## 1 Introduction  
In recent years, the transportation industry has experienced significant transformations driven by the widespread adoption and advancement of mobile and wireless communication technologies [1]. These innovations have led to the electrification of vehicles, the integration of intelligent systems, enhanced networking capabilities, and the rise of shared mobility options. Among these, ride-hailing services facilitated by smartphone applications developed by Transportation Network Companies (TNCs) have emerged as a dominant and flexible travel option for urban residents [2]. A notable example is Didi Chuxing, which, since its launch in December 2015, has achieved remarkable popularity in China, serving over 1.3 billion users across 60 cities and handling an average of more than 2.4 million daily orders [3]. This success underscores the effectiveness of shared mobility solutions in modern urban transportation. Compared to conventional transportation modes such as public transit or taxis, ride-hailing offers several advantages, including greater convenience, personalized service options, real-time responsiveness, and cost transparency [4, 5, 6]. With intelligent matching algorithms and GPS-based tracking, ride-hailing platforms reduce passenger wait times and provide flexible routing options [7]. Passengers can further tailor their experiences by selecting vehicle types and ride conditions, such as requesting child seats or non-smoking environments [8; 9]. The shared economy model inherent to ride-hailing improves vehicle utilization and incorporates green transportation options, such as electric vehicles, which help to alleviate traffic congestion and reduce environmental pollution [10; 11; 12]. Despite these benefits, the rapid surge in demand, especially during peak periods, has led to service supply shortages, increased wait times, and localized inefficiencies [13]. These challenges underscore the importance of identifying the key spatial and temporal drivers of ride-hailing demand. Empirical evidence suggests that ride-hailing demand is shaped by complex temporal and spatial dynamics [14; 15]. Demand tends to rise during commuting hours and in high-density or commercial areas, while decreasing during late nights, holidays, and in peripheral zones. Seasonal and event-related factors, such as weather fluctuations, public holidays, and festivals, further contribute to demand variability. Research indicates that ride-hailing usage patterns result from a combination of internal and external factors [16; 17; 18], including user preferences, sensitivity to convenience and cost, and the demand for personalized services [19; 20]. While early studies focused on broad socioeconomic indicators to assess the development of ride-hailing services across different regions [21], recent research has increasingly adopted a finer spatial lens. Built-environment variables at the neighborhood- or census-tract level have gained prominence in understanding demand heterogeneity [22; 23]. Variables such as population density, land-use diversity, road network structure, access to public transportation, the presence of commercial and service facilities, and traffic conditions have all been shown to affect ride-hailing patterns [24; 25; 26; 27]. Areas characterized by limited public transportation access often exhibit higher dependence on ride-hailing services [28; 29], while the spatial distribution and accessibility of transit stations play a critical role in determining the frequency and spatial distribution of ride requests [15; 28; 30]. Thus, spatial modeling of these built-environment features provides valuable insights for improving demand forecasting and optimizing mobility planning [31]. These insights support more informed planning decisions and enable efficient scheduling, planning, and resource allocation for ride-hailing services by providing a robust theoretical foundation.