## 2 Literature Review  
The transportation infrastructure exhibits network characteristics, and the complex network is a well-established approach for transportation infrastructure planning in current studies [13]. The continuous network design problem method (CNDP) is first applied to the planning and construction of transportation infrastructure. This approach considers transportation infrastructure as an integral system composed of node elements and edge elements. The service capacity and rationality of transportation infrastructure networks can be evaluated by calculating indicators such as the degree, betweenness centrality, edge betweenness, and average path length [14]. This method facilitates the quantification of various performance metrics for transportation infrastructure in megacities, providing a basis for the coordinated planning of transportation infrastructure. The CNDP is widely employed in research on the planning and construction of transportation infrastructure. However, some limitations have gradually emerged during its application. The CNDP assumes that if the evaluated network is fixed and invariant, the addition or removal of existing nodes and edge elements within the network cannot be operated [15]. Transportation infrastructure is constantly changing, and the suspension or disruption of existing routes, as well as the construction and optimization of new routes, are common phenomena of network structure changes. A fixed network struggles to adjust the network structure in response to different environmental changes, and the optimization of transportation networks cannot be realized [16]. The discrete network analysis problem method (DNDP), optimized from the CNDP, is gradually being applied, which inherits the advantage of the CNDP, such as ease of calculation and quantification [17]. A new fixed network derives from the variation in the transportation network structure through the enumeration algorithm, thus the issue that the traditional fixed network analysis could only evaluate issues that fail to be optimized has been resolved. The DNDP is widely utilized in the planning and optimization of transportation infrastructure, playing a significant role in the planning of a single transportation network [18]. However, when the research object is a comprehensive transportation network, including municipal roads, subways, highways, expressways, railways, and high-speed railways, the discrete network optimization cannot consider behavior differences and transfer issues [19]. The selection of transportation infrastructure and possible transfer are ignored by the DNDP. However, the congestion, difficulties in ticketing, and excessive loss of highways in the real transportation system of megacities are primarily caused by the selection of transportation modes. The transfer behavior implies the integration of multiple transportation infrastructures, effectively enhancing the transportation efficiency in megacities [20]. The high costs and low efficiency of transportation infrastructure can be partly attributed to the limitations of research methods. Recent advancements in AI/ML have significantly enhanced transportation planning, particularly in traffic flow prediction [21], public transit demand modeling [22], and travel behavior analysis [23]. Emerging methods like generative AI-based spatiotemporal networks [21] and optimized CNNs [23] outperform traditional models in both accuracy and efficiency. Systematic reviews further underscore the integration of AI, IoT, and blockchain in smart transportation systems [24; 25], while reinforcement learning has shown promise in improving public transport regularity [26]. Furthermore, the transformative potential of artificial intelligence and machine learning (AI/ML) has been clarified in urban transportation planning, providing a barrier-free, green, and livable transportation future for all [27]. These innovations offer strong technical support for large-scale, data-driven planning in megacities.