Accesso libero

Data Technology Triad: A Model towards Integrated Autonomous Transportation (IAT) Networks

 e   
24 lug 2025
INFORMAZIONI SU QUESTO ARTICOLO

Cita
Scarica la copertina

Abdulrashid, I., Zanjirani Farahani, R., Mammadov, S., Khalafalla, M., & Chiang, W.-C. (2024). Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents. Transportation Research Part E: Logistics and Transportation Review, 186, 103563. https://doi.org/10.1016/j.tre.2024.103563Search in Google Scholar

Alsolbi, I., Shavaki, F. H., Agarwal, R., Bharathy, G. K., Prakash, S., & Prasad, M. (2023). Big data optimisation and management in supply chain management: A systematic literature review. Artificial Intelligence Review, 56(S1), 253–284. https://doi.org/10.1007/s10462-023-10505-4Search in Google Scholar

Andrei, N., Scarlat, C., & Ioanid, A. (2024). Transforming E-Commerce Logistics: Sustainable Practices through Autonomous Maritime and Last-Mile Transportation Solutions. Logistics, 8(3), 71. https://doi.org/10.3390/logistics8030071Search in Google Scholar

Atzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: Definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122–140. https://doi.org/10.1016/j.adhoc.2016.12.004Search in Google Scholar

Booth, L., Karl, C., Farrar, V., & Pettigrew, S. (2024). Assessing the Impacts of Autonomous Vehicles on Urban Sprawl. Sustainability, 16(13), 5551. https://doi.org/10.3390/su16135551Search in Google Scholar

Bothra, P., Karmakar, R., Bhattacharya, S., & De, S. (2023). How can applications of blockchain and artificial intelligence improve performance of Internet of Things? – A survey. Computer Networks, 224, 109634. https://doi.org/10.1016/j.comnet.2023.109634Search in Google Scholar

Gao, J., Wu, W., & Aktouf, O.-E.-K. (2022). Adequate Testing Unmanned Autonomous Vehicle Systems—Infrastructures, Approaches, Issues, Challenges, and Needs. 2022 16th Ieee International Conference on Service-Oriented System Engineering (Sose 2022), 154–164. https://doi.org/10.1109/SOSE55356.2022.00025Search in Google Scholar

Giusti, R., Iorfida, C., Li, Y., Manerba, D., Musso, S., Perboli, G., Tadei, R., & Yuan, S. (2019). Sustainable and De-Stressed International Supply-Chains Through the SYNCHRO-NET Approach. Sustainability, 11(4), 1083. https://doi.org/10.3390/su11041083Search in Google Scholar

Gumzej, R. (2023). Intelligent logistics systems in E-commerce and transportation. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 20(2), 2348–2363. https://doi.org/10.3934/mbe.2023110Search in Google Scholar

Khan, S. A., Mubarik, M. S., Kusi-Sarpong, S., Gupta, H., Zaman, S. I., & Mubarik, M. (2022). Blockchain technologies as enablers of supply chain mapping for sustainable supply chains. Business Strategy and the Environment, 31(8), 3742–3756. https://doi.org/10.1002/bse.3029Search in Google Scholar

Kuo, S.-Y., Huang, X.-R., & Chen, L.-B. (2022). Smart ports: Sustainable smart business port operation schemes based on the Artificial Intelligence of Things and blockchain technologies. IEEE Potentials, 41(6), 32–37. https://doi.org/10.1109/MPOT.2022.3198808Search in Google Scholar

Project Management Institute (Ed.). (2021). The standard for project management and a guide to the project management body of knowledge (PMBOK guide) (Seventh edition). Project Management Institute, Inc.Search in Google Scholar

Qin, P., Guo, J., Shen, B., & Hu, Q. (2020). Towards Self-automatable and Unambiguous Smart Contracts: Machine Natural Language. In K.-M. Chao, L. Jiang, O. K. Hussain, S.-P. Ma, & X. Fei (Eds.), Advances in E-Business Engineering for Ubiquitous Computing (Vol. 41, pp. 479–491). Springer International Publishing. https://doi.org/10.1007/978-3-030-34986-8_34Search in Google Scholar

Scarlat, C. (2017). Triadic Models: Triple S Holistic Approach for Inter-relational Analysis in Business Management, Entrepreneurship and Marketing. Research Journal of Social Sciences, 10(1), 1–5.Search in Google Scholar

Sun, Y., Sun, G., Huang, B., & Ge, J. (2023). Modeling a Carbon-Efficient Road-Rail Intermodal Routing Problem with Soft Time Windows in a Time-Dependent and Fuzzy Environment by Chance-Constrained Programming. SYSTEMS, 11(8). https://doi.org/10.3390/systems11080403Search in Google Scholar