## 2 Literature Review  
Waiting time is a crucial factor that influences users' decisions to use public transport. [PERSON] et al. [10] observed that during the COVID-19 pandemic, the increase in waiting times caused by the reduction in service frequency led many users to abandon public transportation. An average increase of one minute in waiting times contributed to a 0.34% decrease in the number of users worldwide.  
[PERSON] et al. [11] emphasized that waiting times are a significant component of accessibility and user experience. They introduced a decision support framework that incorporates pre-trip waiting times into the analysis of accessibility and the planning of public transport networks. By integrating this element, they demonstrated its relevance not only for service optimization but also for improving user perception.  
[PERSON] et al. [12] showed that initial and transfer waiting times are essential components of the Generalized Travel Cost (GTC), a key metric used to measure accessibility in public transport systems. These waiting times, together with in-vehicle travel times and transfer penalties, largely explain spatial disparities in accessibility across different areas, thereby impacting the perceived quality of the system.  
In Amman, Jordan, [PERSON] et al. [13] observed peak-hour waiting times ranging from 12 to 23 min, underscoring the need to treat waiting as a primary planning variable. Likewise, [PERSON] and [PERSON] [14] show that shortening transfer-stage waits is equally decisive for overall system efficiency. Although one study targets pre-trip waiting and the other focuses on inter-modal transfers, both converge on the broader conclusion that the total time passengers spend waiting is a key performance indicator that transit agencies must minimize.  
[PERSON] et al. [15] contributed neuroscientific evidence underscoring the importance of waiting times. They identified out-of-vehicle waiting time as one of the most uncomfortable travel experiences, significantly influencing users' willingness to use public transport. This finding demonstrates the necessity of addressing waiting times not only from an operational standpoint but also as a critical factor in improving overall system perception.  
[PERSON] and [PERSON] [16] observed that passengers perceive station waiting times more negatively compared to in-vehicle travel times, as waiting exerts a stronger impact on their overall travel experience. This highlights the importance of reducing waiting times to improve accessibility and enhance user satisfaction.  
In recent years, several data-driven approaches have been proposed to quantify and optimize public transport waiting and transfer performance. For example, [PERSON] et al. [17] employed smart card data in Nanjing to isolate the highest-priority metro-bus transfer pairs and propose targeted measures for reducing transfer delays. Building on this, [PERSON] and [PERSON] [18] developed an iterative Data Envelopment Analysis (iDEA) framework--augmented with explainable AI--to evaluate bus-metro transfer efficiency for elderly versus general users in Seoul, finding that an 11% reduction in transfer time and its variance would equalize user comfort across groups. Moreover, [PERSON] [19] introduced an XAI-enhanced DEA model to assess origin-destination pair efficiencies in multimodal networks, demonstrating that bus travel time is the predominant factor limiting overall efficiency. While these methods leverage high-resolution operational datasets and advanced optimization techniques, our study complements them by using spatial clustering (DBSCAN) and autocorrelation (Moran's I) on the 2014 SECTRA survey to reveal latent spatial patterns of waiting times in the Greater Valparaiso Metropolitan Area.