Assessing the determinants of passenger satisfaction in public bus transport systems: A fuzzy approach

Öz

Context—Passenger satisfaction is a crucial factor in encouraging the use of public transportation over private vehicles, as it directly affects user loyalty, service effectiveness, and overall urban mobility. Assessing satisfaction in public transport systems is inherently challenging due to the subjective, multidimensional, and uncertain nature of passenger perceptions. The EN 13816 standard offers a comprehensive framework for evaluating public transport service quality through criteria such as accessibility, comfort, safety, security, availability, and information. Nevertheless, traditional evaluation approaches often face difficulties in integrating both qualitative and quantitative aspects of service quality while effectively managing uncertainty in passenger judgments. Therefore, identifying and analyzing the key factors influencing passenger satisfaction is essential for enhancing service performance, increasing ridership, and supporting sustainable transportation policies and urban development goals.

Objective—This study aims to develop a robust framework for assessing passenger satisfaction in public transportation systems by integrating both passenger and operator perspectives. The objective is to identify, prioritize, and evaluate service quality criteria to support informed decision-making for service improvement. The study also seeks to highlight the gaps between passenger expectations and operator assessments, thereby providing actionable insights for transit management.

Method—A hybrid two-level analytic hierarchy process (AHP) integrated with intuitionistic fuzzy sets was employed to address the uncertainty and complexity inherent in multi-criteria decision-making. The AHP method was used to determine the relative importance of service quality criteria, while Intuitionistic fuzzy sets were utilized to capture the vagueness and uncertainty in expert judgments. Transportation experts from diverse disciplines, including strategy development, operations management, and information technology, evaluated the criteria to provide a comprehensive assessment. The proposed framework was validated through a case study of Istanbul’s public bus transportation system. Additionally, gap and sensitivity analyses were conducted to examine the differences between passenger and operator perspectives and to evaluate the robustness of the model.

Results—The analysis revealed that safety and security are the most influential factors for both passengers and operators. From the operator perspective, service availability is equally critical, whereas passengers rank availability second, followed by comfort and travel time. The gap analysis highlighted areas of consensus, such as accessibility, as well as significant differences in comfort and information criteria, indicating priority areas for improvement. The sensitivity analysis confirmed that the model is responsive to variations in expert judgments, emphasizing the importance of carefully selecting evaluators.

Conclusion—The integrated AHP–Intuitionistic Fuzzy Sets framework provides a reliable and adaptable tool for evaluating passenger satisfaction in public transportation systems. By identifying key service quality criteria and highlighting gaps between passenger and operator perceptions, the proposed model supports targeted improvements in service planning and management. The findings provide practical guidance for transit operators to enhance passenger satisfaction, increase ridership, and contribute to the development of more sustainable urban mobility systems.

Anahtar Kelimeler

• Passenger satisfaction
• Public transport
• EN 13816
• Fuzzy AHP

Otobüs toplu ulaşım sistemlerinde yolcu memnuniyetini etkileyen faktörlerin değerlendirilmesi: Bulanık bir yaklaşım

Öz

Arka Plan—Yolcu memnuniyeti, kullanıcı sadakatini, hizmet verimliliğini ve kentsel hareketliliği doğrudan etkilediği için özel araçlar yerine toplu taşıma kullanımının teşvik edilmesinde kritik bir faktördür. Toplu taşımada memnuniyetin değerlendirilmesi, yolcu algılarının öznel, karmaşık ve belirsiz doğası nedeniyle zorludur. EN 13816 standardı; erişebilirlik, konfor, emniyet, güvenlik, uygunluk ve bilgilendirme gibi kriterleri içeren yapılandırılmış bir kalite değerlendirme çerçevesi sunmaktadır. Ancak geleneksel değerlendirme yöntemleri, hizmet kalitesinin hem nitel hem nicel boyutlarını yakalamakta ve yargılardaki belirsizliği ele almakta yetersiz kalmaktadır. Bu nedenle yolcu memnuniyetini etkileyen temel faktörlerin belirlenmesi, hizmet performansının iyileştirilmesi ve sürdürülebilir ulaşım politikalarının desteklenmesi açısından büyük önem taşımaktadır.

Amaç—Bu çalışma, yolcu memnuniyetinin değerlendirilmesine yönelik sağlam bir çerçeve geliştirmeyi amaçlamaktadır. Bu çerçeve, hem yolcu hem de işletmeci bakış açılarını bütünleştirerek toplu taşıma sistemlerinde hizmet kalitesinin değerlendirilmesini sağlamaktadır. Çalışmanın amacı, hizmet iyileştirmesine yönelik bilinçli karar alma süreçlerini desteklemek için hizmet kalitesi kriterlerini belirlemek, önceliklendirmek ve değerlendirmektir. Ayrıca çalışma, yolcu beklentileri ile işletmeci değerlendirmeleri arasındaki farklılıkları ortaya koymayı ve böylece toplu taşıma yönetimine uygulanabilir çıkarımlar sunmayı hedeflemektedir.

Yöntem—Çok kriterli karar verme sürecindeki belirsizlik ve karmaşıklığı ele almak amacıyla iki seviyeli Analitik Hiyerarşi Süreci (AHP) ile Sezgisel (Intuitionistic) Bulanık Kümelerin hibrit bir modeli kullanılmıştır. AHP yöntemi, hizmet kalitesi kriterlerinin göreli önemini belirlemek için kullanılırken, Sezgisel Bulanık Kümeler uzman yargılarındaki belirsizliği modellemek için uygulanmıştır. Strateji geliştirme, operasyon yönetimi ve bilgi teknolojileri gibi farklı disiplinlerden ulaşım uzmanları kriterleri değerlendirerek bütüncül bir analiz sağlamıştır. Çerçeve, İstanbul’un toplu otobüs taşımacılığı sistemi üzerinde gerçekleştirilen bir vaka çalışması ile doğrulanmıştır. Ayrıca yolcu ve işletmeci bakış açıları arasındaki farklılıkları incelemek ve modelin sağlamlığını değerlendirmek amacıyla boşluk analizi ve duyarlılık analizi yapılmıştır.

Bulgular—Analiz sonuçları, hem yolcular hem de işletmeciler açısından emniyet ve güvenliğin en etkili faktörler olduğunu göstermiştir. İşletmeci perspektifinde uygunluk ile emniyet ve güvenlik ile eşit derecede önemli bulunurken, yolcular hizmet sunumunu ikinci sırada, ardından konfor ve seyahat süresini sıralamıştır. Boşluk analizi, erişilebilirlik kriterinde görüş birliği olduğunu; konfor ve bilgilendirme kriterlerinde ise önemli farklılıklar bulunduğunu ortaya koymuştur. Bu durum, iyileştirme öncelik alanlarını işaret etmektedir. Duyarlılık analizi, modelin uzman yargılarındaki değişimlere duyarlı olduğunu göstermiş ve değerlendiricilerin dikkatle seçilmesinin önemini vurgulamıştır.

Sonuç—Entegre AHP–Sezgisel Bulanık Kümeler çerçevesi, toplu taşımada yolcu memnuniyetini değerlendirmek için güvenilir ve uyarlanabilir bir araç sunmaktadır. Temel hizmet kalitesi kriterlerini belirleyerek ve yolcu ile işletmeci algıları arasındaki boşlukları ortaya koyarak, model hizmet planlama ve yönetiminde hedef odaklı iyileştirmeleri desteklemektedir. Bulgular, yolcu memnuniyetini artırmak, yolcu talebini yükseltmek ve daha sürdürülebilir kentsel ulaşım sistemlerine katkı sağlamak için işletmecilere pratik rehberlik sunmaktadır.

Anahtar Kelimeler

• Yolcu memnuniyeti
• Toplu taşıma
• EN 13816
• Bulanık AHP

Kaynakça

1. G. Griffin, G. Gleason, R. Preiss, D. Shevenaugh, “Best practices for customer satisfaction in manufacturing firms”, Sloan Management Review, 36(2), 87–98, 1995.
2. G. Akyıldız Alçura, S. Ş. Kuşakcı Gündoğar, A. Saracoğlu, “A decision tree approach utilizing Quadrant analysis for satisfaction in high-speed rail systems”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 31(2), 267-275, 2025. https://doi.org/10.5505/pajes.2024.33348.
3. M. O. Olgun, G. Özdemir, “Önem-memnuniyet analizi ve Taguchi metodu ile eğitimde iyileştirme önceliklerinin belirlenmesi: bir uygulama örneği”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 19(2), 81-87, 2013. https://doi.org/10.5505/pajes.2013.00710.
4. A. Kanwar, M. Sanjeeva, “Student satisfaction survey: A key for quality improvement in higher education institutions”, Journal of Innovation and Entrepreneurship, 11, 27, 2022. https://doi.org/10.1186/s13731-022-00196-6.
5. G. Mihelis, E. Grigoroudis, Y. Siskos, Y. Politis, Y. Malandrakis, “Customer satisfaction measurement in the private bank sector”, European Journal of Operational Research, 130(2), 347–360, 2001. https://doi.org/10.1016/S0377-2217(00)00036-9.
6. K. Bancsik, C. D. N. Ilea, M. D. Daina, R. Bancsik, C. L. Șuteu, S. D. Bîrsan, F. Manole, L. G. Daina, “Comparative analysis of patient satisfaction surveys: A crucial role in raising the standard of healthcare services”, Healthcare, 11(21), 2878, 2023. https://doi.org/10.3390/healthcare11212878.
7. M. Fuchs, K. Weiermair, “New perspectives of satisfaction research in tourism destinations”, Tourism Review, 58(3), 6–14, 2003. https://doi.org/10.1108/eb058411.
8. S. Duleba, S. Moslem, “User satisfaction survey on public transport by a new PAHP-based model”, Applied Sciences, 11(21), 10256, 2021. https://doi.org/10.3390/app112110256.

9. D. Heerwegh, “Mode differences between face-to-face and web surveys: An experimental investigation of data quality and social desirability effects”, International Journal of Public Opinion Research, 21(1), 111–121, 2009. https://doi.org/10.1093/ijpor/edn054.
10. E. Barabino, E. Deiana, P. Tilocca, “Measuring service quality in urban bus transport: A modified SERVQUAL approach”, International Journal of Quality and Service Sciences, 4(3), 238–252, 2012. https://doi.org/10.1108/17566691211269567.
11. J. Ž. Mikuličić, I. Kolanović, A. Jugović, D. Brnos, “Evaluation of service quality in passenger transport with a focus on liner maritime passenger transport: A systematic review”, Sustainability, 16(3), 1125, 2024. https://doi.org/10.3390/su16031125.
12. L. Redman, M. Friman, T. Gärling, T. Hartig, “Quality attributes of public transport that attract car users: A research review”, Transport Policy, 25, 119-127, 2013. https://doi.org/10.1016/j.tranpol.2012.11.005.
13. J. Černý, A. Černá, B. Linda, “Support of decision-making on economic and social sustainability of public transport”, Transport, 29(1), 59-68, 2014. https://doi.org/10.3846/16484142.2014.897645.
14. J. P. Li, L. Q. Xu, D. Yao, Y. W. Mao, “Impacts of symbolic value and passenger satisfaction on bus use”, Transportation Research Part D: Transport and Environment, 72, 98–113, 2019. https://doi.org/10.1016/j.trd.2019.04.012.
15. S. O. Olorunfemi, A. O. Adeniran, “Assessment of passengers’ satisfaction of public transport system in Akure–Owo axis, Nigeria”, American International Journal of Multidisciplinary Scientific Research, 4(1), 1–16, 2018. https://doi.org/10.46281/aijmsr.v4i1.142.
16. ]M. M. Zefreh, B. Hussain, T. Sipos, “In-depth analysis and model development of passenger satisfaction with public transportation”, KSCE Journal of Civil Engineering, 24(10), 3064–3073, 2020. https://doi.org/10.1007/s12205-020-1871-7.
17. A. N. Ni, C. Q. Zhang, Y. T. Hu, W. T. Lu, H. W. Li, “Influence mechanism of the corporate image on passenger satisfaction with public transport in China”, Transport Policy, 94, 54–65, 2020. https://doi.org/10.1016/j.tranpol.2020.04.014.
18. Ö. Kökalan, A. Tutan, “Passenger satisfaction scale for public transportation”, Transportation Research Record, 2675(3), 44–52, 2021. https://doi.org/10.1177/0361198120961382.
19. B. Henriquez-Jara, J. Arriagada, A. Tirachini, “Impact of real-time information on passenger satisfaction across varying public transport quality levels in 13 Chilean cities”, Transportation Research Part A: Policy and Practice, 200, 104622, 2025. https://doi.org/10.1016/j.tra.2025.104622.
20. E. Ruiz, W. F. Yushimito, L. Aburto, R. de la Cruz, “Predicting passenger satisfaction in public transportation using machine learning models,” Transportation Research Part A: Policy and Practice, 181, 103995, 2024. https://doi.org/10.1016/j.tra.2024.103995.
21. L. C. Yang, H. S. Yang, J. Q. Tang, Y. Lu, J. X. Liu, “Assessing attribute performance and older adults’ satisfaction with transit services: Implications for age-friendly planning”, Travel Behaviour and Society, 40, 101026, 2025. https://doi.org/10.1016/j.tbs.2025.101026.
22. J. B. dos Santos, J. P. Lima, “Quality of public transportation based on the multi-criteria approach and from the perspective of user's satisfaction level: A case study in a Brazilian city”, Case Studies on Transport Policy, 9(3), 1233–1244, 2021. https://doi.org/10.1016/j.cstp.2021.05.015.
23. S. Ibrahim, Y. G. Zhang, A. Ahmad, “Rail-based public transportation service quality and customer satisfaction: A decade of insights and advances”, Public Transport, 17(1), 45–69, 2025. https://doi.org/10.1007/s12469-024-00382-7.
24. L. Eboli, G. Mazzulla, “Relationships between rail passengers’ satisfaction and service quality: A framework for identifying key service factors”, Public Transport, 7(2), 185–201, 2015. https://doi.org/10.1007/s12469-014-0096-x.
25. Y. Q. Saw, D. Dissanayake, F. Ali, T. Bentotage, “Passenger satisfaction towards metro infrastructures, facilities and services”, Transportation Research Procedia, 48, 3980–3995, 2020. https://doi.org/10.1016/j.trpro.2020.08.290.
26. X. H. Li, L. Huang, Q. Li, H. C. Liu, “Passenger satisfaction evaluation of public transportation using Pythagorean fuzzy MULTIMOORA method under large group environment”, Sustainability, 12(12), 4996, 2020. https://doi.org/10.3390/su12124996.
27. Y. H. Zheng, H. Kong, G. Petzhold, M. M. Barcelos, C. P. Zegras, J. H. Zhao, “User satisfaction and service quality improvement priority of bus rapid transit in Belo Horizonte, Brazil”, Case Studies on Transport Policy, 9(4), 1900–1911, 2021. https://doi.org/10.1016/j.cstp.2021.10.011.
28. J. de Oña, “Service quality, satisfaction and behavioral intentions towards public transport from the point of view of private vehicle users”, Transportation, 49(1), 237–269, 2022. https://doi.org/10.1007/s11116-021-10175-7.
29. K. Atanassov, “Intuitionistic fuzzy sets”, Fuzzy Sets and Systems, 20(1), 87–96, 1986. https://doi.org/10.1016/S0165-0114(86)80034-3.
30. K. T. Atanassov, “Type-1 fuzzy sets and intuitionistic fuzzy sets”, Algorithms, 10(3), 106, 2017. https://doi.org/10.3390/a10030106.
31. W. Z. Wang, X. W. Liu, “Some operations over Atanassov's intuitionistic fuzzy sets based on Einstein t-norm and t-conorm”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(2), 263–276, 2013. https://doi.org/10.1142/S0218488513500141.
32. D. H. Hong, C. H. Choi, “Multicriteria fuzzy decision-making problems based on vague set theory”, Fuzzy Sets and Systems, 114(1), 103–113, 2000. https://doi.org/10.1016/S0165-0114(98)00271-1.
33. R. Humić, B. Abramović, “Criteria for the quality of services of public interest organized by train operators”, Transportation Research Procedia, 40, 259–264, 2019. https://doi.org/10.1016/j.trpro.2019.07.039.
34. Transportation, Logistics and Services – Public Passenger Transport – Service Quality Definition, Targeting and Measurement, European Standards, BS EN 13816:2002, https://www.en-standard.eu/search/?q=BS+EN+13816%3A2002 (01.03.2026).
35. L. D. Burns, T. F. Golob, “The role of accessibility in basic transportation choice behavior”, Transportation, 5, 175–198, 1976. https://doi.org/10.1007/BF00167272.
36. S. M. Chen, J. M. Tan, “Handling multicriteria fuzzy decision-making problems based on vague set theory”, Fuzzy Sets and Systems, 67(2), 163–172, 1994. https://doi.org/10.1016/0165-0114(94)90084-1.
37. R. Wawryszczuk, E. Kardas-Cinal, J. Lejk, M. Sokołowski, “Methods of passenger ride comfort evaluation—Tests for metro cars”, Sensors, 23(12), 5741, 2023. https://doi.org/10.3390/s23125741.
38. H. Cornet, S. Stadler, P. Kong, G. Marinkovic, F. Frenkler, P. M. Sathikh, “User-centred design of autonomous mobility for public transportation in Singapore”, Transportation Research Procedia, 41, 191–203, 2019. https://doi.org/10.1016/j.trpro.2019.09.038.
39. Y. Guessous, M. Aron, N. Bhouri, S. Cohen, “Estimating travel time distribution under different traffic conditions”, Transportation Research Procedia, 3, 339–348, 2014. https://doi.org/10.1016/j.trpro.2014.10.014.
40. B. W. Y. Siu, H. K. Lo, “Punctuality-based route and departure time choice”, Transportmetrica A: Transport Science, 10(7), 585–621, 2014. https://doi.org/10.1080/18128602.2012.751681.
41. L. Mallia, L. Lazuras, C. Violani, F. Lucidi, “Crash risk and aberrant driving behaviors among bus drivers: The role of personality and attitudes towards traffic safety”, Accident Analysis and Prevention, 79, 145–151, 2015. https://doi.org/10.1016/j.aap.2015.03.034.
42. A. Tirachini, “Estimation of travel time and the benefits of upgrading the fare payment technology in urban bus services”, Transportation Research Part C: Emerging Technologies, 30, 239–256, 2013. https://doi.org/10.1016/j.trc.2011.11.007.
43. L. Evans, “Transportation safety”, Handbook of Transportation Science, R. W. Hall, Eds., Boston, MA, USA, Springer, 1999, Ch. 4, 63–108. https://doi.org/10.1007/978-1-4615-5203-1_4.
44. M. J. Smith, “Addressing the security needs of women passengers on public transport”, Security Journal, 21(1-2), 117–133, 2008. https://doi.org/10.1057/palgrave.sj.8350071.
45. S. Potter, “Transport energy and emissions: Urban public transport”, Handbook of Transport and the Environment, D. A. Hensher, K. J. Button, Eds., Emerald Group Publishing Limited, 2003, Ch. 13, 247–262. https://doi.org/10.1108/9781786359513-013.
46. M. García-Valiñas, F. Arbués, R. Balado-Naves, “Assessing environmental profiles: An analysis of water consumption and waste recycling habits”, Journal of Environmental Management, 348, 119247, 2023. https://doi.org/10.1016/j.jenvman.2023.119247.
47. L. A. Zadeh, “Fuzzy sets”, Information and Control, 8(3), 338–353, 1965. https://doi.org/10.1016/S0019-9958(65)90241-X.
48. Y. M. Wang, Y. Luo, Z. Hua, “On the extent analysis method for fuzzy AHP and its applications”, European Journal of Operational Research, 186(2), 735–747, 2008. https://doi.org/10.1016/j.ejor.2007.01.050.
49. H. Bustince, E. Barrenechea, M. Pagola, J. Fernandez, Z. S. Xu, B. Bedregal, J. Montero, H. Hagras, F. Herrera, B. De Baets, “A historical account of types of fuzzy sets and their relationships”, IEEE Transactions on Fuzzy Systems, 24(1), 179–194, 2016. https://doi.org/10.1109/TFUZZ.2015.2451692.
50. K. T. Atanassov, “Intuitionistic fuzzy sets”, Intuitionistic fuzzy sets: theory and applications, Berlin, Heidelberg, Springer-Verlag, 1999. https://doi.org/10.1007/978-3-7908-1870-3_1.
51. Z. S. Xu, H. C. Liao, “Intuitionistic fuzzy analytic hierarchy process”, IEEE Transactions on Fuzzy Systems, 22(4), 749–761, 2014. https://doi.org/10.1109/TFUZZ.2013.2272585.
52. S. Faizi, W. Sałabun, T. Rashid, S. Zafar, J. Wątróbski, “Intuitionistic fuzzy sets in multi-criteria group decision making problems using the characteristic objects method”, Symmetry, 12(9), 1382, 2020. https://doi.org/10.3390/sym12091382.
53. F. Kutlu, K. Göleli, “Intuitionistic fuzzy any relation clustering algorithm based on similarity matrix integration with intuitionistic fuzzy C-means and differential evolution optimization”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 32(4), 718-728, 2026. https://doi.org/10.65206/pajes.76350.
54. D. Ç. Yılmaz, H. Gerçek, “Analitik Hiyerarşi Yöntemi ile İstanbul'da Bütünleşik Bisiklet Ağı Kümelerinin Önceliklendirilmesi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(6), 215–224, 2014. https://doi.org/10.5505/pajes.2014.29292.
55. Ş. Gür, M. Hamurcu, T. Eren, “Ankara’da Monoray projelerinin analitik hiyerarşi prosesi ve 0-1 hedef programlama yöntemleri ile seçimi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(4), 437–443, 2017. https://doi.org/10.5505/pajes.2016.03903.
56. Y. Ş. Murat, O. Kulak, “Ulaşım ağlarında bilgi aksiyomu kullanılarak güzergah (rota) seçimi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 11(3), 425–435, 2005. https://izlik.org/JA22SP29TF.
57. X. L. Song, L. Zhong, Z. Zhang, J. P. Xu, C. Shen, F. Peña-Mora, “Multistakeholder conflict minimization–based layout planning of construction temporary facilities”, Journal of Computing in Civil Engineering, 32(2), 04017080, 2018. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000725.
58. T. L. Saaty, “Analytic hierarchy process”, Encyclopedia of Operations Research and Management Science, Third Edition, S. I. Gass, M. C. Fu, Eds., Boston, MA, USA, Springer, 2013, 52–64. https://doi.org/10.1007/978-1-4419-1153-7_31.
59. R. C. B. Leonelli, “Enhancing a Decision Support Tool with Sensitivity Analysis”, M.S. Thesis, University of Manchester, Manchester, United Kingdom, 2012.
60. İETT, “Kısa Tarihçe”, https://iett.istanbul/icerik/kisa-tarihce (01.05.2026).
61. İETT, “2024 Yılı Faaliyet Raporu”, https://iett.istanbul/BBImages/Slider/Image/i%CC%87ett-genel-mu%CC%88du%CC%88rlu%CC%88g%CC%86u%CC%88_2024-faaliyet-raporu-1.pdf (1.5.2026).
62. İETT, “Raporlar”, https://iett.istanbul/icerik/Raporlar (1.5.2026).
63. H. C. Tsai, A. S. Lee, H. N. Lee, C. N. Chen, Y. C. Liu, “An application of the fuzzy Delphi method and fuzzy AHP on the discussion of training indicators for the regional competition, Taiwan national skills competition, in the trade of joinery”, Sustainability, 12(10), 4290, 2020. https://doi.org/10.3390/su12104290.
64. J. F. Wiley, L. A. Pace, “Chapter 16: Modern Statistical Methods II”, Beginning R: An Introduction to Statistical Programming, Berkeley, CA, USA, Apress, 2015, 193–213. https://doi.org/10.1007/978-1-4842-0373-6_16.
65. S. A. S. Shishavan, F. K. Gündoğdu, E. Farrokhizadeh, Y. Donyatalab, C. Kahraman, “Novel similarity measures in spherical fuzzy environment and their applications”, Engineering Applications of Artificial Intelligence, 94, 103837, 2020. https://doi.org/10.1016/j.engappai.2020.103837.
66. Y. J. Wang, L. Z. Hou, M. Li, R. X. Zheng, “A novel fire risk assessment approach for large-scale commercial and high-rise buildings based on fuzzy analytic hierarchy process (FAHP) and coupling revision”, International Journal of Environmental Research and Public Health, 18(13), 7187, 2021. https://doi.org/10.3390/ijerph18137187.
67. Y. L. Yang, S. Goodwin, “What-why analysis of expert interviews: Analysing geographically-embedded flow data,” Proceeding of the 12th IEEE Pacific Visualization Symposium (IEEE PacificVis), Bangkok, Thailand, 23-26 April 2019, 122–126. https://doi.org/10.1109/PacificVis.2019.00022.
68. Transport for London (TfL), “Bus Safety”, https://tfl.gov.uk/corporate/safety-and-security/road-safety/bus-safety (19.06.2026).
69. Singapore, “Road Safety Profile”, https://asiantransportobservatory.org/documents/399/Singapore_road_safety_profile_2025.pdf (19.06.2026).
70. İETT, “Metrobüs Sistemine Uluslararası Yol Trafik Güvenliği Belgesi”, https://iett.istanbul/arsiv/38159/metrobus-sstemne-uluslararas-yol-trafk-guvenl (19.06.2026).
71. İETT, “Gece Hatları”, https://iett.istanbul/icerik/gece-hatlari (19.06.2026).
72. İETT, “Metroya Yeni ve Ücretsiz Entegre Hatlar”, https://iett.istanbul/Announcement/Detail?Id=2097 (19.06.2026).
73. Transports Metropolitans de Barcelona (TMB), “The public transport service on demand”, https://www.tmb.cat/en/barcelona/bus-on-demand (19.06.2026).