Javascript is required
1.
E. Fishman, S. Washington, N. Haworth, and A. Watson, “Factors influencing bike share membership: An analysis of Melbourne and Brisbane,” Transport. Res. A-Pol., vol. 71, pp. 17-30, 2015. [Google Scholar] [Crossref]
2.
A. Faghih-Imani and N. Eluru, “Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system,” J. Transp. Geogr., vol. 54, pp. 218-227, 2016. [Google Scholar] [Crossref]
3.
J. Zhao, J. Wang, and W. Deng, “Exploring bikesharing travel time and trip chain by gender and day of the week,” Transport. Res. C-Emer., vol. 58, pp. 251-264, 2015. [Google Scholar] [Crossref]
4.
S. Shaheen, E. Martin, and A. Cohen, “Public bikesharing and modal shift behavior: A comparative study of early bikesharing systems in North America,” Int. J. Transp., vol. 1, no. 1, Article ID: 2013, 2013. [Google Scholar] [Crossref]
5.
B. K. Sovacool, C. Daniels, and A. AbdulRafiu, “Transitioning to electrified, automated and shared mobility in an African context: A comparative review of Johannesburg, Kigali, Lagos and Nairobi,” J. Transp. Geogr., vol. 98, Article ID: 103256, 2022. [Google Scholar] [Crossref]
6.
S. D. Parkes, G. Marsden, S. A. Shaheen, and A. P. Cohen, “Understanding the diffusion of public bikesharing systems: evidence from Europe and North America,” J. Transp. Geogr., vol. 31, pp. 94-103, 2013. [Google Scholar] [Crossref]
7.
“Rwanda launches Africa’s first public bike-share transport system,” Sisiafrika, 2021, https://www.sisiafrika.com/rwanda-launches-africas-first-public-bike-share-transport-system/. [Google Scholar]
8.
“Community bike-share programs in Africa: Challenges and benefits,” Ethicaltraveler, 2011, https://ethicaltraveler.org/2011/08/community-bike-share-programs-in-africa-challenges-and-benefits/. [Google Scholar]
9.
“Electric bike share scheme to bring new experience in Kigali’s transport,” Ktpress, 2022, https://www.ktpress.rw/2022/07/electric-bike-share-scheme-to-bring-new-experience-in-kigalis-transport/. [Google Scholar]
10.
“Rwanda invests in cycling, helps boost clean air and jobs,” Africarenewal, 2020, https://www.un.org/africarenewal/magazine/november-december-2020/rwanda-invests-cycling-helps-boost-clean-air-and-jobs. [Google Scholar]
11.
E. Ayyildiz, “Fermatean fuzzy step-wise Weight Assessment Ratio Analysis (SWARA) and its application to prioritizing indicators to achieve sustainable development goal-7,” Renew. Energ., vol. 193, pp. 136-148, 2022. [Google Scholar] [Crossref]
12.
E. Ayyildiz and A. Taskin, “A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul,” Socio-Econ. Plan. Sci., vol. 83, Article ID: 101345, 2022. [Google Scholar] [Crossref]
13.
Ž. Stević, M. B. Bouraima, M. Subotić, Y. Qiu, P. A. Buah, K. M. Ndiema, and C. M. Ndjegwes, “Assessment of causes of delays in the road construction projects in the benin republic using fuzzy PIPRECIA method,” Math. Probl. Eng., vol. 2022, Article ID: 5323543, 2022. [Google Scholar] [Crossref]
14.
M. Kovač, S. Tadić, M. Krstić, and M. B. Bouraima, “Novel Spherical Fuzzy MARCOS Method for Assessment of Drone-Based City Logistics Concepts,” Complexity, vol. 2021, Article ID: 2374955, 2021. [Google Scholar] [Crossref]
15.
F. K. Gündoğdu, “Analyzing critical barriers of smart energy city in Turkey based on two-dimensional uncertainty by hesitant z-fuzzy linguistic terms,” Eng. Appl. Artifi. Intel., vol. 113, Article ID: 104935, 2022. [Google Scholar] [Crossref]
16.
M. B. Bouraima, Y. Qiu, Ž. Stević, and V. Simić, “Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model,” Socio-Econ. Plan. Sci., vol. 2022, Article ID: 101475, 2022. [Google Scholar] [Crossref]
17.
Y. Ataei, A. Mahmoudi, M. R. Feylizadeh, and D. F. Li, “Ordinal priority approach (OPA) in multiple attribute decision-making,” Appl. Soft Comput., vol. 86, Article ID: 105893, 2020. [Google Scholar] [Crossref]
18.
A. Mahmoudi, X. Deng, S. A. Javed, and N. Zhang, “Sustainable supplier selection in megaprojects: grey ordinal priority approach,” Bus. Strateg. Environ., vol. 30, no. 1, pp. 318-339, 2021. [Google Scholar] [Crossref]
19.
T. K. Quartey-Papafio, S. Islam, and A. R. Dehaghani, “Evaluating suppliers for healthcare centre using ordinal priority approach,” Manag. Sci. Bus. Decis., vol. 1, no. 1, pp. 5-11, 2021. [Google Scholar] [Crossref]
20.
D. Pamucar, M. Deveci, I. Gokasar, M. Tavana, and M. Köppen, “A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms,” Technol. Forecast. Soc., vol. 182, Article ID: 121778, 2022. [Google Scholar] [Crossref]
21.
M. K. Bah and S. Tulkinov, “Evaluation of Automotive Parts Suppliers through Ordinal Priority Approach and TOPSIS,” Manag. Sci. Bus. Decis., vol. 2, no. 1, pp. 5-17, 2022. [Google Scholar] [Crossref]
22.
M. Sadeghi, A. Mahmoudi, and X. Deng, “Adopting distributed ledger technology (DLT) for the sustainable construction industry: Evaluating the barriers using ordinal priority approach (OPA),” Environ. Sci. Pollut. Res., vol. 29, pp. 10495-10520, 2021. [Google Scholar] [Crossref]
23.
A. Mahmoudi and S. A. Javed, “Probabilistic approach to multi-stage supplier evaluation: Confidence level measurement in ordinal priority approach,” Group Decis. Negot., vol. 31, pp. 1051-1096, 2022. [Google Scholar] [Crossref]
24.
M. B. Bouraima, C. K. Kiptum, K. M. Ndiema, Y. Qiu, and I. Tanackov, “Prioritization road safety strategies towards zero road traffic injury using ordinal priority approach,” Oper. Res. Eng. Sci: Theor. and Appl., vol. 5, no. 2, pp. 206-221, 2022. [Google Scholar] [Crossref]
25.
A. Bendarag, J. Bakkas, M. Hanine, and O. Boutkhoum, “PyOPAsolver: A python based tool for ordinal priority approach operations and normalization,” SoftwareX, vol. 20, Article ID: 101226, 2022. [Google Scholar] [Crossref]
26.
D. Pamucar, M. Deveci, I. Gokasar, L. Martínez, and M. Köppen, “Prioritizing transport planning strategies for freight companies towards zero carbon emission using ordinal priority approach,” Comput. Ind. Eng., vol. 169, pp. 108259-108259, 2022. [Google Scholar] [Crossref]
27.
M. Sadeghi, A. Mahmoudi, X. Deng, and X. Luo, “Prioritizing requirements for implementing blockchain technology in construction supply chain based on circular economy: Fuzzy Ordinal Priority Approach,” Int. J. Environ. Sci. Technol., vol. 2022, pp. 1-22, 2022. [Google Scholar] [Crossref]
28.
M. Deveci, P. R. Brito-Parada, D. Pamucar, and E. A. Varouchakis, “Rough sets based Ordinal Priority Approach to evaluate sustainable development goals (SDGs) for sustainable mining,” Resour. Policy, vol. 79, Article ID: 103049, 2022. [Google Scholar] [Crossref]
29.
M. B. Bouraima, Y. Qiu, C. K. Kiptum, and K. M. Ndiema, “Evaluation of factors affecting road maintenance in Kenyan counties using the Ordinal Priority Approach,” J. Comput. Cogn. Eng., vol. 2022, pp. 1-6, 2022. [Google Scholar] [Crossref]
30.
M. Deveci, D. Pamucar, I. Gokasar, W. Pedrycz, and X. Wen, “Autonomous bus operation alternatives in urban areas using fuzzy Dombi-Bonferroni operator based decision making model,” In IEEE Transactions on Intelligent Transportation Systems, vol. 2022, pp. 1-10, 2022. [Google Scholar] [Crossref]
31.
M. Deveci, D. Pamucar, I. Gokasar, M. Köppen, and B. B. Gupta, “Personal mobility in metaverse with autonomous vehicles using q-rung Orthopair fuzzy sets based OPA-RAFSI model,” IEEE T. Intell. Transp., vol. 2022, pp. 1-10, 2022. [Google Scholar] [Crossref]
32.
M. Abdel-Basset, M. Mohamed, A. Abdel-Monem, and M. A. Elfattah, “New extension of ordinal priority approach for multiple attribute decision-making problems: design and analysis,” Complex Intell. Syst., vol. 8, pp. 4955-4970, 2022. [Google Scholar] [Crossref]
33.
E. Eren and B. Y. Katanalp, “Fuzzy-based GIS approach with new MCDM method for bike-sharing station site selection according to land-use types,” Sustain. Cities Soc., vol. 76, Article ID: 103434, 2022. [Google Scholar]
34.
X. Liang, T. Chen, M. Ye, H. Lin, and Z. Li, “A hybrid fuzzy BWM-VIKOR MCDM to evaluate the service level of bike-sharing companies: A case study from Chengdu, China,” J. Clean. Prod., vol. 298, Article ID: 126759, 2021. [Google Scholar] [Crossref]
35.
M. S. Bahadori, A. B. Gonçalves, and F. Moura, “A GIS-MCDM method for ranking potential station locations in the expansion of bike-sharing systems,” Axioms, vol. 11, no. 6, pp. 263-263, 2022. [Google Scholar] [Crossref]
36.
M. Cheng and W. Wei, “An AHP-DEA Approach of the bike-sharing spots selection problem in the free-floating bike-sharing system,” Discrete Dyn. Nat. Soc., vol. 2020, Article ID: 7823971, 2020. [Google Scholar] [Crossref]
37.
A. Liu, R. Wang, J. Fowler, and X. Ji, “Improving bicycle sharing operations: A multi-criteria decision-making approach,” J. Clean. Prod., vol. 297, Article ID: 126581, 2021. [Google Scholar] [Crossref]
38.
K. Kavta and A. K. Goswami, “A methodological framework for a priori selection of travel demand management package using fuzzy MCDM methods,” Transport., vol. 48, no. 6, pp. 3059-3084, 2021. [Google Scholar] [Crossref]
39.
T. Y. Lee, M. H. Jeong, S. B. Jeon, and J. M. Cho, “Location optimization of bicycle-sharing stations using multiple-criteria decision making,” Sensor. Mater., vol. 32, no. 12, pp. 4463-4470, 2020. [Google Scholar] [Crossref]
40.
M. He, X. Ma, and Y. Jin, “Station importance evaluation in dynamic bike-sharing rebalancing optimization using an entropy-based TOPSIS approach,” IEEE Access, vol. 9, pp. 38119-38131, 2021. [Google Scholar] [Crossref]
41.
C. C. Hsu, J. J. Liou, H. W. Lo, and Y. C. Wang, “Using a hybrid method for evaluating and improving the service quality of public bike-sharing systems,” J. Clean. Prod., vol. 202, pp. 1131-1144, 2018. [Google Scholar] [Crossref]
42.
Z. P. Tian, J. Q. Wang, J. Wang, and H. Y. Zhang, “A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha,” J. Clean. Prod., vol. 171, pp. 1068-1083, 2018. [Google Scholar] [Crossref]
43.
M. Kabak, M. Erbaş, C. Cetinkaya, and E. Özceylan, “A GIS-based MCDM approach for the evaluation of bike-share stations,” J. Clean. Prod., vol. 201, pp. 49-60, 2018. [Google Scholar] [Crossref]
44.
P. Midgley, “The role of smart bike-sharing systems in urban mobility,” Journeys, vol. 2, no. 1, pp. 23-31, 2009. [Google Scholar]
45.
E. Fishman and P. Schepers, “The Safety of Bike Share Systems,” Int. Transp. Forum, vol. 2018, Article ID: 168, 2018. [Google Scholar]
Search
Open Access
Research article

Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach

clement kiprotich kiptum1,
mouhamed bayane bouraima2*,
željko stević3,
sam okemwa1,
sammy birech1,
yanjun qiu2
1
Department of Civil and Structural Engineering, School of Engineering, University of Eldoret, 1125-30100 Eldoret, Kenya
2
School of Civil Engineering, Southwest Jiaotong University, 610031 Chengdu, China
3
Faculty of Transport and Traffic Engineering, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina
Journal of Engineering Management and Systems Engineering
|
Volume 1, Issue 2, 2022
|
Pages 43-50
Received: 11-04-2022,
Revised: 12-05-2022,
Accepted: 12-19-2022,
Available online: 12-30-2022
View Full Article|Download PDF

Abstract:

Over 700 bike-sharing systems are currently in operation worldwide, and the number of systems has grown quickly in recent years. Rwanda's bike-sharing system has only recently begun operations and has encountered numerous challenges. The current study used an Ordinal Priority Approach (OPA) to examine these challenges and provide an acceptable strategy for overcoming them. Five strategies have been established. These strategies are prioritized using four criteria. The results indicate that “theft” and “damage of some bikes when being returned” are the most critical challenges while the first alternative “improving the current bike infrastructure to better serve the bike share system” is the appropriate strategy to overcome these challenges for a successful operation of the bike share system. Taking into account the findings, recommendations were provided to help local administrative bodies handle these challenges.

Keywords: Strategy, Bike-sharing system, Operation, Ordinal priority approach, Rwanda

1. Introduction

Bike-sharing systems have been adopted globally in recent years as a result of local authorities' efforts to encourage environmentally friendly transportation modes and improvements in information systems [1]. Bike-sharing systems are designed to give people greater satisfaction and flexibility in using bicycles while removing the expense and responsibility connected with bicycle ownership [2]. Shared bikes are particularly well suited for small distance or one-way journeys because they are utilized on an “as-needed” premise and people can decide to take a trip in a short amount of time [3]. Additionally, by encouraging the utilization of bicycles for frequent commutes and leisure travel, bike-sharing systems help cut down on emissions and fuel consumption, alleviate traffic congestion, and help people meet their prescribed exercise goals by incorporating physical work into daily existence [4].

Many cities have recognized the advantages of cycling and have encouraged bike-sharing systems [5]. The primary idea behind these systems is to provide inexpensive or free accessibility to bicycles for shorter journeys in cities as an alternative to automobile transportation. Such arrangements relieve the user of the responsibility of purchasing and maintaining a bicycle. Bike-sharing systems become a component of the public transportation system in the city by offering convenient sites from which the bikes may be taken up and left, which is advantageous to many individuals [6].

Whilst bike-sharing initiatives have been introduced across several European cities since the early 1960s, Rwanda is the first African nation to do so because it has steadily positioned itself to embrace technological advancements. When dock stations are built adjacent to bus crossings and provide a ground-breaking last-mile solution, bike-share systems can easily connect with other means of transportation, according to the documentation from the Netherlands, Copenhagen City Bikes, and Rennes Vélo a la Carte Bikeshare.

The largest disadvantage, though, is theft and vandalism. In the past few years, the excesses of this tendency have resulted in the demise of bike-sharing systems in new markets. Because of frequent reports of stolen and/or damaged bikes, MO Bike ceased operations in Manchester and removed approximately 2000 bikes in September 2018. The bikes were either broken, spray-painted, or thrown into canals. Texas Monthly published a story on how five bike-share firms turned Dallas into “the bike-share capital of America” with more than 18000 bikes, but then lost almost all of them in a single night. Rwanda is not excluded from this situation, although it is extremely difficult to figure out the exact rate of theft and vandalism occurring due to the absence of reliable statistics. This study covers the bike-sharing systems and the major challenges related to them using Kigali city in Rwanda as an example.

Richard [7] demonstrated through a report how Kigali has worked with a public transportation enterprise to offer Rwandans comfortable, efficient, cost-effective, and ecologically friendly micro-mobility solutions. Nardini [8] discusses the benefits of African community bike-sharing systems. He claims that, while the system is intended to make public transportation more accessible, bike-share systems in developing areas have significant challenges. According to Sabiiti [9], bike-sharing systems could be a feasible option for the existing problems users encounter in Kigali city. Diana et al. [10] demonstrated why Rwanda invests in cycling, which contributes to cleaner air and more jobs.

The bike-sharing systems in Africa are one of the topics covered in the literature [7], [8], [9]. But up till now, no previous research examined the challenges to their systems and remedial strategies for overcoming them. There are numerous strategies available, and the operators of the bike-share systems need to choose the suitable one. Recommending a strategy for the successful operation of the bike-share systems based on specific criteria can lead to poor decisions. As a result, various criteria should be considered, and an appropriate multi-decision-making tool should be used [11], [12]. When determining which strategy is the most appropriate, multi-decision-making approaches can be quite helpful [13], [14].

Multi-decision-making techniques are used in a variety of fields and are critical in determining the best option from several choices [15], [16]. These decision-making models are carried out utilizing proper mathematical methodologies. Multi-decision-making approaches are helpful means for assisting policymakers who are involved in the evaluation process. Based on the OPA approach, this study presents a methodology to examine the critical challenges to the operation of the bike-sharing system in Kigali (Rwanda) as well as the appropriate strategy to overcome them.

After the introduction, literature is first provided. Next, the OPA methodology is presented. Then, the application of the methodology is explained. After that, the results are discussed. Finally, the conclusion is given including future directions and limitations.

2. Literature

Two sub-sections sections have been presented below.

2.1 Research Related to the OPA Technique

After the introduction of the Ordinal Priority Approach by Ataei et al. [17], it has been used in several areas such as the choice of supplier in megaprojects [18], healthcare supplier [19], metaverse evaluation for sustainable transport [20], supplier of automobile portions [21], sustainable construction sector [22], multi-phase supplier assessment [23], road safety [24], software application [25], transport planning [26], blockchain technology [27], sustainable mining [28], road maintenance [29], autonomous bus operation [30], personal mobility assessment [31], and robot choice [32].

Table 1. Application of MCDM on bike-sharing systems

Authors

Region

Methodology

Subject

Eren and Turkey [33]

Turkey

FL, AHP, VIKOR

Bike-sharing station site choice

Liang et al. [34]

China

BWM, VIKOR

Bike-sharing enterprise service level

Bahadori et al. [35]

Portugal

AHP, TOPSIS

Station locations of the bike-sharing system

Cheng and Wei [36]

China

DEA, AHP

Bike-sharing spot choice issue

Liu et al. [37]

China

DEMATEL, ANP

Bike-sharing operation enhancement

Kavta and Goswami [38]

India

DEMATEL, ANP, VIKOR

Travel demand management choice

Lee et al. [39]

Republic of Korea

MOORA

Position optimization of the bicycle-sharing scheme

He et al. [40]

China

TOPSIS

Bike-sharing rebalancing development

Hsu et al. [41]

Taiwan

DEMATEL, ANP, VIKOR

Service quality assessment of bike-sharing

Tian et al. [42]

China

BWM, MULTIMOORA

Bike-sharing performance assessment

Kabak et al. [43]

Turkey

AHP, MOORA

Bike-sharing stations assessment

2.2 MCDM on Bike-Sharing Systems Assessment

A wide range of studies has been conducted in the context of evaluating bus-sharing systems. Table 1 shows numerous applications of MCDM techniques used in this topic.

3. Ordinal Priority Approach Methodology

In this study, the strategies are prioritized to find out the appropriate one after assessment of criteria and the expert's significance to overcome the challenges to the bike-sharing system in Kigali City, Rwanda. Following the subsequent research of Ataei et al. [17], three steps have been applied as bellows.

Step 1: Evaluating the impediments parameters to the bike-sharing system.

Step 2: Determination of the ordinal desire of impediment parameters.

Step 3: Establishment of the linear model (1) based on collection data through steps 1 and 2, and analysis of the model through a suitable Excel sheet.

$\begin{array}{} {Max Z} \\{S.t.} \\{Z \leq i\left(j\left(k\left(W_{i j k}^k-W_{i j k}^{k+1}\right)\right)\right) \quad \forall {\,\,i, j\,\, and\,\, k}} \\{Z \leq i j m W_{i j k}{ }^m \quad \forall {\,\,i, j\,\, and\,\, k}} \\{\sum_{i=1}^p \sum_{j=1}^n \sum_{k=1}^m W_{i j k}=1} \\{W_{i j k} \geq 0 \quad \forall {\,\,i, j\,\, and\,\, k}} \\ \text{with Z-unconditional in indication}\end{array}$
(1)

After analyzing the model, the significance of alternatives, criteria, and experts is determined through Eqns. (2) to (4), respectively.

$W_k=\sum_{i=1}^p \sum_{i=1}^n W_{i j k} \quad \forall k$
(2)
$W_j=\sum_{i=1}^p \sum_{i=1}^m W_{i j k} \quad \forall j$
(3)
$W_i=\sum_{i=1}^n \sum_{i=1}^m W_{i j k} \quad \forall i$
(4)

The methodology applied in this study employs simple steps for the determination of requisite weights in absence of assistance from other techniques.

4. Application Method

The data was gathered from distinct experts using the hierarchical framework shown in Figure 1. Five strategies were proposed for allowing a successful operation of the bike-sharing system. These strategies were selected depending on their influence on resolving the system's primary challenges.

Figure 1. Implementation of an effective bike-sharing system safeguarding procedures

Three experts who are all employed by the public bike-share (PBS) transportation system company took part in the survey. They each work for the company for more than five years. Expert decisions were based on four criteria namely theft (C1), roads not well sealed (C2), some bikes may be damaged in return (C3), and high dependency on the single-occupancy vehicle (C4). Criteria have been ranked according to their severity. The first position is offered to the factor that poses the greatest challenge to the bike-sharing system. Experts 1 and 2 have classified the criteria as follows: C1 > C3 > C2 > C4, while the ranking of expert 3 is C3 > C1 > C4 > C2. Expert 1 has given C1 the first position. According to him, C1 is the most challenging element of the bike-sharing system. Meanwhile, C4 is the last position for expert 1, explaining why C4 is the least challenging element for the bike-sharing system. The OPA is used to prioritize strategies. The benefit of using the model is that it prevents data normalization. Table 2 and Table 3 summarize the data gathered.

Table 2. Ranking of criteria

C1

C2

C3

C4

E-1

1

3

2

4

E-2

1

3

2

4

E-3

2

4

1

3

Table 3. Ranking of strategies

S1

S2

S3

S4

S5

E-1

C1

2

3

1

4

5

C3

1

2

5

4

3

C2

1

3

5

4

2

C4

2

1

5

4

3

E-2

C1

1

4

3

2

5

C3

2

5

1

3

4

C2

1

5

2

3

4

C4

4

3

5

2

1

E-3

C1

1

4

2

3

5

C3

1

2

3

4

5

C2

3

2

1

4

5

C4

4

2

5

3

1

“E” means an expert

5. Results and Discussion

Table 4. Weights and ranking of components of the OPA

Weight

Ranking

Experts

E-1

0.55

1

E-2

0.27

2

E-3

0.18

3

Criteria

C1

0.44

1

C2

0.15

3

C3

0.28

2

C4

0.13

4

Figure 2. Strategies for the successful operation of bike-sharing systems

In this section, the model's three components-experts, criteria (challenges), and alternatives (strategies)—were each given a weight using Eqns. (2)-(4). They were then ranked in decreasing order, with a lower weight denoting a lower rank. Table 4 displays the expert and criteria weights and rankings.

As indicated in Table 4, the theft factor (C1) has occupied the first position with a weight of 0.44. This result confirms those analyses of Midgley [44] and Fishman and Schepers [45] which indicate that theft is among one the most frequent problems of bike-sharing systems. The least significant criterion remains the fourth criterion C4 (high dependency on the single-occupancy vehicle). When considering the alternatives (strategies), “S1-improving the existing bike infrastructure to better serve the bike share system” is the most appropriate strategy followed by “S3- check-in staff and apparatus required for inspection of the bike after use” (See Figure 2).

6. Conclusion

An effective strategy framework is proposed for the successful operation of the bike-sharing system based on the challenges to the bike-sharing system. Based on the literature review, four criteria are evaluated- theft, roads not well sealed, some bikes may be damaged in return, and high dependency on the single-occupancy vehicle. The survey incorporates the perspectives of three professionals. The theft criterion is found to be the most challenging problem for the bike-sharing system, followed by the damage to certain bikes when being returned while the least complicated issue is the reliance on single-occupancy vehicles. The most appropriate strategy is to improve the current bike infrastructure to better serve the bike share system.

Given the detrimental effects of these issues on bike-sharing systems, it is initially recommended that local administrative organizations, particularly the City of Kigali, create more bike lanes to alleviate concerns about inadequate cycling infrastructure. Furthermore, to ensure the security of bike stations and bikes/scooters, these local administrative organizations should ensure that stations/bikes are outfitted with tracking systems such as GPS to avoid theft. This research is important because it will be helpful to the government of Rwanda in developing regulations and laws that could enhance Rwanda's means of transportation.

This study is new in the Kigali city context, involving the application of decision-making tools to examine the critical factor impeding the successful operation of the bike-sharing system and figure out how to overcome them. The implemented technique demonstrated not only how decisions were made without a decision-making matrix, but also the experts' capacity to selectively evaluate components for which they have sufficient knowledge and experience. According to the application outcomes, the applied technique can be described as an appropriate evaluation process that regulators and administrators may utilize to draw important judgments in the bike-sharing systems. As a result, the method described here can be applied in a variety of circumstances.

The method's main weakness is that it doesn't take into account circumstances where experts are unsure about their decisions. This study can be expanded by including more requirements on the mathematical modeling during the optimization of multi-criteria due to some very dynamic natural circumstances and the procedural necessity of unclear and misinformation. Another weakness in this study is the evidence that just four criteria and the views of three experts were accounted for. Future studies may use additional criteria subdivided into categories based on social, economic, and environmental factors for an in-depth examination. Additionally, there should be a wider variety of professional backgrounds. Furthermore, a national study that looks at more than merely Kigali City is necessary.

Data Availability

The data supporting our research results are included within the article.

Acknowledgments

We are grateful to those who have provided us with data for analysis. We are also grateful to the reviewers for their precious time.

Conflicts of Interest

There are no conflicts of interest related to this work publication.

References
1.
E. Fishman, S. Washington, N. Haworth, and A. Watson, “Factors influencing bike share membership: An analysis of Melbourne and Brisbane,” Transport. Res. A-Pol., vol. 71, pp. 17-30, 2015. [Google Scholar] [Crossref]
2.
A. Faghih-Imani and N. Eluru, “Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system,” J. Transp. Geogr., vol. 54, pp. 218-227, 2016. [Google Scholar] [Crossref]
3.
J. Zhao, J. Wang, and W. Deng, “Exploring bikesharing travel time and trip chain by gender and day of the week,” Transport. Res. C-Emer., vol. 58, pp. 251-264, 2015. [Google Scholar] [Crossref]
4.
S. Shaheen, E. Martin, and A. Cohen, “Public bikesharing and modal shift behavior: A comparative study of early bikesharing systems in North America,” Int. J. Transp., vol. 1, no. 1, Article ID: 2013, 2013. [Google Scholar] [Crossref]
5.
B. K. Sovacool, C. Daniels, and A. AbdulRafiu, “Transitioning to electrified, automated and shared mobility in an African context: A comparative review of Johannesburg, Kigali, Lagos and Nairobi,” J. Transp. Geogr., vol. 98, Article ID: 103256, 2022. [Google Scholar] [Crossref]
6.
S. D. Parkes, G. Marsden, S. A. Shaheen, and A. P. Cohen, “Understanding the diffusion of public bikesharing systems: evidence from Europe and North America,” J. Transp. Geogr., vol. 31, pp. 94-103, 2013. [Google Scholar] [Crossref]
7.
“Rwanda launches Africa’s first public bike-share transport system,” Sisiafrika, 2021, https://www.sisiafrika.com/rwanda-launches-africas-first-public-bike-share-transport-system/. [Google Scholar]
8.
“Community bike-share programs in Africa: Challenges and benefits,” Ethicaltraveler, 2011, https://ethicaltraveler.org/2011/08/community-bike-share-programs-in-africa-challenges-and-benefits/. [Google Scholar]
9.
“Electric bike share scheme to bring new experience in Kigali’s transport,” Ktpress, 2022, https://www.ktpress.rw/2022/07/electric-bike-share-scheme-to-bring-new-experience-in-kigalis-transport/. [Google Scholar]
10.
“Rwanda invests in cycling, helps boost clean air and jobs,” Africarenewal, 2020, https://www.un.org/africarenewal/magazine/november-december-2020/rwanda-invests-cycling-helps-boost-clean-air-and-jobs. [Google Scholar]
11.
E. Ayyildiz, “Fermatean fuzzy step-wise Weight Assessment Ratio Analysis (SWARA) and its application to prioritizing indicators to achieve sustainable development goal-7,” Renew. Energ., vol. 193, pp. 136-148, 2022. [Google Scholar] [Crossref]
12.
E. Ayyildiz and A. Taskin, “A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul,” Socio-Econ. Plan. Sci., vol. 83, Article ID: 101345, 2022. [Google Scholar] [Crossref]
13.
Ž. Stević, M. B. Bouraima, M. Subotić, Y. Qiu, P. A. Buah, K. M. Ndiema, and C. M. Ndjegwes, “Assessment of causes of delays in the road construction projects in the benin republic using fuzzy PIPRECIA method,” Math. Probl. Eng., vol. 2022, Article ID: 5323543, 2022. [Google Scholar] [Crossref]
14.
M. Kovač, S. Tadić, M. Krstić, and M. B. Bouraima, “Novel Spherical Fuzzy MARCOS Method for Assessment of Drone-Based City Logistics Concepts,” Complexity, vol. 2021, Article ID: 2374955, 2021. [Google Scholar] [Crossref]
15.
F. K. Gündoğdu, “Analyzing critical barriers of smart energy city in Turkey based on two-dimensional uncertainty by hesitant z-fuzzy linguistic terms,” Eng. Appl. Artifi. Intel., vol. 113, Article ID: 104935, 2022. [Google Scholar] [Crossref]
16.
M. B. Bouraima, Y. Qiu, Ž. Stević, and V. Simić, “Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model,” Socio-Econ. Plan. Sci., vol. 2022, Article ID: 101475, 2022. [Google Scholar] [Crossref]
17.
Y. Ataei, A. Mahmoudi, M. R. Feylizadeh, and D. F. Li, “Ordinal priority approach (OPA) in multiple attribute decision-making,” Appl. Soft Comput., vol. 86, Article ID: 105893, 2020. [Google Scholar] [Crossref]
18.
A. Mahmoudi, X. Deng, S. A. Javed, and N. Zhang, “Sustainable supplier selection in megaprojects: grey ordinal priority approach,” Bus. Strateg. Environ., vol. 30, no. 1, pp. 318-339, 2021. [Google Scholar] [Crossref]
19.
T. K. Quartey-Papafio, S. Islam, and A. R. Dehaghani, “Evaluating suppliers for healthcare centre using ordinal priority approach,” Manag. Sci. Bus. Decis., vol. 1, no. 1, pp. 5-11, 2021. [Google Scholar] [Crossref]
20.
D. Pamucar, M. Deveci, I. Gokasar, M. Tavana, and M. Köppen, “A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms,” Technol. Forecast. Soc., vol. 182, Article ID: 121778, 2022. [Google Scholar] [Crossref]
21.
M. K. Bah and S. Tulkinov, “Evaluation of Automotive Parts Suppliers through Ordinal Priority Approach and TOPSIS,” Manag. Sci. Bus. Decis., vol. 2, no. 1, pp. 5-17, 2022. [Google Scholar] [Crossref]
22.
M. Sadeghi, A. Mahmoudi, and X. Deng, “Adopting distributed ledger technology (DLT) for the sustainable construction industry: Evaluating the barriers using ordinal priority approach (OPA),” Environ. Sci. Pollut. Res., vol. 29, pp. 10495-10520, 2021. [Google Scholar] [Crossref]
23.
A. Mahmoudi and S. A. Javed, “Probabilistic approach to multi-stage supplier evaluation: Confidence level measurement in ordinal priority approach,” Group Decis. Negot., vol. 31, pp. 1051-1096, 2022. [Google Scholar] [Crossref]
24.
M. B. Bouraima, C. K. Kiptum, K. M. Ndiema, Y. Qiu, and I. Tanackov, “Prioritization road safety strategies towards zero road traffic injury using ordinal priority approach,” Oper. Res. Eng. Sci: Theor. and Appl., vol. 5, no. 2, pp. 206-221, 2022. [Google Scholar] [Crossref]
25.
A. Bendarag, J. Bakkas, M. Hanine, and O. Boutkhoum, “PyOPAsolver: A python based tool for ordinal priority approach operations and normalization,” SoftwareX, vol. 20, Article ID: 101226, 2022. [Google Scholar] [Crossref]
26.
D. Pamucar, M. Deveci, I. Gokasar, L. Martínez, and M. Köppen, “Prioritizing transport planning strategies for freight companies towards zero carbon emission using ordinal priority approach,” Comput. Ind. Eng., vol. 169, pp. 108259-108259, 2022. [Google Scholar] [Crossref]
27.
M. Sadeghi, A. Mahmoudi, X. Deng, and X. Luo, “Prioritizing requirements for implementing blockchain technology in construction supply chain based on circular economy: Fuzzy Ordinal Priority Approach,” Int. J. Environ. Sci. Technol., vol. 2022, pp. 1-22, 2022. [Google Scholar] [Crossref]
28.
M. Deveci, P. R. Brito-Parada, D. Pamucar, and E. A. Varouchakis, “Rough sets based Ordinal Priority Approach to evaluate sustainable development goals (SDGs) for sustainable mining,” Resour. Policy, vol. 79, Article ID: 103049, 2022. [Google Scholar] [Crossref]
29.
M. B. Bouraima, Y. Qiu, C. K. Kiptum, and K. M. Ndiema, “Evaluation of factors affecting road maintenance in Kenyan counties using the Ordinal Priority Approach,” J. Comput. Cogn. Eng., vol. 2022, pp. 1-6, 2022. [Google Scholar] [Crossref]
30.
M. Deveci, D. Pamucar, I. Gokasar, W. Pedrycz, and X. Wen, “Autonomous bus operation alternatives in urban areas using fuzzy Dombi-Bonferroni operator based decision making model,” In IEEE Transactions on Intelligent Transportation Systems, vol. 2022, pp. 1-10, 2022. [Google Scholar] [Crossref]
31.
M. Deveci, D. Pamucar, I. Gokasar, M. Köppen, and B. B. Gupta, “Personal mobility in metaverse with autonomous vehicles using q-rung Orthopair fuzzy sets based OPA-RAFSI model,” IEEE T. Intell. Transp., vol. 2022, pp. 1-10, 2022. [Google Scholar] [Crossref]
32.
M. Abdel-Basset, M. Mohamed, A. Abdel-Monem, and M. A. Elfattah, “New extension of ordinal priority approach for multiple attribute decision-making problems: design and analysis,” Complex Intell. Syst., vol. 8, pp. 4955-4970, 2022. [Google Scholar] [Crossref]
33.
E. Eren and B. Y. Katanalp, “Fuzzy-based GIS approach with new MCDM method for bike-sharing station site selection according to land-use types,” Sustain. Cities Soc., vol. 76, Article ID: 103434, 2022. [Google Scholar]
34.
X. Liang, T. Chen, M. Ye, H. Lin, and Z. Li, “A hybrid fuzzy BWM-VIKOR MCDM to evaluate the service level of bike-sharing companies: A case study from Chengdu, China,” J. Clean. Prod., vol. 298, Article ID: 126759, 2021. [Google Scholar] [Crossref]
35.
M. S. Bahadori, A. B. Gonçalves, and F. Moura, “A GIS-MCDM method for ranking potential station locations in the expansion of bike-sharing systems,” Axioms, vol. 11, no. 6, pp. 263-263, 2022. [Google Scholar] [Crossref]
36.
M. Cheng and W. Wei, “An AHP-DEA Approach of the bike-sharing spots selection problem in the free-floating bike-sharing system,” Discrete Dyn. Nat. Soc., vol. 2020, Article ID: 7823971, 2020. [Google Scholar] [Crossref]
37.
A. Liu, R. Wang, J. Fowler, and X. Ji, “Improving bicycle sharing operations: A multi-criteria decision-making approach,” J. Clean. Prod., vol. 297, Article ID: 126581, 2021. [Google Scholar] [Crossref]
38.
K. Kavta and A. K. Goswami, “A methodological framework for a priori selection of travel demand management package using fuzzy MCDM methods,” Transport., vol. 48, no. 6, pp. 3059-3084, 2021. [Google Scholar] [Crossref]
39.
T. Y. Lee, M. H. Jeong, S. B. Jeon, and J. M. Cho, “Location optimization of bicycle-sharing stations using multiple-criteria decision making,” Sensor. Mater., vol. 32, no. 12, pp. 4463-4470, 2020. [Google Scholar] [Crossref]
40.
M. He, X. Ma, and Y. Jin, “Station importance evaluation in dynamic bike-sharing rebalancing optimization using an entropy-based TOPSIS approach,” IEEE Access, vol. 9, pp. 38119-38131, 2021. [Google Scholar] [Crossref]
41.
C. C. Hsu, J. J. Liou, H. W. Lo, and Y. C. Wang, “Using a hybrid method for evaluating and improving the service quality of public bike-sharing systems,” J. Clean. Prod., vol. 202, pp. 1131-1144, 2018. [Google Scholar] [Crossref]
42.
Z. P. Tian, J. Q. Wang, J. Wang, and H. Y. Zhang, “A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: A case of smart bike-sharing programs in Changsha,” J. Clean. Prod., vol. 171, pp. 1068-1083, 2018. [Google Scholar] [Crossref]
43.
M. Kabak, M. Erbaş, C. Cetinkaya, and E. Özceylan, “A GIS-based MCDM approach for the evaluation of bike-share stations,” J. Clean. Prod., vol. 201, pp. 49-60, 2018. [Google Scholar] [Crossref]
44.
P. Midgley, “The role of smart bike-sharing systems in urban mobility,” Journeys, vol. 2, no. 1, pp. 23-31, 2009. [Google Scholar]
45.
E. Fishman and P. Schepers, “The Safety of Bike Share Systems,” Int. Transp. Forum, vol. 2018, Article ID: 168, 2018. [Google Scholar]

Cite this:
APA Style
IEEE Style
BibTex Style
MLA Style
Chicago Style
Kiptum, C. K., Bouraima, M. B., Stević, Ž., Okemwa, S., Birech, S., & Qiu, Y. J. (2022). Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach. J. Eng. Manag. Syst. Eng., 1(2), 43-50. https://doi.org/10.56578/jemse010201
C. K. Kiptum, M. B. Bouraima, Ž. Stević, S. Okemwa, S. Birech, and Y. L. Qiu, "Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach," J. Eng. Manag. Syst. Eng., vol. 1, no. 2, pp. 43-50, 2022. https://doi.org/10.56578/jemse010201
@research-article{Kiptum2022SustainableSF,
title={Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach},
author={Clement Kiprotich Kiptum and Mouhamed Bayane Bouraima and žEljko Stević and Sam Okemwa and Sammy Birech and Yanjun Qiu},
journal={Journal of Engineering Management and Systems Engineering},
year={2022},
page={43-50},
doi={https://doi.org/10.56578/jemse010201}
}
Clement Kiprotich Kiptum, et al. "Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach." Journal of Engineering Management and Systems Engineering, v 1, pp 43-50. doi: https://doi.org/10.56578/jemse010201
Clement Kiprotich Kiptum, Mouhamed Bayane Bouraima, žEljko Stević, Sam Okemwa, Sammy Birech and Yanjun Qiu. "Sustainable Strategies for the Successful Operation of the Bike-Sharing System Using an Ordinal Priority Approach." Journal of Engineering Management and Systems Engineering, 1, (2022): 43-50. doi: https://doi.org/10.56578/jemse010201
cc
©2022 by the author(s). Published by Acadlore Publishing Services Limited, Hong Kong. This article is available for free download and can be reused and cited, provided that the original published version is credited, under the CC BY 4.0 license.