International Journal For Multidisciplinary Research

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Leveraging Generative AI for Knowledge Management in Transportation Systems Management and Operations

Author(s) Md Kazi Shahab Uddin, Syed Sobhan, Saba Jarin Nudhar
Country United States
Abstract There has been growing appreciation of the capability of Generative AI to enhance and support innovation in different sectors such as Knowledge Management (KM) in Transportation Systems Management and Operations (TSMO). This is because of the increasing complexities and datacentric approach to transportation, where effective KM is critical to enhance decision making, shortening response durations and improving the system Emilia (2023). Generative AI, because of its offering predictive insights and even automating report generation can considerably improve the traditional KM approaches, in which case it’s a great asset in TSMO which deals with extensive data management (Chen & Li, 2021). In this line, this article discusses these applications in generative AI in TSMO and seeks to give a framework on the improvement of KM in TSMO through generative AI.
The extent of knowledge management potential of generative AI in TSMO goes hand in hand with the competitive position that this technology creates based on real time analytics and intelligent decision making. In advanced predictive analytics, for instance, generative AI can be used to augment traffic incident and disruption predictions thereby lessening the travel interruptions by avoiding such scenarios (Rahman & Smith, 2023). In addition, by reducing the need for personnel to perform repetitive functions such as report writing and creating operational manuals, for instance, by generating AI, also frees up more time and resources which is very important in TSMO where needs are usually a lot (Bates, 2022). This explains the focus on the benefits of generative AI and how it has expanded capabilities in terms of operation being the heart of the integration which results in better transportation systems.
The potential advantages are great, however, there are also obstacles in adopting generative AI for TSMO knowledge management, especially in terms of data protection and ethical issues. In consideration of transportation systems that deal with sensitive data, there has to be a set of guidelines to ensure that people’s data are safeguarded and trust is kept with the society (Rahman & Smith, 2023). In addition, the ease of use of insights generated by AI is very complicated – it requires considerable training for the TSMO personnel on how to adopt AI in their day-to-day activities (Chen & Li, 2021). This article elaborates on the implications we have mentioned above, provides suggestions on how to put into practice generative AI-enabled KM strategies in the context of TSMO, and outlines further research that is needed to tackle issues that remain in this growing area.
Keywords Generative AI, Knowledge management, Transportation systems, Predictive analytics, Data security, Intelligent transportation systems (ITS), Workflow automation.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-30
Cite This Leveraging Generative AI for Knowledge Management in Transportation Systems Management and Operations - Md Kazi Shahab Uddin, Syed Sobhan, Saba Jarin Nudhar - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31547
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31547
Short DOI https://doi.org/g8sg65
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