## 2 Related Works
[2] discusses some methodsof how intelligent transport systems (ITS) should be able to scale and adapt to different traffic conditions and unforeseen scenarios that traditional rigid systems cannot achieve due to limited adaptability and scalability. The management information systems lack support in the high-level decision-making process. Today's multimodal behavior involves complex decision-making processes that require the integration of different data sources and the use of complex algorithms. Traditional AI, often created using a single asset for specific logistics operations, does not provide the advanced capabilities to support the solutions needed for multimodal transportation. Ref. [5] provides some discussions about the importance of decision support systems that have several criteria for sustainable mobility needed to assess a wide range of factors, such as price, time, and environmental impact. The decision-making systems are usually designed without knowing the context of transportation. The general understanding required by modern multimodal transport is greater than traditional AI capabilities. Modern systems need to be context-oriented in order to adapt to different scenarios, such as weather changes, road conditions, or unexpected delays that directly affect route and loading planning. The need for contextual frameworks is emphasized in [9; 10], which can dynamically alter activities based on real-time environmental data that are not supported by traditional systems. Cyber security and privacy issues must be included in the infrastructure of ISs for the management of transportation. Because multimodal behavior increasingly relies on interconnected systems and real-time data exchange, cybersecurity and data protection risks have increased significantly. Traditional AI often lacks the strong security features needed to protect against cyber threats in such a complex and interconnected environment. The vulnerabilities in traditional cyberattack systems discussed by [12] and [13] could lead to data breaches and business disruptions, highlighting the need for more secure and advanced artificial intelligence that can solve these problems. Finally, traditional IT systems are not enough to meet the needs of modern multimodal transport although they have proven that logistics operations are simpler. The basic requirements are real-time data integration, better interoperability, scalability, better decision-making, contextual understanding, and robust cybersecurity tools that modern systems must follow. The development of multimodal transport requires the introduction of more complex, flexible, and secure IT systems that will be able to support the complex and dynamic nature of modern logistics operations. If contextual data are included in a multimodal e-services system, there are certain risks based on different research studies in the areas of concern. According to the literature, these risks can be divided into technical risks, operational risks, information security threats, and interoperability risks. By construction of the e-service system, we have faced technical risks. Communication failures: The integration of vehicle-to-vehicle communication systems, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity, is essential for real-time data exchange in multimodal transport. However, reliability and performance issues associated with communication protocols, such as those specified in [3] IEEE 802.11p and LTE-V2V standards, can cause errors or delays in data transmission. Such errors can hinder the coordination of different modes of transport, leading to a decrease in efficiency. The systems have incompatibility with existing systems. The introduction of new technologies such as the IoT and WSNs, which are discussed in [4], carries the risk of incompatibility with existing traffic management systems.