## 2 Related Works
For example, studies such as [10] examined the contextual application in the urban rail environment, but the integration of these technologies into multimodal transport systems is still at an early stage. The potential of these technologies to improve decision-making, increase efficiency, and reduce environmental impact in multimodal transport chains remains untapped. The following key areas are important for multimodal transportation e-services provision:  
* _The creation of a common framework_. The paper proposes a comprehensive approach to integrating basic data on different modes of transport, such as weather, traffic, and freight conditions, into a multimodal transport system. This framework aims to improve real-time decision-making and the efficiency of multimodal freight transport. * _Better coordination and sustainability_. The proposed framework for e-services aims to optimize coordination between different transport modes by including multi-key decision-making support and providing contextual information in real time. This approach is expected to improve the sustainability of freight transport by reducing delays, reducing emissions, and using resources more efficiently. * _Application of new technologies_. This study will use new technologies such as the Internet of Things, WSN, and contextual systems to create a robust e-service system that can adapt to dynamic changes in the transport environment. The integration of this technology is aimed at facilitating a smooth modal shift from one mode of transport to another, thereby improving the overall efficiency of multimodal transport systems. Due to some critical limitations, traditional IT systems are becoming less adapted to the complex needs of modern multimodal transport. These systems, especially for linear and less dynamic logistics operations, are designed to meet the modern complex needs of multimodal transport, which include the coordination of different modes of transport, the integration of real-time data, and the seamless interoperability of different systems. According to the presented literature, the reasons for removal are as follows. We can mention that, in the provided studies, there is a lack of real-time data integration possibilities. Traditional IT systems are often poorly equipped to meet today's real-time data requirements in multimodal transport. IoT devices and wireless sensor networks (WSNs) are essential for monitoring and controlling the flow of goods between different modes of transportation. In our previous work [4], we underlined the modern transport systems and the need for the ability to process and manage real-time data in order to optimize routes, planning, and load management processes that traditional systems cannot effectively support with their static computing capacities. In the mentioned studies, there is a lack of attention to the description and realization of interaction between the ISs. Modern multimodal transport requires seamless interoperability of different systems, including systems using different modes of transport, such as rail, road, and sea. Traditional AI often faces interoperability challenges as it is based on outdated protocols and data formats that are not compatible with different multimodal systems. In study we draw the attention to the challenges they face in achieving interoperability between maritime and rail transport and to the need for improved systems to harmonize different communication standards and data formats for which traditional systems have not been developed. We can mention the limited scalability and flexibility of information management systems. Traditional AI is not scalable and flexible, which is needed to support the dynamic nature of multimodal transport. Modern transport systems must be efficiently adapted to different cargo sizes and changing logistics requirements. Ref.