[
Abduljabbar R, Dia H, Liyanage S, Bagloee SA. Applications of Artificial Intelligence in Transport: An Overview. Sustainability. 2019; 11(1):189. https://doi.org/10.3390/su11010189
]Search in Google Scholar
[
Jamal Robinson, How Uber Organizes Around Machine Learning, 2019. Available online: https://jamal-robinson.medium.com/how-uber-organizes-around-artificial-intelligence-machine-learning-665cdeb946bc (accessed on 24 February 2024).
]Search in Google Scholar
[
Jiang, H.; Gao, L. Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method. Sustainability 2020, 12, 658. https://doi.org/10.3390/su12020658
]Search in Google Scholar
[
J. Lee, J. Chung and K. Sohn, "Reinforcement Learning for Joint Control of Traffic Signals in a Transportation Network," in IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 1375-1387, Feb. 2020, doi: 10.1109/TVT.2019.2962514
]Search in Google Scholar
[
Kamargianni, M.; Matyas, M. The Business Ecosystem of Mobility as a Service, 96th Transportation Research Board Annual Meeting, Washington DC, 8–12 January 2017. Available online: https://discovery.ucl.ac.uk/id/eprint/10037890/1/a2135d_445259f704474f0f8116ccb625bdf7f8.pdf (accessed on 24 January 2024).
]Search in Google Scholar
[
Klügl, F.; Bazzan, ALC; Ossowski, S .Agents in traffic and transportation. Res. Partea C Emerg. Tehnol. 2010,18, 69–70. https://doi.org/10.1016/j.trc.2009.08.002
]Search in Google Scholar
[
Leclerc, F., Potvin, J.-Y., & Potvin, J. Y. (1997). Genetic Algorithms for Vehicle Dispatching. International Transactions in Operational Research, 4(5-6), 391–400. doi:10.1111/j.1475-3995.1997.tb00094.x
]Search in Google Scholar
[
Ledoux, C. An urban traffic flow model integrating neural networks.Transp. Res. Partea C Emerg. Tehnol.1997, 5, 287–300. https://doi.org/10.1016/S0968-090X(97)00015-6
]Search in Google Scholar
[
Lotfi, M. M., & Tavakkoli-Moghaddam, R. (2013). A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems. Applied Soft Computing, 13(5), 2711–2726. https://doi.org/10.1016/j.asoc.2012.11.016
]Search in Google Scholar
[
L. Kuyer et al., “Multiagent reinforcement learning for urban traffic control using coordination graphs,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Berlin, Heidelberg, Springer, 2008
]Search in Google Scholar
[
Moovit 2024. About Moovit. Available on: https://moovit.com/about-us/ (accessed on 24 January, 2024).
]Search in Google Scholar
[
Nikitas, A.; Michalakopoulou, K.; Njoya, E.T.; Karampatzakis, D. Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era. Sustainability 2020, 12, 2789. https://doi.org/10.3390/su12072789
]Search in Google Scholar
[
Precedence Research. (Accessed on 23 January, 2024). Artificial Intelligence in Transportation Market (By Offering: Hardware, Software; By Machine Learning Technology: Deep Learning, Computer Vision, Context Awareness, Natural Language Processing; By Process: Signal Recognition, Object Recognition, Data Mining; By Application) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032. https://www.precedenceresearch.com/artificial-intelligence-in-transportation-market
]Search in Google Scholar
[
Precedence Research. (Accessed on 23 January, 2024). Autonomous Vehicle Market (By Application: Defense, Transportation; By Level of Automation: Level 1, Level 2, Level 3, Level 4, Level 5; By Propulsion: Semi-autonomous, Fully Autonomous; By Vehicle:
]Search in Google Scholar
[
Passenger Car, Commercial Vehicle) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023 - 2032. https://www.precedenceresearch.com/autonomous-vehicle-market
]Search in Google Scholar
[
P&S Intelligence. (2019). Artificial Intelligence (AI) in Transportation Market by Offering (Software, Hardware), by Application (HMI, ADAS), by Technology (Deep Learning, Computer Vision, NLP), by Process (Signal Recognition, Data Mining), by Geography (U.S., Canada, Germany, U.K., France, China, Japan, India, South Korea, Russia, Brazil) – Global Market Size, Share, Development, Growth, and Demand Forecast, 2013–2023. https://www.psmarketresearch.com/market-analysis/ai-in-transportation-market
]Search in Google Scholar
[
Santos, G.; Nikolaev, N. Mobility as a Service and Public Transport: A Rapid Literature Review and the Case of Moovit. Sustainability 2021, 13, 3666. https://doi.org/10.3390/su13073666
]Search in Google Scholar
[
Tamboli, A. (2019). Evaluating Risks of the AI Solution. Keeping Your AI Under Control. https://doi.org/10.1007/978-1-4842-5467-7_4.
]Search in Google Scholar
[
Umair Akbar, Revolutionizing Driving Forever: How Tesla’s Neural Networks Power Full Self-Driving Capabilities, 2023. Available online: https://akbu.medium.com/revolutionizing-driving-forever-how-teslas-neural-networks-power-full-self-driving-capabilities-11c09a4329ec (accessed on 24 February, 2024).
]Search in Google Scholar
[
Xu, T.;Wei, H.;Wang, Z.D. Study on continuous network design problem using simulated annealing and genetic algorithm. Expert Syst. Appl. 2009, 36 Pt 2, 2735–2741. https://doi.org/10.1016/j.eswa.2008.01.071
]Search in Google Scholar
[
Wang, C.; Li, X.; Zhou, X.;Wang, A.; Nedjah, N. Soft computing in big data intelligent transportation systems. Appl. Soft Comput. J. 2016, 38, 1099–1108. https://doi.org/10.1016/j.asoc.2015.06.006
]Search in Google Scholar
