Impacts of Artificial Intelligence on the Transportation Sector

Öz

When we look at the economic development of the world in the historical process, it is seen that there are various breaking points called the industrial revolution or industrial revolution. This period covers the period from 1760 to the present day. The 4th Industrial Revolution, now called Industry 4.0, basically refers to a structure that tries to integrate the industry with information technologies and performs the production process by providing coordination and optimization conditions completely independent of humans. In our age, technology is becoming more and more involved in human life every day and has an extraordinary dimension, including the use of robots and artificial intelligence in the production process. Countries and companies that try to meet people's expectations and make life easier are transferring more and more resources to this field. Historically, it has its beginnings in philosophy, fiction and imagination, but today Artificial Intelligence (AI), deep learning, machine learning and neural networks represent highly complex and powerful machine learning-based techniques that are not being used to solve a variety of real-world problems. Therefore, it has become a science that focuses on providing solutions to real world problems. It is now possible to see Artificial Intelligence systems in many different sectors such as health sector, education sector, transportation sector, communication sector, e-commerce, automotive sector, defense industry, media, etc. In this context, this study aims to examine the effects of Artificial Intelligence systems on the transportation sector in line with their development in the historical process. Our aim is to show the contributions of Artificial Intelligence to the transportation sector and to bring suggestions for further contributions in this regard.

Anahtar Kelimeler

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Neural Networks
• Robot Technology
• Transportation
• Economy

Yapay Zekâ Kullanımının Ulaştırma Sektörü Üzerine Etkileri

Öz

Tarihsel süreçte dünyanın ekonomik gelişimine baktığımızda sanayi devrimi ya da endüstri devrimi olarak isimlendirilen çeşitli kırılma noktalarının olduğu görülmektedir. Bu dönem 1760 yılından günümüze kadar olan süreci kapsamaktadır. Günümüzde Endüstri 4.0 adı verilen 4. Sanayi Devrimi temelde bilişim teknolojileriyle endüstriyi bütünleştirmeye çalışan, insanlardan tamamen bağımsız bir şekilde koordinasyon ve optimizasyon koşullarını sağlayarak üretim sürecini gerçekleştiren bir yapıyı ifade etmektedir. Çağımızda teknoloji her geçen gün insan hayatına daha fazla dâhil olmaktadır ve üretim sürecinde robotların kullanımı ve yapay zekâyı da kapsayan olağan üstü bir boyuta sahiptir. İnsanların beklentilerini karşılamaya ve hayatı kolaylaştırmaya çalışan ülkeler ve şirketler bu alana sürekli daha fazla kaynak aktarmaktadır. Tarihsel süreçte, başlangıcı felsefe, kurgu ve hayal gücüne dayansa da günümüzde Yapay Zekâ (AI), derin öğrenme, makine öğrenimi ve sinir ağları, çeşitli gerçek dünya sorunlarını çözmek için kullanılmayan son derece karmaşık ve güçlü makine öğrenimi tabanlı teknikleri temsil etmektedir. Dolayısıyla gerçek dünya sorunlarına çözüm sağlamaya odaklanan bir bilim haline gelmiştir. Yapay Zekâ sistemlerini artık sağlık sektörü, eğitim sektörü, ulaşım sektörü, iletişim sektörü, e-ticaret, otomotiv sektörü, savunma sanayi, medya vb. gibi pek çok farklı sektörde görmek mümkündür. Bu bağlamda, bu çalışmada Yapay Zekâ sistemlerinin tarihsel süreçteki gelişimi doğrultusunda ulaşım sektörüne etkileri incelenmeye çalışılmıştır. Amacımız Yapay Zekânın ulaştırma sektörüne sağladığı katkıları göstermek ve bu konuda daha fazla katkı sağlayabilmesi için öneriler getirmektir.

Anahtar Kelimeler

• Yapay Zekâ
• Makine Öğrenimi
• Derin Öğrenme
• Sinir Ağları
• Robot Teknolojisi
• Ulaşım
• Ekonomi

Kaynakça

1. Agrawal, A., Gans, J. & Goldfarb, A. (2017). What to Expect from Artificial Intelligence. MITSloan Management Review, Volume 58, Issue 3, 23-27.
2. Anyoha, R. (2017). The History of Artificial Intelligence. Science in the News (SITN), Harvard University, https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
3. Bharadiya, J. P. (2023). Artificial Intelligence in Transportation Systems A Critical Review. American Journal of Computing and Engineering, Vol.6, Issue 1, 35-45, SSN 2790-5586, https://www.ajpojornals.org
4. Bhbosale, S., Pujari, V., & Multani, Z. (2020). Advantages and Disadvantages of Artificial Intellegence. Aayushi International Interdisciplinary Research Journal, 77, 227-230.
5. Boucher, P. (2020). Artificial Intelligence: How does it Work, Why does it What can We do About it? European Parliamentary Research Service, Scientific Foresight Matter, and Unit (STOA) PE 641.547, ISBN: 978-92-846-6770-3 DOI: 10.2861/44572
6. Buchanan, B. G. (2005). A (Very) Brief History of Artificial Intelligence. AI MAGAZINE, 25th Anniversary Issue, 53-60, https://s2.smu.edu/tfomby/eco5385_eco6380/lecture/Brief%20 History%20of%20AI.pdf
7. Chowdhury, M., & Sadek, A. W. (2012). Advantages and Limitations of Artificial Intelligence. Artificial Intelligence Applications to Critical Transportation Issues, 6-8, Transportation Research Circular E-C168, ISSN: 0097-8515 European Commission (2020). AI Watch Historical Evolution of Artificial Intelligence. Joint Research Centre, ISBN 978-92-76-18940-4, ISSN 1831-9424, DOI:10.2760/801580, https://ec.europa.eu/jrc
8. Fetzer, J. H., & Fetzer, J. H. (1990). What is Artificial Intelligence? Springer Netherlands.

9. Hamet, P. & Tremblay, J. (2017). Artificial Intelligence in Medicine. Metabolism, Volume 69, 36-40, https://doi.org/10.1016/j.metabol.2017.01.011 IBM (2023). AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference? https://www.ibm.com/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks/
10. IBM (2024). What is Machine Learning? https://www.ibm.com/topics/machine-learning
11. Institute for Global Affairs (2024). Digital Revolution: Technology, Power, & You. https://instituteforglobalaffairs.org/
12. Khanzode, K. C. A., & Sarode, R. D. (2020). Advantages and Disadvantages of Artificial Intelligence and Machine Learning: A Literature Review. International Journal of Library & Information Science (IJLIS), Volume 9, Issue 1, ISSN Print: 2277-3533 and ISSN Online: 2277-3584 Doi: https://doi.org/10.17605/OSF.IO/GV5T4 https://iaeme.com/
13. Kikuchi, S. (2007). Fuzzy Sets Theory Approach to Transportation Problems. Artificial Intelligence in Transportation Information for Application, Transportation Research Circular E-C113, 33-48, ISSN 0097-8515, https://onlinepubs.trb.org/onlinepubs/circulars/ec113.pdf
14. Laskowski, N. & Tucci, L. (2023). Artificial Intelligence (AI). Teach Target, https://www.techtarget.com/
15. Market Research Future (2024). Global AI in Transportation Market Overview. https://www.marketresearchfuture.com
16. Marr, B. (2023). 15 Amazing Real-World Applications of AI Everyone should Know about. Forbes, https://www.forbes.com/sites/bernardmarr/2023/05/10/15-amazing-real-world
17. Matthew L. J. (2023). AI in History, The American Historical Review, Volume 128, Issue 3, 1360–1367, https://doi.org/10.1093/ahr/rhad361
18. McGuire, B. (2006). The History of Artificial Intelligence. History of Computing CSEP 590A, https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf
19. Mohammed, Z. (2019). Artificial Intelligence Definition, Ethicsand Standards. The British University in Egypt, 150407, https://www.researchgate.net/publication/332548325
20. Newell, A. (1983). Intellectual Issues in the History of Artificial Intelligence. The Study of Information: Interdisciplinary Messages, Ed. Fritz Machlup & Una Mansfield, 187-227, https://cse.sc.edu/~mgv/csce580sp15/Newell_Issues1983.pdf
21. Parveen, S. & Chadha, R. S. (2022). Artificial Intelligence in Transportation Industry. International Journal of Innovative Science and Research Technology, IJISRT22AUG964, Volume 7, Issue 8, 1274-1278, https://ijisrt.com/
22. PK, F.A. (2021). Artificial Intelligence. Learning Outcomes of Classroom Research. December 2021, Ed. Dr. J. Karthikeyan, Dr. Ting Su Hie, Dr. Ng Yu Jin. 65-73. ISBN: 978-93-92995-15-6. https://scholar.google.com/ Precedence Research (2023). https://www.precedenceresearch.com
23. Sadek, A. W. (2007). Artificial Intelligence Applications in Transportation. Artificial Intelligence in Transportation Information for Application, Transportation Research Circular E-C113, 1-6, ISSN 0097-8515, https://onlinepubs.trb.org/onlinepubs/circulars/ec113.pdf
24. Smith, C. (2006). The History of Artificial Intelligence. History of Computing CSEP 590A, https://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf
25. Sternberg, R. J. (2005). The Theory of Successful Intelligence. Revista Interamericana de Psicología/Interamerican Journal of Psychology, Vol. 39, Num. 2, 189-202, https://www.researchgate.net/publication/26610484
26. Sternberg, R. J. (2012). Intelligence. Dialogues in Clinical Neuroscience- Vol 14, No 1, 19-27, DOI: 10.31887/DCNS.2012.14.1/rsternberg
27. Susar, D. & Aquaro, V. (2019). Artificial Intelligence: Opportunities and Challenges for the Public Sector. In Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance, 418-426, https://doi.org/10.1145/3326365.3326420
28. Zuylen, H. (2012). Difference Between Artificial Intelligence and Traditional Methods. Artificial Intelligence Applications to Critical Transportation Issues, 3-5, Transportation Research Circular E-C168, ISSN: 0097-8515