Trafik Talep Yönetimi Yaklaşımlarının Sürdürülebilirlik Perspektifinde İncelenmesi ve Politika Önerileri

Abstract

Artan kentleşme, motorlu taşıt sahipliğindeki yükseliş ve değişen hareketlilik talepleri, trafik sıkışıklığını çevresel, ekonomik ve toplumsal boyutları olan çok boyutlu bir politika alanına dönüştürmüştür. Bu bağlamda trafik talep yönetimi (TTY), yeni altyapı yatırımlarına dayalı arz odaklı yaklaşımların sınırlılıklarına alternatif sunan ve mevcut ulaşım sistemlerinin daha verimli kullanılmasını hedefleyen stratejik bir politika aracı olarak öne çıkmaktadır. Bu çalışma, TTY yaklaşımlarını sürdürülebilirliğin çevresel, ekonomik ve sosyal boyutları çerçevesinde ele alarak uluslararası literatürdeki temel eğilimleri ve bulguları yapılandırılmış bir derleme kapsamında incelemeyi amaçlamaktadır. Çalışma, 2000–2025 yılları arasında yayımlanan akademik çalışmalar ve politika belgelerini kapsamakta olup literatür; emisyonların azaltılması ve enerji verimliliği gibi çevresel etkiler, maliyet etkinliği ve ulaştırma verimliliği gibi ekonomik etkiler ile erişilebilirlik, toplumsal kabul ve eşitlik gibi sosyal etkiler temelinde sistematik olarak analiz edilmektedir. Bulgular, sıkışıklık ücretlendirmesi, yönetilen şeritler, düşük emisyonlu bölgeler ve akıllı ulaşım sistemleriyle desteklenen TTY stratejilerinin çevresel ve ekonomik boyutlarda güçlü etkiler sunduğunu göstermektedir. Buna karşılık, sosyal etkiler ve uzun vadeli sonuçlara ilişkin bulguların sınırlı olduğu görülmektedir. Türkiye bağlamında değerlendirmeler, TTY’nin mevcut kapasitenin etkin yönetimine odaklanan bütünleşik ve veri temelli politikalarla daha etkili sonuçlar üretebileceğine işaret etmektedir.

Keywords

• trafik talep yönetimi
• sürdürülebilir ulaşım
• sıkışıklık ücretlendirmesi
• yönetilen şeritler
• politika önerileri

An Examination of Traffic Demand Management Approaches from a Sustainability Perspective and Policy Recommendations

Abstract

Rapid urbanization, rising motor vehicle ownership, and evolving mobility demands have transformed traffic congestion into a multidimensional policy challenge with environmental, economic, and social implications. In this context, traffic demand management (TDM) has emerged as a strategic policy instrument that offers an alternative to supply-oriented approaches reliant on new infrastructure investments by promoting the more efficient use of existing transport systems. This study examines TDM approaches through the environmental, economic, and social dimensions of sustainability by means of a structured review of the international literature. The review covers academic studies and policy documents published between 2000 and 2025. The literature is systematically analyzed with respect to environmental impacts, including emissions reduction and energy efficiency; economic impacts, such as cost effectiveness and transport system efficiency; and social impacts related to accessibility, public acceptance, and equity. Findings indicate that TDM strategies, particularly congestion pricing, managed lanes, low-emission zones, and intelligent transport systems, generate strong effects in environmental and economic terms. However, evidence on social impacts and long-term outcomes remains limited. Evaluations in the Turkish context suggest that integrated, data-driven TDM policies focusing on efficient capacity management can yield more effective results.

Keywords

• traffic demand management
• sustainable transport
• congestion pricing
• managed lanes
• policy recommendations

References

1. Abirami, S., Pethuraj, M., Uthayakumar, M. ve Chitra, P. (2024). A systematic survey on big data and artificial intelligence algorithms for intelligent transportation system. Case Studies on Transport Policy, 17, 101247. https://doi.org/10.1016/j.cstp.2024.101247
2. Afrin, T. ve Yodo, N. (2020). A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 12 (11), 4660. https://doi.org/10.3390/su12114660
3. Aftabuzzaman, M. (2007). Measuring traffic congestion: A critical review [Konferans sunumu]. 30th Australasian Transport Research Forum, London, United Kingdom. https://api.semanticscholar.org/CorpusID:127214818
4. Akbulut, D. (2024). SKUP Türkiye “Kullanım” yılı olarak ilan ettiği 2024’te iki yeni bileşeni hayata geçiriyor. BThaber. https://www.bthaber.com/skup-turkiye-kullanim-yili-olarak-ilan-ettigi-2024te-iki-yeni-bileseni-hayata-geciriyor/
5. Amanor, W. K., Adanu, E. K., Adams, C. A. ve Adi, S. B. (2024). Assessing road users’ preferences for various travel demand management strategies for adoption in Accra, Ghana. Future Transportation, 4(3), 919-937. https://doi.org/10.3390/futuretransp4030044
6. Anell, K. ve Hartmann, D. (2007). Flexible work arrangements in Asia. Boston College Center for Work ve Family. https://www.bc.edu/content/dam/files/centers/cwf/research/publications/researchreports/Executive%20Summary_Flexible%20Work%20Arrangements%20in%20Asia
7. Austroads. (2007). Tools that impact network performance: Road space allocation tools (AP–R311/07). Austroads Ltd. https://austroads.com.au/publications/network/ap-r311-07/media/AP-R311-07.pdf
8. Aydogmus, H. Y. ve Turkan, Y. S. (2020). Application of machine learning methods for passenger demand prediction in transfer stations of İstanbul’s public transportation system. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering. https://doi.org/10.4018/978-1-7998-0301-0.ch011

9. Ayland, N. D. ve Emmott, N. P. (1990). The integration of road transport informatics for demand management [Konferans sunumu]. Third International Conference on Road Traffic Control, 1990, London, United Kingdom. https://download.ssrn.com/19/01/09/ssrn_id3312829_code2547155.pdf
10. Aytekin, A., Korucuk, S. ve Görçün, Ö. (2024). Determining the factors affecting transportation demand management and selecting the best strategy: A case study. Transport Policy, 146, 150-160. https://doi.org/10.1016/j.tranpol.2023.11.003
11. Babaei, A., Khedmati, M., Jokar, M. ve Tirkolaee, E. (2023). Sustainable transportation planning considering traffic congestion and uncertain conditions. Expert Systems with Applications, 227, 119792. https://doi.org/10.1016/j.eswa.2023.119792
12. Banister, D. (2008). The sustainable mobility paradigm. Transport Policy, 15(2), 73-80. https://doi.org/10.1016/j.tranpol.2007.10.005
13. Baradaran, H., Bozorgvar, H., Nourallahzadeh, Z. ve Rasaizadi, A. (2023). Effectiveness of flexible working hours on traffic index, a case study for Tehran. Advance Researches in Civil Engineering, 5(1), 56-69.
14. Bigazzi, A. ve Rouleau, M. (2017). Can traffic management strategies improve urban air quality? A review of the evidence. Journal of Transport and Health, 7, 111-124. https://doi.org/10.1016/J.JTH.2017.08.001
15. Boltze, M. ve Tuan, V. A. (2016). Approaches to achieve sustainability in traffic management. Procedia Engineering, 142, 204-211. https://doi.org/10.1016/j.proeng.2016.02.033
16. Brussels Parlement. (2019). The text of the Brussels government’s general policy statement. https://www.parlement.brussels/texte-de-la-declaration-de-politique-generale-du-gouvernement-bruxellois/
17. Cambridge Systematics. (2005). Traffic congestion and reliability: Trends and advanced strategies for congestion mitigation. United States. Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/20656
18. Canıtez, F. (2024). Sustainable urban mobility in ıstanbul: Transition and planning. Journal of Intelligent Decision Making and Information Science, 1, 45-54. https://doi.org/10.59543/jidmis.v1i.11269
19. CEDR. (2012). Traffic management to reduce congestion. Conference of European Directors of Roads. CEDR’s Secretariat General. https://www.cedr.eu/download/Publications/2013/T12_Traffic_management.pdf
20. CEDR. (2022). Fact sheet on hard shoulder running v2.0. Working Group Traffic and Network Management. https://www.cedr.eu/docs/view/629f3cd6e8920-en
21. Chen, X. G., Zhou, J., Zhu, Z. T. ve Xu, H. L. (2009). Measure of urban traffic supply and demand coupling balance [Konferans sunumu]. 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. https://doi.org/10.1109/FSKD.2009.260
22. Collier, T. ve Goodin, G. (2004). Managed lanes: A cross-cutting study. Operations Office of Transportation Management Federal Highway Administration. https://rosap.ntl.bts.gov/view/dot/39867
23. Conceição, L., Correia, G. H. D. A. ve Tavares, J. P. (2020). The reversible lane network design problem (RL-NDP) for smart cities with automated traffic. Sustainability, 12(3), 1226. https://doi.org/10.3390/su12031226
24. Cracknell, J. A. (2000). Experience in urban traffic management and demand management in developing countries (Final Report). Department for International Development, United Kingdom. https://www.gtkp.com/document/experience-in-urban-traffic-management-and-demand-management-in-developing-countries/
25. Çevre Şehircilik ve İklim Değişikliği Bakanlığı, Türkiye Cumhuriyeti Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2024). Türkiye 2053 uzun dönemli iklim stratejisi. https://iklim.gov.tr/db/turkce/dokumanlar/turkiyenin--8230-3143-20250210095501.pdf
26. Daganzo, C. F. (2007). Urban gridlock: Macroscopic modeling and mitigation approaches. Transportation Research Part B: Methodological, 41(1), 49-62. https://doi.org/10.1016/j.trb.2006.03.001
27. Dang, L., Von Arx, W. ve Frölicher, J. (2021). The impact of on-demand collective transport services on sustainability: A comparison of various service options in a rural and an urban area of Switzerland. Sustainability. 13(6), 3091. https://doi.org/10.3390/SU13063091
28. Dilek, E. ve Dener, M. (2023). Computer vision applications in ıntelligent transportation systems: A survey. Sensors, 23(6), 2938. https://doi.org/10.3390/s23062938
29. Dilek, E., Talih, Ö. ve Bensghir, K. T. (2024). An overview of paradigm shift dynamics in transportation: Use of artificial ıntelligence in ıntelligent transportation systems in Türkiye. Acta Infologica, 8(1), 71-87. https://doi.org/10.26650/acin.1389571
30. Dilek, E., Talih, Ö. ve Ceylan, H. (2023a). Ulusal akıllı ulaşım sistemleri mimarisinin yaygınlaştırılması: Türkiye önerisi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi (JITSA), 6(2), 353-392. https://doi.org/10.51513/jitsa.1309583
31. Dilek, E., Talih, Ö. ve Tektaş, N. (2023b). Akıllı ulaşım sistemlerinde siber güvenlik: Türkiye’de mevcut durum ve öneriler [Konferans sunumu]. 3. Uluslararası Akıllı Ulaşım Sistemleri Konferansı ITSC’23. https://d1wqtxts1xzle7.cloudfront.net/108842078/siber_guvenlik-libre.pdf
32. EDP. (2022). Urban mobility. 5 sustainable trends of smart cities. https://www.edp.com/en/urban-mobility-5-sustainable-trends-smart-cities
33. Elassy, M., Al-Hattab, M., Takruri, M. ve Badawi, S. (2024). Intelligent transportation systems for sustainable smart cities. Transportation Engineering, 16, 100252. https://doi.org/10.1016/j.treng.2024.100252
34. ESMAP. (2004). Toward cleaner urban air in south Asia: Tackling transport pollution, understanding sources. https://www.esmap.org/sites/default/files/esmap-files/FR_SouthAsia_UrbanAirfinal52404.pdf
35. Ewing, R. (2021). Travel demand management. Transportation ve Land Use Innovations. https://doi.org/10.4324/9781351179362-5
36. Fadila, J. N., Wahab, N. H. A., Alshammari, A., Aqarni, A., Al-Dhaqm, A. ve Aziz, N. (2024). Comprehensive review of smart urban traffic management in the context of the fourth ındustrial revolution. IEEE Access, 12, 196866-196886. https://doi.org/10.1109/ACCESS.2024.3509572
37. Fernandes, P., Vilaça, M., Macedo, E., Sampaio, C., Bahmankhah, B., Bandeira, J., Guarnaccia, C., Rafael, S., Fernandes, A. P., Relvas, H., Borrego, C. ve Coelho, M. (2019). Integrating road traffic externalities through a sustainability indicator. The Science of the Total Environment, 691, 483-498. https://doi.org/10.1016/j.scitotenv.2019.07.124
38. Fujii, S. ve Taniguchi, A. (2006). Determinants of the effectiveness of travel feedback programs—a review of communicative mobility management measures for changing travel behaviour in Japan. Transport Policy, 13(5), 339-348. https://doi.org/10.1016/J.TRANPOL.2005.12.007
39. Gai, K., Tang, H., Li, G., Xie, T., Wang, S., Zhu, L. ve Choo, K.-K. R. (2022). Blockchain-based privacy-preserving positioning data sharing for IoT-enabled maritime transportation systems. IEEE Transactions on Intelligent Transportation Systems, 24(2), 2344-2358. https://doi.org/10.1109/TITS.2022.3190487
40. Haldenbilen, S. ve Ceylan, H. (2005). Transport demand management in Turkey: A genetic algorithm approach. Transportation Planning and Technology, 28, 403-426. https://doi.org/10.1080/03081060500515507
41. INRIX. (2025). INRIX 2024 Global traffic scorecard. https://inrix.com/scorecard/
42. IPCC. (2023). AR6 synthesis report: Climate change 2023. https://www.ipcc.ch/report/ar6/syr/
43. Ivanova, D., Yeralina E. ve Shatila K. (2023). Strategies for sustainable transportation in road way system in urban areas. E3S Web of Conf., 389, 5001. https://doi.org/10.1051/e3sconf/202338905001
44. İnce, E. C. (2025). Mapping the path to sustainable urban mobility: A bibliometric analysis of global trends and ınnovations in transportation research. Sustainability, 17(4), 1480. https://doi.org/10.3390/su17041480
45. Javid, R., Xie, J., Wang, L., Yang, W., Javid, R. ve Salari, M. (2021). High-occupancy vehicle (HOV) and high-occupancy toll (HOT) lanes. International Encyclopedia of Transportation 4, 45-51. https://doi.org/10.1016/b978-0-08-102671-7.10292-1
46. Jia, F. (2024). Application of ıntelligent transportation system in modern transportation management [Konferans sunumu]. 2024 International Conference on Computing, Robotics and System Sciences (ICRSS). https://doi.org/10.1109/ICRSS65752.2024.00034
47. Karimi, H., Ghadirifaraz, B., Boushehri, S., Hosseininasab, S. M. ve Rafiei, N. (2021). Reducing traffic congestion and increasing sustainability in special urban areas through one-way traffic reconfiguration. Transportation, 49(1), 37-60. https://doi.org/10.1007/S11116-020-10162-4
48. Kılavuz, T. ve Kisla, R. (2016). Demand management methods for the environment oriented hybrid traffic system to be implemented in Istanbul. Transportation Research Procedia, 14, 3380-3389. https://doi.org/10.1016/J.TRPRO.2016.05.290
49. Kim, Y. ve Kang, S. C. (2011). Innovative traffic demand management strategy. Transportation Research Record, 2245(1), 27-35. https://doi.org/10.3141/2245-04
50. Kwak, J., Ku, D., Jo, J., Kim, D., Bencekri, M., Choi, M. ve Lee, S. (2024). Travel demand management strategies to mitigate climate change. Proceedings of the İnstitution of Civil Engineers-Municipal Engineer, 177(2), 64-75. https://doi.org/10.1680/jmuen.23.00026
51. Li, Q., Xie, Z., Guo, Y., Wei, F., Tian, Y., Zhang, Y. ve Wang, C. (2019). Innovation mechanism of traffic demand management. American Journal of Traffic and Transportation Engineering, 4(4), 132-136. https://doi.org/10.11648/J.AJTTE.20190404.13
52. Li, X., Chen, J. ve Wang, H. (2013). Study on flow direction changing method of reversible lanes on urban arterial roadways in China. Procedia - Social and Behavioral Sciences, 96, 807-816. https://doi.org/10.1016/j.sbspro.2013.08.092
53. Li, X., Yang, H. ve Ke, J. (2023). Booking cum rationing strategy for equitable travel demand management in road networks. Transportation Research Part B: Methodological, 167, 261-274. https://doi.org/10.2139/ssrn.4104689
54. Li, Z., Liang, C., Hong, Y. ve Zhang, Z. (2016). How do on‐demand ridesharing services affect traffic congestion? The moderating role of urban compactness. Production and Operations Management, 31(1), 239-258. https://doi.org/10.1111/poms.13530
55. Lilhore, U., Imoize, A., Li, C.T., Simaiya, S., Pani, S., Goyal, N., Rana, A. ve Lee, C. (2022). Design and Implementation of an ML and IoT based adaptive traffic-management system for smart cities. Sensors, 22(8), 2908. https://doi.org/10.3390/s22082908
56. Ling, S., Ma, S. ve Jia, N. (2022). Sustainable urban transportation development in China: A behavioral perspective. Frontiers of Engineering Management, 9(1), 16-30. https://doi.org/10.1007/s42524-021-0162-4
57. Litman, T. (1999). Potential transportation demand management strategies. Victoria Transport Policy Institute.
58. Litman, T. (2014). Congestion evaluation best practices [Konferans sunumu]. International Transportation Economic Development Conference, Sheraton Dallas Hotel, Dallas, USA. https://vtpi.org/ITED_congestion.pdf
59. Litman, T. (2025). Evaluating transportation economic development ımpacts. https://www.vtpi.org/econ_dev.pdf
60. Mahmoudi, R., Shetab-Boushehri, S. N., Hejazi, S. R. ve Emrouznejad, A. (2019). Determining the relative importance of sustainability evaluation criteria of urban transportation network. Sustainable Cities and Society, 47, 101493. https://doi.org/10.1016/J.SCS.2019.101493
61. Makridis, C., Menelaou, C., Timotheou, S. ve Panayiotou, C. (2024). A real-time demand management and route-guidance system for eliminating congestion. IEEE Intelligent Transportation Systems Magazine, 16, 86-104. https://doi.org/10.1109/MITS.2024.3418513
62. Menelaou, C., Timotheou, S., Kolios, P. ve Panayiotou, C. (2021). Joint route guidance and demand management for real-time control of multi-regional traffic networks. IEEE Transactions on Intelligent Transportation Systems, 23(7), 8302-8315. https://doi.org/10.1109/TITS.2021.3077870
63. Meyer, M. D. (1999). Demand management as an element of transportation policy: Using carrots and sticks to influence travel behavior. Transportation Research Part A: Policy and Practice, 33(7-8), 575-599. https://doi.org/10.1016/S0965-8564(99)00008-7
64. Mobility. (2024). Flexible work hours strategy. Texas A&M Transportation Institute. https://mobility.tamu.edu/mip/strategies.php
65. Municipality Amsterdam. (2024). From environmental zones to emission-free zones. https://www.amsterdam.nl/verkeer-vervoer/milieuzone-uitstootvrijezone/#h4831c120-e0b8-4112-a686-1054ac6b50ad
66. Muslih, M., Abduljabbar, A. ve Joni, H. (2023). Review of traffic demand management strategies. IOP Conference Series: Earth and Environmental Science, 1232(1), 12055. https://doi.org/10.1088/1755-1315/1232/1/012055
67. Othman, B., De Nunzio, G., Domenico, D. ve Canudas-De-Wit, C. (2019). Ecological traffic management: A review of the modeling and control strategies for improving environmental sustainability of road transportation. Annual Reviews in Control, 48, 292-311. https://doi.org/10.1016/j.arcontrol.2019.09.003
68. Palanichamy, V., Vijay, A. ve Moorthy, R. (2023). Smart innovative congestion pricing strategy for Traffic demand management in smart cities using structural equation modelling [Konferans sunumu]. 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS). https://doi.org/10.1109/AICERA/ICIS59538.2023.10420021
69. Pamučar, D., Deveci, M., Canıtez, F. ve Božanić, D. (2020). A fuzzy full consistency method-dombi-bonferroni model for prioritizing transportation demand management measures. Applied Soft Computing, 87, 105952. https://doi.org/10.1016/j.asoc.2019.105952
70. Papadakis, D. M., Savvides, A., Michael, A. ve Michopoulos, A. (2024). Advancing sustainable urban mobility: Insights from best practices and case studies. Fuel Communications, 20, 100125. https://doi.org/10.1016/j.jfueco.2024.100125
71. Pascariu, B., Flensburg, J. V., Pellegrini, P. ve Azevedo, C. M. L. (2025). Formulation and solution framework for real-time railway traffic management with demand prediction. IET Intelligent Transport Systems, 19(1), e12610. https://doi.org/10.1049/itr2.12610
72. Qin, X., Ke, J., Wang, X., Tang, Y. ve Yang, H. (2022). Demand management for smart transportation: A review. Multimodal Transportation, 1(4), 100038. https://doi.org/10.1016/j.multra.2022.100038
73. Ramp, M., Kasis, A., Menelaou, C. ve Timotheou, S. (2024). Stability of regional traffic networks employing maximum throughput demand management. European Journal of Control, 80, 101061. https://doi.org/10.1016/j.ejcon.2024.101061
74. Santosa, G., Behrendtb, H. ve Teytelboymb, A. (2010). Part II: Policy instruments for sustainable road transport. Research in Transportation Economics, 28(1), 46-91. https://doi.org/10.1016/j.retrec.2010.03.002
75. Seilabi, S., Tabesh, M., Davatgari, A., Miralinaghi, M. ve Labi, S. (2020). Promoting autonomous vehicles using travel demand and lane management strategies. Frontiers in Built Environment, 6, 560116. https://doi.org/10.3389/fbuil.2020.560116
76. Shah, K., Pan, S., Lee, I., Kim, H., You, Z., Zheng, J. M. ve Chiang, P. (2021). Green transportation for sustainability: Review of current barriers, strategies, and innovative technologies. Journal of Cleaner Production, 326, 129392. https://doi.org/10.1016/j.jclepro.2021.129392
77. Shao, Y., Wang, C. (Ross), Berres, A., Yoshioka, J., Cook, A. ve Xu, H. (2022). Computer vision-enabled smart traffic monitoring for sustainable transportation management [Konferans sunumu]. International Conference on Transportation and Development 2022. https://doi.org/10.1061/9780784484319.004
78. SKUP İstanbul. (2023). SKUp İstanbul. https://skup.istanbul/hakkinda/
79. SKUP İzmir. (2024). SKUp İzmir. https://www.sumpizmir.org/tr/skup-izmir/
80. SKUP Türkiye. (2022). SKUP Türkiye. https://www.skupturkiye.org.tr/proje-hakkinda-bilgi/
81. SKUP Türkiye. (2023). SKUP Türkiye’den haberler: C. sayı 5. https://www.skupturkiye.org.tr/skup-turkiyeden-haberler-5-skup-turkiye-calisma-agi/
82. SMART Ankara. (2022). SMART Ankara.
83. Su, Q. ve Wang, D. Z. W. (2021). Bottleneck congestion and work starting time distribution considering household travels. arXiv preprint arXiv:2104.00938. https://doi.org/10.48550/arXiv.2104.00938
84. Sultana, S., Salon, D. ve Kuby, M. (2019). Transportation sustainability in the urban context: a comprehensive review. Urban Geography, 40, 279-308. https://doi.org/10.1080/02723638.2017.1395635
85. T. C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2019). On Birinci Kalkınma Planı (2019-2023). https://www.sbb.gov.tr/wp-content/uploads/2019/07/OnbirinciKalkinmaPlani.pdf
86. T. C. Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. (2023). On İkinci Kalkınma Planı. https://onikinciplan.sbb.gov.tr/
87. T.C. Ulaştırma ve Altyapı Bakanlığı. (2025). Teknik destek projeleri. https://ipa.uab.gov.tr/teknik-destek-projeleri
88. Talih, Ö. (2024). Trafik yönetim tekniklerinin incelenmesi: Akıllı ulaşım sistemleri bağlamında trafik yönetiminin geleceği. [Yüksek lisans tezi]. Bandırma Onyedi Eylül Üniversitesi, Fen Bilimleri Enstitüsü. https://tez.yok.gov.tr/
89. Talih, Ö. ve Çelikok, K. (2023). Intelligent transportation systems and policies in the world and in Türkiye: Assessment from a sustainable transportation perspective. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 14(100. Yıl Özel Sayısı), 254-279. https://doi.org/10.21076/vizyoner.1281444
90. Talih, Ö. ve Dilek, E. (2023). Lojistik/E-lojistik süreçlerinde akıllı ulaşım sistemlerinin rolü. AUS Türkiye Bülten, 15, 120-133. AUS Türkiye. https://austurkiye.org.tr/uploads/blog/file_378-15-numarali-bulten-46.pdf
91. Texas Department of Transportation. (2024). Managed lanes. https://www.txdot.gov/discover/express-toll-hov-lanes/managed-lanes.html
92. Tomorrow World Today. (2022). Everything you should know about low emission zones. https://www.tomorrowsworldtoday.com/sustainability/everything-you-should-know-about-low-emission-zones/
93. Türkiye İstatistik Kurumu. (2025a). Adrese dayalı nüfus kayıt sistemi sonuçları, 2024. https://data.tuik.gov.tr/Bulten/Index?p=53783
94. Türkiye İstatistik Kurumu. (2025b). Motorlu kara taşıtları, Haziran 2025. https://data.tuik.gov.tr/Bulten/Index?p=Motorlu-Kara-Tasitlari-Haziran-2025-54042
95. U.S. Department of Transportation. (2024). High occupancy vehicle facilities. Federal Highway Administration. https://ops.fhwa.dot.gov/freewaymgmt/hov.htm
96. Victoria Transport Policy Institute. (2017). Why manage transportation demand? https://www.vtpi.org/tdm/tdm51.htm
97. Walker, J., Blythe, P. ve Pickford, A. (2012). Congestion charging: Learning the lessons and moving forward [Konferans sunumu]. 18th World Congress on Intelligent Transport Systems, Vienna, Austria.
98. Wallace, C., Kilpatrick, A. ve Schneider, K. (1996). Intelligent transportation systems : Applications to transportation demand management (Final Report). Southeastern Transportation Center Consortium, Raleigh, NC (United States).
99. Wang, Q. ve Sun, H. (2019). Traffic structure optimization in historic districts based on green transportation and sustainable development concept. Advances in Civil Engineering, 2019(1), 9196263. https://doi.org/10.1155/2019/9196263
100. Wang, Y., Geng, K., May, A. ve Zhou, H. (2022). The impact of traffic demand management policy mix on commuter travel choices. Transport Policy, 117, 74-87. https://doi.org/10.1016/j.tranpol.2022.01.002
101. Wood, N., Gupta, V., Cardenas, J. P., Hwang, J. ve Raghunathan, D. (2021). National ınventory of specialty lanes and highways: Technical report. U.S. Department of Transportation Federal Highway Administration. https://ops.fhwa.dot.gov/publications/fhwahop20043/fhwahop20043.pdf
102. Xiaojie, T. (2005). Research on knowledge-based methodologies and system for decision making of traffic congestion management. Southeast University, Nanjing.
103. Zhao, W. (2018). The general equilibrium effects of HOV lanes on congestion, sprawl, energy use, and carbon emissions. Social Science Research Network. http://dx.doi.org/10.2139/ssrn.3312829
104. Zhou, Z., Lin, S., Du, W. ve Liang, H. (2019). Integration of regional demand management and signals control for urban traffic networks. IEEE Access, 7, 20235-20248. https://doi.org/10.1109/ACCESS.2019.2897157