformation is needed to convert the latitude and longitude position from the world geodetic system (WGS84) to the WGS84 EASE-1 Grid Global projected coordinate system. WGS84 EASE-1 is the recommended coordinate system in the GIS software, ARCGIS, where EASE stands for Equal Area Scalable Earth. In June 2018, the School of Transportation at Southeast University launched the Nanjing FFBS bike survey. The numbers and integrity of bikes belonging to three enterprises (Mobike, ofo, and Hellobike) were investigated in 104 sites in Nanjing from June 25e26, 2018. The three companies account for the whole market of FFBS in Nanjing since the second half of 2017. The survey results found the bike distribution of these three FFBS companies. There are 3368 FFBS bikes investigated in this survey. 1366 bikes (40.6%) belong to ofo, 1100 bikes (32.7%) belong to Mobike, and 902 bikes (26.8%) belong to Hellobike. 88.2% of investigated ofo bikes are in good condition, the integrity ratio is 92.8% for Mobike, and the integrity ratio is 93.8% for Hellobike. The results show that ofo has an advantage in the number of bikes, while Mobike and Hellobike are better in bike quality. Because the Nanjing government banned the allocation of new FFBS bikes in August 2017, the Nanjing FFBS market has become gradually stabilized since then. So the journey data of Mobike in Nanjing in September 2017 and the Nanjing FFBS bike survey in June 2018 could be used for the combined analysis. Presently each FFBS company operates relatively independently, but comanagement can help to improve the service level of the whole system. Therefore, the bike survey data is a powerful complement
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to the Mobike journey data. The journey data of Mobike can be clustered to obtain the parking demand of Mobike, while FFBS survey results can be used to calculate the proportion of Mobike bikes of three companies in all locations. Combing the two datasets finally helps to analyze the whole parking demand of three FFBS companies in the built-up area of Nanjing. Different sizes of journey data were applied in the three core data analyzing process in this paper. (a) FFBS basic characteristics (using one-week Mobike journey data). (b) Virtual station clustering (using one-day Mobike journey data). (c) Parking facility demand calculation (using oneday Mobike journey data and the Nanjing FFBS bike survey). 3. Methodological framework Considering the complexity of the FFBS system, it is necessary to simplify the parking problem with journey data mining. To achieve this goal, the key is to convert the aggregates of bicycles in certain area into virtual stations. A virtual station is a set of trip origins within the acceptable walking distance. Because of the free-floating characteristic of FFBS, their quantity in each area is consistently changing, so it is difficult to estimate the parking demand directly using their number and location. A more effective method for analyzing user demand, allocating appropriate area and determining virtual stations should be developed. Cluster analysis or clustering is an unsupervised learning classification method which is commonly used in machine learning (Jain et al., 1999). The aim of clustering is to allocate elements of different patterns into a small number of homogeneous groups without any prior information. Considering that the FFBS bikes are free-floating and the trip origins are dispersed, cluster analysis can be used to determine both the location and scale of virtual stations in Nanjing. Each cluster is regarded as the area of corresponding virtual station, the centroid of each cluster can be regarded as the recommended location of this virtual station, and the maximum number of bikes in each cluster can be regarded as the design reference of the scale of the virtual station. The entire procedure of parking demand estimation is outlined in Fig. 2. The destination of last trip and the origin of the next trip of the same bike are mostly coincident even with some exceptions, so the origins of the trips are selected as the research objects of parking demand. Step 2: Select virtual stations candidates with clustering Clustering of journey data includes spatial clustering and spatiotemporal clustering. K-means clustering and density-based clustering are widely used in spatial clustering. Eventually, Kmeans clustering, density-based clustering and spatiotemporal clustering are individually applied to identify virtual stations.
Fig. 1. Parking samples of FFBS in China.
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The best clustering result is selected as the locations for virtual stations. The evaluation criteria of clustering results are silhouette coefficient and Calinski-Harabaz index (CH index). Silhouette coefficient is a measure of how similar an object is to its own cluster compared to other clusters, ranging from 1 to 1. A higher Silhouette coefficient relates to a model with better defined clusters. CH index evaluates the effect of clustering by the density within the cluster and the dispersion between clusters. When it comes to CH index evaluation, the Elbow Method is applied. The Elbow method (Thorndike, 1953) is that a number of clusters should be chosen if adding the number of clusters doesn’t give much better clustering, so the chosen point in the number-index curve likes the “Elbow” on the arm. Fig. 2. Flowchart of parking demand estimation methodology. Step 1: Select data of parking demand
K-means clustering aims to minimize the within-cluster sum of squares (WCSS), which is also the key evaluation criterion of this method (Macqueen, 1967) and suitable for the Elbow Method. Kmeans clustering requires only one parameter, which is the number of clusters, k. The algorithm involves the following steps: (a) Randomly create k initial cluster centroids. (b) Calculate the distances between the elements and the centroids and assign each element to the nearest cluster. (c) Take the arithmetic mean of the elements in each cluster as the new centroid. (d) Repeat step (b) and (c) until the clustering results no longer change. The distance between each element in (b) is Euclidean distance because it considers walking distance which is in accordance with the definition of virtual station. The distance in density-based clustering method and spatiotemporal clustering is also Euclidean distance. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is one of the most common density-based clustering methods. The aim is to make the number of objects contained in a certain area not less than a given threshold (Ester, 1996). DBSCAN has two parameters as inputs, the radius (ε) and the minimum number of points (MinPts). The process of the DBSCAN algorithm adopts the following steps: (a) Find one core point as a cluster, which satisfies the MinPts in the neighborhood of radius ε. (b) Scan the points in the neighborhood from this core point and put these points into the cluster. Repeat this step until there is no point that can meet the requirements. (c) Delete every point in the previous cluster and repeat step (a) and (b) until there are no new core points in the dataset. (d) Points that are not included in any cluster compose the noise set. Taking the temporal patterns of FFBS into account, spatiotemporal clustering is used to figure out virtual stations. The spatiotemporal clustering consists of two layers. The first layer is to aggregate the temporal trends of the journey data, and the second layer is to aggregate the spatial characteristics based on the temporal clustering results. The process of spatiotemporal clustering is outlined as follows: (a) Allocate grids (50 m 50 m) as the initial station service zones, and the centroids of grids as the initial stations. (b) Draw the time curve of the number of bikes in each grid by analyzing the journey data, and then conduct the temporal clustering. The time curve is the time series variation curve of the number of FFBS bikes with a time span of one day. (c) Based on the results of temporal clustering, spatial clustering is performed on the centroids of each temporal cluster, and the virtual stations are finally determined. Step 3: Determine locations for virtual stations
Step 4: Calculate parking demand of virtual stations According to the results of the Nanjing FFBS bike survey referred in Section 2.2, we calculate the proportion of Mobike bikes in all FFBS bikes at investigation locations, that is, Mobike ratio. For example, there are 100 FFBS bikes in an investigation location, 20 of them are Mobike bikes, so the Mobike ratio of this place is 0.2. By taking the average value of each administrative district, the Mobike ratio in each virtual station can be obtained. The maximum value in the time curve of the number of Mobike bikes can be calculated in each virtual station. The parking demand of each virtual station can be evaluated with Mobike ratio and the maximum value of Mobike bikes, which is the foundation of parking facility planning. The formula computing the parking demand of virtual stations is as follows:
n N ¼ ,s,c k
(1)
where N is the parking demand of each virtual station; n is the maximum number of Mobike bikes in the day at this station; k is the Mobike ratio; s is the safety factor. Assume that the number of bikes increases to 1.1 times of the maximum value in an emergency, then s equals 1.1. c is the capacity factor. Assume that parking facility of this virtual station can accommodate 90 percent of the FFBS bikes, then c equals 0.9. Step 5: Recommend facility construction for parking planning The FFBS parking facility involves occupying urban space and financial support, so it must be carried out under the planning arrangement of the government department. Taking into account the FFBS parking planning goals, the locations in high demand should be selected to build parking facilities. And based on the different scales of parking demand, different forms of construction design are recommended. 4. Data analysis and results The mathematical formulation was coded in Spyder using Python and implemented on a workstation with Intel XeonW-2145 CPU @ 3.70 GHZ and 64.0 GB RAM. 4.1. Descriptive analysis After the elementary analysis of the journey data, it is found that Mobike has a total of 3.09 million trips in the built-up area of Nanjing in the week, 665,320 users and 204,087 bikes. For the amount of usage, it has an average daily order of about 440,000 trips, a weekly travel frequency of 5.5 times per person and a daily
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bike turnover rate of 2.5 times per bike. Fig. 3(a) shows that most bicycles of Mobike fleet are in a lowfrequency state. Specifically, more than half of the bikes are used less than twice a day. Most bikes are in low turnover rate, indicating that the operation effectiveness of FFBS should be reinforced, and parking facilities should be improved. In Fig. 3(b), one-third users have at least 5 trips per week, showing that the majority of the FFBS journeys come from the travel demand of these frequent users. The average travel time, the average travel distance, and the average riding speed of Mobike in Nanjing are 11.9 min, 2.2 km, and 11.0 km per hour respectively. Fig. 3(c) and (d) show that 65% of the travel time is less than 10 min, and the proportion of travel distance within 1 km reaches 62%, indicating that FFBS mainly serves shortdistance trips. Fig. 4 presents the spatial distribution of Mobike trips in Nanjing. The major hotspots are distributed in the central area, and the hotspots in the surrounding areas are fewer and mostly located in the vicinity of important business districts, subway stations and higher education institutions. The numbers of daily trips under different weather conditions are summarized in Table 1. The numbers of journeys on sunny and cloudy days are relatively high, while overcast and shower would partially reduce travel demand. There are few trips in bad weather such as heavy rain. The findings show that weather has a significant impact on the quantities of FFBS trips, and users are more inclined to travel in good weather.
4.2. Estimating parking demand of FFBS In order to avoid the effects of the weekend and represent average daily trips, Wednesday was selected to be studied. The origins of Mobike journeys in Nanjing on September 20, 2017 (Wednesday) compose the clustering input dataset. As a result, a total of 428,235 origins are selected. Table 2 and Fig. 5(a) show the results of K-means clustering. The
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silhouette coefficient is the largest when the number of clusters is set as 4000. And according to the Elbow Method of WCSS and CH index, the result of 4000 clusters performs well. In summary, the reasonable number of clusters for K-means clustering is 4000. The results of DBSCAN at different radius are shown in Table 3 and Fig. 5(b). The MinPts for each virtual station is set as 10, that is, at least 10 trips are generated each day within the service area of a virtual station. Setting 10 as the MinPts considers the experience that if the parking demand is less than 10 there is no need to build a station for it, these bikes can park in the place where private bikes used to park. After the MinPts is determined, a series values of radii are selected for comparative analysis. If the clustering evaluation index reaches the best at a certain radius, this radius is taken as the optimal radius. Density-based clustering has no WCSS, so it is replaced here by the CH index. When the radius reaches 20 m, the silhouette coefficient is the largest and the criterion of CH index is satisfied. So the density-based clustering result is the best with the radius of 20 m. When the radius is greater than 50 m, all samples in the city center are merged into one cluster, of which the clustering result is unrealistic. Density-based clustering has no defined cluster centroids as K-means clustering does, so the barycenter of each cluster is seen as the cluster centroid. The results of spatiotemporal clustering are as follows. Temporal clustering: The mean, the variance, the ratio between the number of bikes of day and night, and other indicators of the time curve were calculated, standardized and used as clustering criteria, and K-means clustering is performed. According to the Elbow Method and the silhouette coefficient, it finally forms 3 temporal clusters. The first temporal cluster is the general zone, in which the quantity of bikes remains relatively unchanged. The second cluster shows the feature of “morning-arrive and nightleave”, in which bikes during the daytime is much more than that at night. These zones are represented by the commercial areas and most subway stations. The third cluster shows the feature of “morning-leave and night-arrive”, in which bikes during the
Fig. 3. Operational characteristics of Mobike in Nanjing.
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Fig. 4. Spatial distribution of Mobike trips in Nanjing.
Table 1 Trips in different weather conditions in Nanjing. September 18th-25th, 2017
Weather condition
Number of daily trips (units: 10000 trips)
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Sunny Cloudy Shower Overcast Cloudy Shower Heavy Rain
53.7 52.4 42.8 45.8 55.8 46.7 12.2
Table 2 K-means clustering evaluation results. Number of clusters
Within-cluster sum of squares (WCSS)
Silhouette coefficient
CH index
Running time(s)
1000 2000 3000 4000 5000 6000 7000 8000 9000
18133 7831 4678 3252 2442 1927 1566 1314 1123
0.4440 0.4530 0.4574 0.4605 0.4598 0.4536 0.4521 0.4470 0.4434
731967 845301 941157 1012758 1076417 1134361 1193162 1241539 1287954
2009.3 4956.7 8113.9 10673.4 15557.8 15335.1 20985.3 26316.9 35975.6
daytime is much less than that at night. These zones usually consist of residential areas and a few subway stations. Spatial clustering: K-means clustering is conducted on the grid centroids of the three obtained temporal clusters to determine the virtual stations. The procedure is the same as that in Section 3. As a result, the first temporal cluster generates 2000 virtual stations, the second one generates 500 virtual stations, and the third one generates 1000 virtual stations. Totally, the spatiotemporal clustering generates 3500 virtual stations.
The clustering results of various methods are shown in Table 4. K-means clustering and spatiotemporal clustering both have their advantages and disadvantages. K-means clustering has the largest silhouette coefficient and CH index, which demonstrates it is the most suitable one for matching the distribution of FFBS bikes. However, some places with few FFBS bikes are recognized as clusters because they are in the remote areas, which is a disadvantage of K-means clustering. Spatiotemporal clustering can reveal the temporal mode of the bicycle flows, which might be an
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Fig. 5. Spatial clustering index evaluation.
Table 3 DBSCAN clustering evaluation results. Radius(m)
Number of clusters
Silhouette coefficient
CH index
Noise ratio
Running time(s)
5 10 15 20 25 30 40 50
1762 4022 4775 4777 4462 4057 3211 2347
0.7891 0.4427 0.2032 ¡0.1357 0.1371 0.1478 0.2479 0.3215
25 40 64 97 141 200 373 612
88.6% 65.1% 47.2% 35.6% 27.7% 21.9% 14.1% 9.6%
3.3 3.7 4.3 4.7 5.0 5.5 5.7 6.2
K-means clustering and DBSCAN are both spatial clustering methods, the clustering results under various parameters are shown in Fig. 5. The result of K-means clustering is much better than that of DBSCAN, and the appropriate number of virtual stations is 4000 in spatial clustering.
Table 4 Evaluation of various clustering results. Clustering methods
Number of clusters
Silhouette coefficient
CH index
Noise ratio (%)
K-means clustering DBSCAN Spatiotemporal clustering
4000 4777 3500
0.4605 0.1357 0.0937
1012758 97 28
0.0 35.6 21.5
indicator for the relocation of FFBS. The drawback of spatiotemporal clustering is that the silhouette coefficient and CH index of that are relatively low. When it comes to the parking management of FFBS virtual stations, this paper merely aims to accurately identify the maximum number of bikes that the virtual stations should accommodate. Therefore, K-means clustering is the best choice for estimating FFBS parking demand, the appropriate number of virtual stations is 4000. On Wednesday of the week, almost 100% trip origins and destinations can be located in one of the virtual stations. On other days of the week, about 90% trip origins and destinations can be located in one of the virtual stations. The virtual stations can ensure a high level of travel demand coverage, proving the validity of clustering results. Fig. 6 and Table 5 indicate that the Mobike ratios in Nanjing are unevenly distributed. The Mobike ratios in Gulou, Xuanwu, Qinhuai and Jiangning Districts are high, while those in Jianye, Qixia and Yuhuatai Districts are low. The Mobike ratio within each administrative district is comparatively consistent, and the average Mobike ratio in each district can be regarded as that in each virtual station in this district. The operation fleet of Mobike in Nanjing has 204,087 bikes, of which 156,822 bikes are parked in the virtual stations and 47,280 bikes are parked in remote areas outside the virtual stations. The turnover rate in virtual stations is usually high so more parking space than the current number of bikes is needed. The calculation shows that Mobike requires a total of about 208,000 parking spaces in the virtual stations, so a FFBS bike in the virtual stations requires
approximately 1.3 parking spaces. On the other hand, because the turnover rate in remote areas is low, it is assumed that a FFBS bike in remote areas only needs 1.0 parking space. The built-up areas include both the service area of virtual stations and remote areas. A total of about 255,000 parking spaces are required for Mobike in the built-up area of Nanjing. So the ratio between parking spaces and FFBS bikes in the built-up area of Nanjing is about 1.2, rather than 1.0 or 1.3. Assume that the bike proportion of other FFBS companies in remote areas is basically the same as that of Mobike. Using the maximum number of Mobike bikes and Mobike ratio, it can be calculated that the parking spaces required by all enterprises in the virtual stations are about 796,000. The total number of parking spaces required by all enterprises in the built-up area of Nanjing is about 977,000. According to Chinese design specifications, the area of one bicycle parking space should be 1.5 m2 at least (Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2013). Under the existing FFBS operational condition, the parking space required in Nanjing is 1,466,000 m2. On the basis of clustering, 4000 virtual stations are located in the built-up area of Nanjing. Regarding low-demand parking areas, 1185 virtual stations demand less than 100 parking spaces, 1851 stations demand 100e250 parking spaces. As for high-demand parking areas, 749 stations demand 250e500 parking spaces, and 215 stations demand above 500 parking spaces. In 2017, the Nanjing Urban Management Bureau formulated the “Work Plan for FFBS Parking Management”, and proposed to complete the construction of 1000 non-motor vehicle parking
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Fig. 6. Mobike ratios of survey locations in administrative districts.
Table 5 Mobike ratio and parking demand of FFBS in administrative districts of Nanjing. District
Mobike ratio
SD of Mobike ratio
Number of virtual stations
Number of virtual stations (>250 parking spaces)
Parking demand of virtual stations (units: 10000 parking spaces)
Gulou Xuanwu Qinhuai Jianye Qixia Yuhuatai Jiangning Total
0.289 0.343 0.301 0.192 0.174 0.250 0.313 e
0.16 0.11 0.19 0.11 0.15 0.13 0.16 e
469 464 546 361 642 401 1117 4000
152 101 179 186 195 70 81 964
12.5 8.3 12.6 11.4 14.8 6.7 13.3 79.6
stations. This paper finds that there are 964 virtual stations in the built-up area of Nanjing demanding more than 250 parking spaces. It is recommended to use the research results as a reference for the planning and construction of Nanjing non-motor vehicle parking stations. The specific conditions are shown in Table 5 and Fig. 7. Station locations in Fig. 7 are determined considering the results of cluster analysis, the computed parking demand of virtual stations as well as “Work Plan for FFBS Parking Management”. 4.3. FFBS sustainable development analysis based on parking demand The process of FFBS services is that the enterprises product and supply the bicycles, the users use and park the bicycles because of travel demand, and the bicycles stacked or parked in disorder are repositioned by the enterprises. In the operation process, bicycle supply and repositioning, which are closely related to parking, are the main parts that consume resources and generate GHG emissions. The bicycle supply of FFBS should be constrained by the supply
of parking space, and the FFBS fleet scale in Nanjing should be appropriately reduced to promote sustainable development. According to the data of the Nanjing Urban Management Bureau, Nanjing has 287,000 m2 of public parking resource for non-motor vehicle until 2018. However, the FFBS parking demand in Nanjing is 1,466,000 m2, which is far larger than parking supply. The ratio between parking spaces and FFBS bikes in Nanjing is about 1.2, and the area of one bicycle parking space should be 1.5 m2 at least. Based on the supply of public parking resource, the appropriate scale of FFBS fleet in Nanjing should be not more than 160,000 bikes. Actually, the bicycle supply of FFBS in Nanjing exceeds this parking limit to a large extent. Although the bike parking resources can be gradually increased in the future, the supply of FFBS bicycles should still be effectively reduced. In China, the GHG emissions in full life cycle of FFBS bicycle are 76 kg CO2 per bike (Chen and Chen, 2018). If the supply of FFBS bicycles can decrease by 100,000 bikes, 7600 tonnes of CO2 emissions would be reduced. In the FFBS operation, bicycle repositioning is an important source of generating GHG emissions. In the journey data of FFBS, if the last trip destination and the next trip origin of a bike are more
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Fig. 7. The planning suggestion for FFBS parking facility in Nanjing.
than 100 m away, it can be considered as one time of FFBS repositioning. Under this assumption, Mobike bikes were repositioned in 45,500 times in Nanjing within this week. So the current repositioning frequency of Mobike in Nanjing is about 6500 times per day. According to FFBS companies, one truck could reposition FFBS bikes in about 150 times per day, and the average mileage of each truck is about 50 km per day. The main repositioning vehicles for FFBS are small trucks with a carbon emission of 0.2577 kg/km (Chen and Chen, 2018). The resulting carbon emissions of Mobike repositioning in Nanjing are therefore 204 tonnes per year. The parking demand should be reasonably satisfied to reduce the repositioning FFBS and the resulting GHG emissions. Comparing the parking demand with the repositioning frequency in each virtual station, a significant positive correlation was observed. The linear regression model could be applied to describe the relationship, as follows:
r ¼ 0:352 þ 0:016n
(2)
where r represents Mobike repositioning frequency (units: repositioning times per day) in a virtual station; n means Mobike parking demand (the maximum number of Mobike bikes in the day, units: parking spaces) for this station; and R2 ¼ 0.27. Moreover, the Spearman correlation coefficient of 0.51 was estimated, and the Pearson correlation coefficient of 0.52 was derived (both coefficients are significant at the 0.001 level). There are about 1000 times per day of repositioning repeated in the same virtual stations, and the repeated repositioning could be avoided by adding corresponding parking spaces in these stations. If the 1000 parking spaces are increased in the reasonable places, the repositioning frequency would decrease by the same number and the resulting CO2 emissions will be reduced by 31 tonnes per year.
5. Conclusion and discussion In this paper, a methodological framework was proposed to support FFBS parking planning. The framework was applied in Nanjing with Mobike journey data and the Nanjing FFBS bike survey. The Mobike journey data were used to identify the virtual stations and the Mobike parking demand. Various clustering methods were adopted to identify the virtual stations with the trip origins. Combined with the Nanjing FFBS bike survey, this paper estimated the parking demand of all companies in the city. The relation of sustainable development and parking analysis was discussed accordingly. The significant findings are summarized as follows. (a) More than half of bikes are in low turnover rate, and the planning and operation of FFBS should be improved. (b) The K-means clustering performs the best in the cluster analysis, and the fitting number of virtual stations in Nanjing is 4000. The spatiotemporal clustering results reveal the temporal patterns of FFBS. (c) 204,087 bikes of Mobike in Nanjing need 255,000 parking spaces, and there are nearly a thousand virtual stations that could be used as reference for constructing parking facilities. (d) FFBS bicycle supply and repositioning can be improved based on parking analysis, to reduce GHG emissions and avoid resource waste. The research can effectively estimate the parking demand and provide some insights in FFBS management, which would help promote emissions reduction and sustainable development. This study can be applied in several aspects. First, the detailed parking design should be combined with land use, construction cost and parking demand. Zhang et al. (2019b) proposed an efficient method to select locations of FFBS electric fences, determining capacities of parking sites in proportion to the allocated travel demand. This paper determines capacities of each parking sites based on FFBS bike amount, which can more accurately estimate parking
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demand. To handle the space constraints of the virtual stations having high parking demand, different solutions could be considered, such as constructing mechanical stereo garages, reorganizing existing parking areas of private bicycles, integrating with stationed bike sharing parking facilities. Second, the FFBS bike over-supply problem can be avoided by considering the parking space constraint, which would both meet travel demand and avoid oversupply problem. This paper found that a FFBS bike requires approximately 1.2 parking spaces. The reasonable scale of the FFBS fleet could be calculated based on the available parking space. FFBS services consume enormous resources, and reducing the FFBS fleet scale contributes to sustainable development. Especially, the proper decrease of FFBS bicycle supply can contribute to thousands of tonnes of CO2 emissions reduction. Third, the GHG emissions could be reduced with the improvement of FFBS repositioning, which is based on the analysis of parking demand. There are correlation and causality between efficient repositioning and meeting parking demand of FFBS. To regulate the order of FFBS parking, other policies and technical improvements are also necessary. The FFBS supervision platform established by the government is an effective tool for FFBS management. The FFBS supervision platform supports real-time access to the bike positioning and journey data of all enterprises. With the virtual stations of this paper, The Nanjing supervision platform could monitor the detailed operation of FFBS services. Besides, the integrated repositioning system based on big data analysis also helps to mobilize unproductive resources and balance supply and demand. The existing repositioning mode mainly relies on manual input of identifying problems and developing plans. The government staff find chaotic parking and publish repositioning tasks in the WeChat group, then the enterprise employees undertake the tasks and move these bikes. This paper finds that nearly a thousand virtual stations have extremely high parking demand, which helps the integrated repositioning system to identify oversupply regions. Accessing and processing the data of the FFBS supervision platform, the integrated repositioning system automatically determines the reasonable rebalancing target and truck arrangement. In addition, electric fence technology needs to be further refined to be low-cost and high-precision. The GPS-based bike positioning has low accuracy, but requires no additional equipment. The Bluetooth-based bike positioning has high accuracy, but the construction and maintenance costs of the Bluetooth device are high. The user’s smartphone has high positioning accuracy, and it can be integrated with bike positioning to determine the proximity between bikes and electronic fences. Future research can be improved in some ways. This study only used the data from Mobike and estimated other companies’ parking demand with the FFBS bike survey. With the establishment of the FFBS supervision platform, it is possible to analyze the data of all companies to obtain more accurate and reliable results. In addition, the urban management department has set up no-parking areas considering various factors, and the research results can be used to meet the affected parking demand. Moreover, user incentive strategies can be combined with parking demand analysis to improve FFBS operation. For example, users riding the bikes away from where the parking demand exceeds the capacity constraint, would receive money or bonus points. Besides, more researches are needed to acquire optimized solutions of bike repositioning, solving FFBS parking problems and reducing GHG emissions in another way. Acknowledgments This research is sponsored by the National Natural Science Foundation of China (51338003, 71801041, 71901059) and Special
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