Analyzing users’ attitudes and behavior of free-floating bike sharing: an investigating of Nanjing

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Transportation Research Procedia 39 (2019) 634–645 www.elsevier.com/locate/procedia

Green Cities 2018 Green Cities 2018

Analyzing users' attitudes and behavior of free-floating bike sharing: Analyzing users' attitudes and behavior free-floating bike sharing: an investigating of of Nanjing an investigating of aNanjing a a Jing Chen a, Yong Zhang a, Rui Zhang* a, Xu Chengb, Fengyi Yanb Jing Chen , Yong Zhang , Rui Zhang* , Xu Chengb, Fengyi Yanb a

School of Transportation, Southeast University jiulonghu campus, Nanjing, Jiangsu, 210000, CN a Transportation Management of JiangsuSoutheast ProvincialUniversity Transportation Department, No. 69, Shigu Road, Nanjing, School ofBureau Transportation, jiulonghu campus, Nanjing, Jiangsu, 210000, CN Jiangsu, 210000,CN b Transportation Management Bureau of Jiangsu Provincial Transportation Department, No. 69, Shigu Road, Nanjing, Jiangsu, 210000,CN b

Abstract Abstract Free-floating bike sharing (FFBS) is an innovative mode of bicycle operation that has grown rapidly in China. This paper studies the attitude and travel behavior of isfree-floating bike sharing in Nanjing. Questionnaires wererapidly sent byinfield survey. Investigators Free-floating bike sharing (FFBS) an innovative mode of bicycle operation that has grown China. This paper studies go on streets, stations, shopping malls, bike campus to distribute questionnaires. A total 700byquestionnaires were issued, the attitude andsubway travel behavior of free-floating sharing in Nanjing. Questionnaires wereofsent field survey. Investigators andon data from subway 453 users were analyzed. data of thetoquestionnaire were analyzedAtototal findofout attraction of were free-floating go streets, stations, shopping The malls, campus distribute questionnaires. 700thequestionnaires issued, bike sharing theusers citizens Nanjing, The including thethe travel time and distance characteristics of free-floating used by the and data fromfor453 wereofanalyzed. data of questionnaire were analyzed to find out the attractionbike of free-floating people of Nanjing. clustering analysis of the data shows time the identity characteristics of four of people and the by travel bike sharing for theThe citizens of Nanjing, including the travel and distance characteristics of types free-floating bike used the characteristics of using bike. The results that:(1) The major cyclists of of FFBS Nanjing are college students people of Nanjing. The free-floating clustering analysis of the datashow shows the identity characteristics four intypes of people and the travel and enterprise'sof staff, betweenbike. 18 and (2) show Among the respondents, 71.52% of FFBS users recognized as the characteristics using aged free-floating The 40. results that:(1) The major cyclists of FFBS in Nanjing are collegeitstudents mainstream modestaff, of short-distance and40. 43.05% of usersthe are respondents, willing to use71.52% FFBS companies' long-term serviceit such as and enterprise's aged betweentravel, 18 and (2) Among of FFBS users recognized as the mobike and ofo. has an impact onand utilization public private car trip long-term frequency,service particularly mainstream mode(3)ofFFBS short-distance travel, 43.05%rate of of users are transportation willing to use and FFBS companies' such on as short distance transport Theonconclusions of this papertransportation can be referenced by urban management to mobike and ofo.public (3) FFBS has antrips. impact utilization rate of public and private car trip frequency, departments particularly on formulate policies and transport free-floating bike sharing enterprises to provide better short distance public trips. The conclusions of this paper can beservice. referenced by urban management departments to formulate policies and free-floating bike sharing enterprises to provide better service. © 2018 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific of Greener Cities This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee committee of Green Green Logistics Logistics for for Greener Cities 2018. 2018. Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. Keywords: Free-floating bike sharing, Cluster analysis, Trip behavior, Nanjing Keywords: Free-floating bike sharing, Cluster analysis, Trip behavior, Nanjing

* Corresponding author. Tel.: +86-177-0518-9676 ; fax: +86-177-0518-9676 address: author. [email protected] *E-mail Corresponding Tel.: +86-177-0518-9676 ; fax: +86-177-0518-9676 E-mail address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2352-1465 © 2018 Thearticle Authors. Published by Elsevier B.V. Selection under responsibility of the scientific of Green Logistics for Greener Cities 2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 2352-1465  2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 10.1016/j.trpro.2019.06.065

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Introduction Research Background Free-floating bike is a type of bicycle that can be unlocked by scanning. Unlike a public bicycle, free-floating bike sharing system do not have a fixed parking place restriction, so there is no site construction and maintenance costs. The free-floating bike is shown in Fig.1. When people see the shared bicycle on the roadside, they use the APP code on the mobile phone to unlock the bicycle. When the bicycle is over, park the vehicle on the roadside and manually close the bicycle lock. The subsequent ride costs will be deducted from the account. There is a record of the vehicle's driving directions and parked GPS in the lock, according to the data, free-floating bike sharing system operator can schedule a bicycle, recover and repair the damaged vehicle, and manage the vehicle.

Fig. 1. Picture of Free-floating bike.

Free-floating bike appeared in China since 2015, because the free-floating bike has features such as convenient use, environmental protection as low carbon, help to ease traffic congestion, environmental pollution and other issues, have vigorous development in 2016. The country's overall users reached 20.3 million people, the size of the operating market reached 1.15 billion yuan (http://www.pday.com.cn/htmls/report/201704/24516157.html). At the end of 2016, FFBS began to put in use in Nanjing, Wuxi and other cities. Until July 2017, less than a year, FFBS has been extended to other cities of Jiangsu province, such as Nanjing, Wuxi, Yangzhou, Changzhou, Zhenjiang, Nantong. On May 22. 2016, the Ministry of Transport issued “The Guidance on Encouraging and Standardizing the Development of Internet Rental Bicycles” (Draft for Comment), and publicly solicited opinions from the whole society. Chengdu, Shenzhen, Beijing, Hangzhou and other cities have also issued guidance on FFBS, designed to regulate the FFBS in the city's operation and development. In this background, the usage of FFBS in Nanjing were investigated, help the urban management department to make relevant policy recommendations, provide a reference for enterprises to improve the quality of service. Literature review Free-floating bike sharing system is an emerging new generation of bike rentals, free-floating bike is developing rapidly. It is of great practical significance to investigate the attitude of urban residents to free-floating bike and the impact of free-floating bike on urban residents' travel. Public bicycles are growing rapidly around the world, and Cork has been operating public bikes since 2014, to study the use of medium-sized urban public bicycles such as Cork. The findings provide a small dynamic view of using public bicycles and how to provide the public with a new traffic option(Caulfield, B., O'Mahony, M., Brazil, W., & Weldon, P,2017). Toronto has Canada's second largest public bicycle system, which offers a unique case because it is one of the few public bicycles that are located in relatively cold North America and are operating throughout the year. Using a year of historical data on the Toronto population, land, built environment, the impact of different weather on riding. The construction of infrastructure is an important factor in the growth of public bicycle demand, and the study reveals the important correlation between temperature, land use and cycling activities (ElAssi, W., Mahmoud, M. S., & Habib, K. N, 2017). The objective of the study was to quantify the impact of public

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bicycle systems on bus passengers. The study found that public bicycles had an impact on the number of private car trips and bus trips, while also affecting private bikes (Campbell, K. B., & Brakewood, C, 2017). Public bicycles are gaining popularity in major cities around the world and have established public bicycle operating systems in many cities. In these systems, the main operating cost is to move the bicycle reasonably over time so that it can be re-balanced so that a reasonable number of bicycles can be used at the public bicycle site for the user to use. Determine the service level of each bike site, and optimize the vehicle scheduling route to balance the inventory. The results of Hubway (Boston, MA) and Capital Bike share (Washington, DC) are used to calculate the new cluster priority routing on the vehicle scheduling problem. Finally, the proposed heuristic algorithm is superior to the pure mixed integer programming. And constrained programming methods (Schuijbroek, J., Hampshire, R. C., & Hoeve, W. J. V, 2016). Considering the problem of the balance of the number of bikes between the public bicycle stations as a scheduling problem of a vehicle, the problem of balancing public bikes is studied according to the idea of vehicle dispatching, and a heuristic algorithm with an iterated local search (ILS) is proposed to solve the problem. And the validity of the algorithm is verified by testing. In addition, the algorithm can solve most of the known problems, and can improve the results in open examples (Cruz, F., Subramanian, A., Bruck, B. P., & Iori, M, 2016). An effective algorithm for static cycling problem of public bike is proposed (Chemla, D., Meunier, F., & Calvo, R. W, 2013). An accurate algorithm is designed to solve the problem of public bicycle scheduling. An example is given to demonstrate that the optimal solution of 60 stations can be calculated when the calculation time is 2 hours (Erdoğan, G., Battarra, M., & Calvo, R. W, 2015). Public bicycles in the course of operation there will be some of the site demand, the bike is not enough, some sites require small, too many bikes, for this problem, according to the bike system detailed GPS data, consider the weather, time, holidays and other effects Factor, create the demand model, get the optimal distribution of the number of bikes. Finally, through a vehicle scheduling application, ensure that the least fleet to meet the public bike in time and space on the demand (Reiss, S., & Bogenberger, K, 2015). Combined with a large number of studies on public bicycles, some scholars combine the characteristics of FFBS to design algorithms to solve the problem of FFBS vehicle scheduling balance. FFBS is an innovative model of bike sharing, compared with public bicycle, no fixed parking construction site, save the startup costs. With FFBS GPS, we can know the vehicle trajectory and position at any time, then to realize the intelligent management of bikes. The great success of FFBS has benefited greatly from meeting people's needs. The hybrid integer programming is used to optimize the large scale FFBS vehicle scheduling, and the effectiveness of the algorithm is verified by an example(Pal, A., & Zhang, Y, 2017). There are many researches on the public bicycle vehicle scheduling, mainly for the research of algorithms. In addition, some scholars have discussed the characteristics of the use of the crowd and related policies. The study investigated the impact of bicycle riding frequency on university campuses and major cities. The hybrid algorithm reveals the differences and relative advantages between riding obstacles. Using multinomial logit model to control variable selection, based on the social population and riding characteristics, the prediction was related to the riding frequency of the subjects. It is found that periodic routes are closely related to high frequency riding and commuting. In addition, the potential for the rider is hard work and the lack of a sense of security is the main obstacle. Finally, qualitative analysis confirmed the riding route and revealed the importance of improving communication among street users. The study is useful for traffic planners to improve commuters' use of bicycles (Manaugh, K., Boisjoly, G., & El-Geneidy, A, 2017). Through analyzing a large number of bicycle sharing data in Chicago, we can understand the spatio-temporal patterns of bicycle riding behavior. In Chicago, about 15.9% of the people ordered services for public bicycles. Through research, we found different riding patterns for weekdays and weekends, and also found a large demand for bicycle vehicles and sites through hierarchical clustering method. The study will help to better understand the flow patterns of bicycles and can be extended to other cities to study bicycle models and functions (Zhou, X, 2015). FFBS as a new type of rental bike, no fixed parking station, providing a high degree of flexibility and convenience to the user. The use of non-linear autoregressive models in each region is used to model the spatio-temporal clusters and predict the tendency of each cluster bicycle to use (Caggiani, L., Ottomanelli, M., Camporeale, R., & Binetti, M, 2016). Analysis factors influencing the effectiveness of public bike. Established a model to analysis the effects of city characteristics and system characteristics of public bikes for daily use and turnover rate, and to analyze the 60 Chinese public bike system data, the result indicates that public bike passenger volume and turnover rate affected the number of city population, government spending, the number of

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station (Zhao, J., Deng, W., & Song, Y, 2014).Through the data mining technology, in-depth understanding of the activities of the bike mode, revealing the uneven distribution of bicycles. To have a better understand of the structure of the system. To provide support for the public bicycle system design and management decision (Vogel, P., Greiser, T., & Mattfeld, D. C, 2011). From the development of public bicycles in Europe and North America to analyze the diffusion of public bicycle systems (Parkes, S. D., Marsden, G., Shaheen, S. A., & Cohen, A. P, 2013). Through collecting data to analyze policies, urban texture and so on to understand the movement of public bicycles (Tuama, D. Ó, 2015). Research objectives and scope FFBS appeared in Nanjing since late 2016. It brought convenience to Nanjing residents' travel, but also have produced some problems, such as disorderly parking place. This article analyzed the usage of FFBS in Nanjing, mainly analyze the Nanjing FFBS travel behavior and attitude of the user based on the respondents of the questionnaire data. Using statistical analysis and cluster analysis to understand usage of the crowd. It provided a reference for the subsequent management department's management of FFBS, but also as a reference to improve the quality of service enterprises. The rest of the paper is organized as follows. Section 2 presents the state of usage of FFBS in Nanjing. Section 3 introduces the process of data collection and analysis method used in this paper. Section 4 presents analyze of the collected data. Section 5 concludes and future works. Section 6 suggestions. Status of the use of FFBS in nanjing From the second half of 2016, free-floating bikes are sprung up in the streets of Nanjing, the yellow “ofo” and orange “mobike” became a beautiful landscape of Nanjing. According to the data provided by the enterprise for statistical analysis, as of May 10, 2017, there are 8 core brands that have entered the Nanjing free-floating bike sharing market, they are ofo, mobike, bluegogo, hellobike, coolqi, dingding, quickto, No. 7 motorcycle, a total of 285,000 put on. As of July 7, 2017, Nanjing's free-floating bike market has 10 cqore brands, namely ofo, mobike, bluegogo, hellobike, coolqi, dingding, quickto, No. 7 motorcycle, colorful, xiangqi, a total of 510,000 put on. The number of vehicles on each free-floating bike sharing companies is shown in Figure 2. The rapid increase in the volume of free-floating bike has brought travel convenience to urban residents, but also brought a lot of problems, such as not parking in the place, the deposit refunded procrastination and so on. Based on the Investigation of Nanjing Free-floating Bike Sharing Project, the questionnaire was designed to investigate and analyze the citizens' attitudes and behaviors towards free-floating bikes.

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Number of vehicles (Unit: 10,000)

18

5

16

16

13,8

14 12 10

8,5

8 6

5 5

5

4,5 4,1

4 2

3

3

0,7

0,3

4

1,5

1,7

1,5 1,5

0 0,4

0

0

Enterprise of Internet rental bike 10-May-17 7-Jul-17 Fig. 2. Nanjing Free-floating bike number.

Material and methods Data collection At the end of May 2017, a total of 700 questionnaires were randomly distributed on the street through field surveys, and we were successfully recovered 686 valid questionnaires, which the recovery rate was 98.00%. After data cleansing, deletion errors and missing item data, the sample data volume is 624 copies. Among the respondents, users and non-users accounted for 72.60%, respectively, 27.40%. The basic information of each survey is summarized in Table 1. Table 1. Basic information of Nanjing respondents. Attributes

Users

Non-user

Total characteristics

Frequency

%

Frequency

%

Frequency

%

＜12year

2

0.44

2

1.17

4

0.64

12-18year

42

9.27

18

10.53

60

9.62

18-40year

399

88.08

144

84.21

543

87.02

41-65year

9

1.99

6

3.51

15

2.40

＞65year

1

0.22

1

0.58

2

0.32

Male

265

58.50

103

60.23

368

58.97

Female

188

41.50

68

39.77

256

41.03

Did not complete high school

6

1.32

5

2.92

11

1.76

Did not complete high school

37

8.17

18

10.53

55

8.81

Associate's

57

12.58

19

11.11

76

12.18

Age

Gender

Education

6

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Bachelor’s

257

56.73

113

66.08

370

59.29

Graduate

96

21.19

16

9.36

112

17.95

＜RMB 60000 yuan

308

67.99

127

74.27

435

69.71

RMB 60000-120000 yuan

93

20.53

31

18.13

124

19.87

RMB 120000-200000 yuan

34

7.51

13

7.60

47

7.53

RMB 200000-300000 yuan

9

1.99

0

0.00

9

1.44

＞RMB 300000 yuan

9

1.99

0

0.00

9

1.44

Student

255

56.29

108

63.16

363

58.17

Teacher

24

5.30

5

2.92

29

4.65

Employee's

94

20.75

24

14.04

118

18.91

Civil servants

16

3.53

1

0.58

17

2.72

Worker

14

3.09

7

4.09

21

3.37

Doctor

8

1.77

3

1.75

11

1.76

Retired

6

1.32

4

2.34

10

1.60

Others

36

7.95

19

11.11

55

8.81

Average income

Job

Questionnaire design Through the field questionnaire survey of the Free-Floating shared bike usage in Nanjing, we have a general understanding of the situation of Free-Floating bike sharing in Nanjing. We designed the questionnaire according to the questionnaire design principles and methods, modified and improved the questionnaire through the expert discussion way. Respondents choose the appropriate options according to the actual situation. The first part of the questionnaire is to understand the basic information of the respondents, including gender, age, education level, annual income, living city, occupation and so on. The second part of the questionnaire focuses on the travel behavior of the respondents. The average distance traveled daily, 1km range of regular choice of transport, daily travel time, focus on which aspects when choosing a travel mode are all involved. The third part of the questionnaire mainly understands respondents' attitudes toward FFBS, including whether they agree with FFBS as the mainstream way of the last kilometer of non-motorized trip in the future, the positive significance to accept FFBS long-term rental business and FFBS attraction. The fourth part of the questionnaire focuses on the usage of Free-Floating shared bike, including the number of times for daily use, the length of time for daily use, the impact of private car trips, public transport trips, the situation, brands and problems encountered when use Free-Floating shared bike. The last, what’s the concern for no use of Free-Floating shared bike. The last part of the questionnaire focuses on the respondents' suggestions for better development of Free-Floating bike sharing. The proposal to solve the bike parking chaos, the setting location of the parking site, the advice on the orderly development of Free-Floating bike sharing are all involved. Statistical analysis In the first place, filter the collected data, delete the missing items and the wrong data. Then do the statistical analysis of collected data and association analysis of the issues associated with shared bicycle users. Last, the Kmeans clustering method is used to cluster the data by R language, get the characteristics of different types of people for who use Free-Floating shared bike Indicate references by (Zhang, Y., Yu, Y., Li, T., & Zou, B., 2011) and

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(James, G., Witten, D., Hastie, T., & Tibshirani, R., 2013). Data analysis is only for Free-Floating shared bike users, no detailed analysis of non-users. Results and discussion Topic 1: travel distance and travel time distribution Free-Floating shared bike in the use of the process have the advantages of easy get and placement. Compared with public bicycles, there is no fixed parking station, the usage is more flexible and convenient. But it is still a way to travel by bike, suitable for short distance travel.

Frequency

200 150 100 50 0

0-2km

2-4km 4-6km Distance

6-8km

＞8km

Fig. 3. Travel distance map.

Figure 3 indicate that Nanjing Free-Floating shared bikes are mainly used for 0 to 4km short distance travel of which 2 to 4km travel the most, 6-8 km travel the least number. 200

Frequency

150 100 50 0

<6:00

6:00~8:00

8:00~11:00

Go to school

11:00~13:00

Time of day

13:00~17:00

17:00~19:00

>19:00

Commuting

Official business

Amusement (play, cinema)

Transfer to bus or subway

Leisure fitness

Fig. 4. Travel time and travel destination graph.

From Figure 4, we can see that the peak travel time of the Free-Floating shared bike users is early, noon and late, respectively, the morning peak hours of 8: 00-11: 00 and the evening rush hours of 17:00 to 19:00. There are a few differences in the peak hours of travel groups for different purposes. Two peak hours at the same time superimposed shopping and entertainment, leisure and fitness and other travel needs.

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Topic 2: The focus of the user's choice of travel mode Through the investigation of the focus of the user's choice of travel mode, can help FFBS enterprise to improve service quality and help city management to understand the travel needs of the people, so they can improve the city travel environment, the quality of travel services. Table 2. Points that users concern when they choose to travel. Factor

Time saved

Economically

Cozy

Green

Safe

Especially

61.15%

32.01%

23.62%

20.53%

52.10%

Concern

30.24%

45.92%

45.70%

41.28%

35.10%

Generally

7.95%

20.09%

27.37%

31.13%

11.70%

not

0.22%

1.77%

2.65%

5.08%

0.66%

hardly

0.44%

0.22%

0.66%

1.99%

0.44%

As can be seen from Table 2, the users in Nanjing pay great attention to economic time and security, the relative lack attention to comfort and have low concern for environmental protection. Topic 3: Attract points of Free-Floating bike sharing to the travelers By analyzing attractiveness of Free-Floating bike sharing to travelers, looking for the reasons of rapid development of FFBS and the advantages of using it. Table 3. Analysis of the Attractors of Travelers in Nanjing. Factor

Especially

Attract

Generally

Not

Hardly

Travel convenient

59.82%

34.88%

4.64%

0.66%

0.00%

Cheap rent

23.62%

37.75%

30.02%

6.40%

2.21%

Easy get

35.54%

33.33%

26.05%

4.64%

0.44%

Easy park

40.84%

40.40%

15.01%

2.87%

0.88%

Leisure and fitness

18.32%

34.88%

35.98%

9.27%

1.55%

Easy to pay

28.26%

47.24%

19.87%

4.42%

0.22%

Relax

24.28%

36.64%

30.91%

7.06%

1.10%

Convenient for sightseeing

26.49%

38.63%

28.70%

5.52%

0.66%

Green

35.32%

39.07%

21.85%

2.65%

1.10%

Convenient public transport connections

36.87%

34.00%

24.06%

3.75%

1.32%

From table 3 can be seen, the most attractiveness of FFBS to Nanjing citizen is travel convenience, easy parking, easy to get, convenient public transport connections, easy to pay and green environmental protection. This is benefit from the operating mode of FFBS. FFBS gets support from the Internet platform, people can scan at any time, stop and pay for it at any time without consuming petroleum energy and being environmentally friendly. Therefore, from table 4, the vast majority of Nanjing citizens recognized FFBS as a short-distance non-motor vehicle travel mainstream way in the survey. From table 5, further 43.05% of the respondents recognized the long-rent business of FFBS and said they would use it. FFBS companies can be based on the needs of different users to carry out longterm rental business FFBS, to provide users with a wide range of services. Table 4. The recognized of FFBS as a short-distance travel mainstream way. Factor

whether recognized FFBS as a short-distance non-motor vehicle travel mainstream way

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Strongly agree

43.71%

More agree

11.48%

generally

16.34%

Not agree

28.48%

Hardly agree

0%

9

Table 5. The attitude of FFBS long lease business. Factor

Whether use the long-rent business of FFBS

Yes

43.05%

No

56.95%

Topic 4: Cluster Analysis of the crowd in Nanjing who use FFBS The K-means clustering method is used to calculate the clustering data from R language, the clustering results are four categories. The results were analyzed to obtain the age, sex, education level, average income, occupation status information for each category. Table 6. Demographics per cluster. Attributes

Age

Gender

Education

Average income

Job

Cluster 1

Cluster 2

Cluster 3

Cluster 4

＜12year

0.00%

0.00%

0.00%

1.18%

12-18year

5.95%

13.33%

6.25%

9.47%

18-40year

92.86%

86.67%

83.75%

88.76%

41-65year

1.19%

0.00%

8.75%

0.59%

＞65year

0.00%

0.00%

1.25%

0.00%

Male

51.19%

67.50%

52.50%

58.58%

Female

48.81%

32.50%

47.50%

41.42%

Did not complete high school

0.00%

1.67%

2.50%

1.18%

Did not complete high school

9.52%

6.67%

20.00%

2.96%

Associate's

17.86%

11.67%

26.25%

4.14%

Bachelor’s

65.48%

53.33%

40.00%

62.72%

Graduate

7.14%

26.67%

11.25%

28.99%

＜RMB 60000 yuan

60.71%

58.33%

36.25%

93.49%

RMB 60000-120000 yuan

23.81%

23.33%

47.50%

4.14%

RMB 120000-200000 yuan

9.52%

12.50%

11.25%

1.18%

RMB 200000-300000 yuan

4.76%

2.50%

1.25%

0.59%

＞RMB 300000 yuan

1.19%

3.33%

3.75%

0.59%

Student

40.48%

51.67%

6.25%

91.12%

10

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643

Teacher

3.57%

4.17%

7.50%

5.92%

Employee's

29.76%

23.33%

45.00%

2.96%

Civil servants

3.57%

8.33%

3.75%

0.00%

Worker

3.57%

1.67%

11.25%

0.00%

Doctor

3.57%

2.50%

2.50%

0.00%

Retired

2.38%

0.83%

3.75%

0.00%

Others

13.10%

7.50%

20.00%

0.00%

18.54%

26.49%

17.66%

37.31%

The number of samples per category

From table 6 clustering results can be found, Cluster 1 is consists of mainly by highly educated students and employees, which accounted for 18.54%. Cluster 2 is consists of mainly by highly educated students and employees, such people accounted for 26.49%. Cluster 3 is consists of mainly by employees and other staff, which accounted for 17.66%. Cluster 4 is consists of mainly by college students, which accounted for 37.31%. Table 7. Analysis of the behavior impact of FFBS users in Nanjing per cluster. Attributes

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Average number of times

2.08

2.45

1.85

1.60

5 minutes or less

9.52%

7.50%

8.75%

8.28%

5 to 10 minutes

35.71%

40.00%

40.00%

49.11%

10 to 20 minutes

34.52%

35.83%

33.75%

26.63%

Travel time

20 to 30 minutes

11.90%

10.83%

12.50%

14.20%

30 minutes or more

8.33%

5.83%

5.00%

1.78%

Yes

38.10%

54.17%

46.25%

41.42%

No

61.90%

45.83%

53.75%

58.58%

Yes

73.81%

68.33%

53.75%

69.82%

No

26.19%

31.67%

46.25%

30.18%

Influences on the number of private car trips

Influences on the number of public transport trips

From table 7 we can find that Cluster 2 has the largest usage count of FFBS per day and Cluster 4 has the minimum. Most people use a free floating bike to travel for no more than 20 minutes. For Cluster 1, the impact on the number of private car trips is relatively smaller than the other three categories, which has a greater impact on the number of public transport trips, indicating that such groups mainly rely on public transport in their daily shortdistance trips. While for Cluster 2, the impact on the number of private car trips is greater relative to the other three categories, indicating that such groups in the daily short-distance travel more private car. For Cluster 3, the impact on the number of public transport trips is relatively smaller compared with the other three categories after choose FFBS as a way to travel, indicating that the proportion of employees choosing public transport in their daily trips is relatively smaller than that of other categories. Combined with the previous crowd analysis, it indicates that the proportion of employees choosing public transport in their daily trips is relatively smaller than that of other categories. As a student population of Cluster 4, there is a significant impact on the number of public transport trips using free floating bike.

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Topic 5: Suggestions on Regional Planning of Bicycle Parking in Nanjing The problem of FFBS parking is a huge challenge for urban management. Reasonable planning of parking areas and parking sites can contribute to alleviate the problem bike parking chaos and guide the public to use FFBS normatively. Table 8. Suggestions for FFBS parking sites in Nanjing. Factor

Nearby residential area

Especially

54.75%

Important

Nearby enterprises

nearby Subway or bus station

Nearby school

Near by the mall

Nearby park or tourist attractions

Nearby hotels or hotels

38.63%

61.15%

53.86%

41.50%

48.57%

31.35%

36.87%

37.53%

30.02%

31.13%

33.77%

35.10%

28.92%

Generally

7.73%

21.85%

7.51%

13.02%

20.97%

14.57%

30.68%

Not

0.66%

1.77%

1.32%

1.32%

3.53%

1.32%

7.73%

Hardly

0.00%

0.22%

0.00%

0.66%

0.22%

0.44%

1.32%

From table 8 we can find out that Nanjing residents most want to plan parking area is near the subway station, bus station, residential area, school. This shows that the use of FFBS in Nanjing is mainly used for daily commuter connection. Conclusions The explosive growth of the FFBS in Nanjing has brought challenges to the urban management of Nanjing, so it's necessary to have a detailed understanding of attitudes and usage behaviors of Nanjing residents on the FFBS. The following conclusions can be obtained from the survey results of 453 users. As FFBS has the advantage of convenient parking, easy access, easy payment, green and so on, it is more and more popular among the people of Nanjing. The major cyclists in Nanjing of FFBS are college students, enterprise's staff, aged between 18 and 40 years. The people in Nanjing use FFBS mainly for daily commuter connection, travel, school, and presented morning peak hour, evening rush hour. Among the respondents, 71.52% of FFBS users agree to use FFBS as the mainstream mode of short-distance travel, and 43.05% of users are willing to use FFBS's long-term service. FFBS has an impact on the number of public transportation and private car trips to employees, mainly for short distance public transport trips. The average number of FFBS used in Nanjing is about 2 times a day, and most people travel for less than 20 minutes. This paper analyzes the users of FFBS in Nanjing city. In the future, we can analyze the behavior and attitudes of FFBS users and non-users in Nanjing, and further analyze of the reasons why Nanjing residents do not use FFBS. Users and non-users in the choice of travel mode differences and other issues. Suggestions Through the analysis of the attitudes and behavior of FFBS users in Nanjing city, the needs of urban management departments and FFBS enterprises, the following suggestions are given. It is suggested that Nanjing urban management department should consider planning more bicycle parking areas near public transport and subway stations, near residential areas and schools when planning bicycle parking areas. Reasonable planning and utilization of urban resources for the public to provide better travel services. FFBS is mainly used for school and commute commuter service. It is recommended to share the FFBS into the urban public transport components. Overall planning in the process of urban planning. FFBS is recommended to launch long rental business, to meet the diverse needs of users.

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Jing Chen et al. / Transportation Research Procedia 39 (2019) 634–645 Jing Chen/ Transportation Research Procedia 00 (2018) 000–000

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It share the long-distance business of companies to meet the diversified needs of users. Reference Research In China. (2017)China Free-floating bike 2020.http://www.pday.com.cn/htmls/report/201704/24516157.html.

sharing

Industry

Research

Report

in

2017-

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