challenges, including inefficiencies in transportation systems and environmental capacity limitations [8]. In this context, analyzing intercity travel characteristics has become a critical approach for optimizing transportation planning and enhancing residents' quality of life [9]. Intercity trip chains, functioning as dynamic networks linking nodes within agglomerations, not only capture spatiotemporal mobility behaviors but also inform data-driven strategies for resource allocation and multimodal optimization [10,11]. The COVID-19 pandemic has further reshaped mobility trends, underscoring the urgency of continuous monitoring and adaptive strategies to address evolving travel demands [12,13]. Due to the pandemic, in Spain, travel volume dropped dramatically while public transportation was extremely affected and private cars were relatively less affected [14]. The travel volume in most countries decreased [15,16]. The number of travelers who travelled for education, to visit friends, and for personal care decreased the most among all the purposes for travel in the Netherlands [17]. In China's Greater Bay Area, it was found by analyzing mobile phone data that, in comparison to advantaged groups, socially disadvantaged groups experienced a steeper decline in travel mobility during the pandemic's peak but a more significant recovery afterward [18]. Previous studies have achieved significant progress in analyzing intercity travel characteristics. Smart card data, valued for their accessibility and wide availability, have become a predominant big-data source in public transit research, enabling analyses of spatiotemporal commuting patterns and large-scale demand modeling [19]. When integrated with GPS trajectories, these data can reconstruct detailed trip sequences, though inferring trip purposes remains challenging [20]. To address this, Minimum Entropy Rate (MER) methods optimize sequence uncertainty and enhance origin-destination estimation [21]. Machine learning techniques (e.g., sequence alignment, decision trees) further identify recurrent mobility patterns in transit datasets [22,23], while discrete choice models quantify mode selection dynamics and comfort trade-offs in trip chains [24]. Concurrently, investigations into high-speed rail network development and its spatial interactions with urban agglomerations [25], along with analyses of multimodal travel decision-making processes [26], have significantly advanced theoretical frameworks for regional transportation planning. Despite these achievements, there are still some limitations. While traditional survey instruments effectively capture detailed trip characteristics, including purpose and mode choice [27], their implementation faces practical challenges, including substantial costs, limited spatial coverage, and low response rates, hindering their ability to represent population-wide mobility patterns at the urban agglomeration scale [28]. Furthermore, insufficient coverage of transient populations and non-commuting trips exacerbates sampling biases. From an analytical perspective, conventional clustering methods (e.g., K-means, K-modes) demonstrate limited efficacy when processing mixed data types, frequently producing suboptimal classifications due to their inability to effectively handle heterogeneous variables [29]. Current variable selection methods, often relying on simplified correlation measures, fail to adequately capture complex relationships between categorical and numerical features [30]. Additionally, the sensitivity of clustering algorithms to initialization parameters frequently results in locally optimal solutions [31]. Perhaps most significantly, prevailing research paradigms tend to examine transportation modes in isolation or focus on static spatial-temporal parameters, neglecting the dynamic interactions between multimodal systems, temporal fluctuations, and individual behavioral variations [26,32].