## 2 Literature Review  
In the contemporary context, the transport industry emerges as a significant contributor to greenhouse gas emissions, responsible for roughly one-third of global emissions. As the freight transportation sector experiences continuous growth, there is an urgent requirement to create transportation networks that are both sustainable and effective. Multimodal transportation networks offer a promising solution by seamlessly integrating various modes, including rail, road, and water, aiming to reduce costs and improve supply chain performance. Nevertheless, the intricate task of designing such networks is beset with challenges, necessitating a comprehensive approach. Past research has employed various methodologies, such as linear programming, integer programming, and metaheuristic algorithms, to achieve a balance between multiple objectives in optimizing transportation [15; 16; 17]. Multicasting, a crucial element in computer networks, involves the simultaneous transmission of identical data from a source to a group of destinations. Multicast routing, vital for multimedia information transmission, involves selecting a routing tree from a source to encompass all destinations, addressing the NP-complete Steiner tree problem. The challenge lies in constructing an optimal multicast tree with minimal cost, considering quality of service (QoS) requirements like delay, delay jitter, bandwidth, and packet loss inherent in multimedia communications. To address these constraints, multicast routing introduces the concept of a constrained Steiner tree, an NP-complete problem tackled by various heuristic algorithms [18; 19; 20]. [PERSON] et al. [8] evaluate the efficiency and sustainability of urban rail transit (URT) through a two-stage methodology involving exploratory data analytics (EDA) and data envelopment analysis (DEA). EDA characterizes URT efficiency and sustainability using existing and suggested indicators, while DEA assesses URT efficiency through two original models. The proposed methodology undergoes experimental validation using open data from the Transport for London (TfL) URT network. [PERSON] et al. [9] address deficiencies in previous road transportation sustainability research by considering epistemic uncertainty related to innovation and research and development (R&D) expenditure impact on pollutant emissions performance. They introduce a TEA-IS model for assessing road transportation sustainability performance in 29 Chinese provinces over a 14-year period, employing a hybrid DEA-TOPSIS approach and machine learning techniques. Results reveal high synergy in Chinese provinces, suggesting the need for favorable policies to enhance innovation and attract foreign direct investment. [PERSON] et al. [10] highlight the transportation network design problem (TNDP), involving strategic criteria selection to enhance an existing transportation network amid increasing traffic demand. The authors employ a hidden Markov model and equilibrium optimizer (EO) for resolution, demonstrating the novel method's superior effectiveness through comparative analysis on a test network. [PERSON] et al. [21] present a robust mixed-integer linear program (MILP) supporting railway operators in making informed decisionsabout the expansion of their intermodal networks. The model addresses uncertainties related to demand and supply, incorporating budgetary constraints and capacity limitations. A hybrid GA, utilizing column generation and multimodal shortest path label-setting algorithms, tackles the complex MILP. [PERSON] [11] proposes an innovative approach to construct multicast trees incorporating multiple QoS parameters, utilizing data envelopment analysis techniques. The effectiveness of the proposed method is demonstrated through numerical examples. Unmanned aerial vehicles (UAVs) equipped with high-definition cameras play a crucial role in efficiently collecting comprehensive road data from diverse angles. However, their limited energy capacity poses challenges for sustained operation in such tasks.