## 2 Literature Review
### Built Environment and Travel Behavior  
Understanding ride-hailing demand patterns is critically important as it not only provides a scientific foundation for developing diverse operational strategies but also supports more effective urban and transportation planning [60]. In recent years, researchers have extensively investigated how the built environment influences ride-hailing demand, recognizing it as a primary driver of mobility needs [15; 25; 28; 29; 31; 34; 36; 52; 53; 55; 60]. Defined as a human-made space shaped by policies and human activities, the built environment consists of multiple interrelated spatial variables [61]. These variables are commonly grouped into three dimensions: density, diversity, and design. Density refers to the spatial concentration of economic activities in a given area, often measured by key indicators such as population density, employment density, or building density [62]. It reflects the scale of human and economic activities in a region and serves as a foundational metric for evaluating transportation demand. The diversity dimension considers the mix of land uses within a region, ranging from residential to commercial or industrial, which can shape both the types and patterns of travel [63]. The design dimension encompasses the features of the transportation network, including road connectivity, pedestrian facilities, and bicycle lane accessibility [64]. Well-designed networks can improve traffic efficiency and lowertravel costs, indirectly affecting ride-hailing demand. Building on these fundamentals, researchers have further expanded the analytical framework for the built environment by incorporating transportation distance and demand management characteristics into a \"5D\" model [65; 66]. This extension adds two key dimensions: transportation distance, which gauges how readily users can reach their destinations, and demand management, which includes transportation policies, parking facilities, and pricing incentives, factors that directly influence the frequency and rate of ride-hailing use. Over the past decade, a substantial body of research has confirmed that the built environment has a broad and significant influence on travel behavior [67; 68; 69; 70]. A number of these studies emphasize statistical associations without adequately addressing the potential for self-selection bias, where individuals may choose neighborhoods that align with their travel preferences, thereby complicating causal interpretation. Scholars adopt various perspectives and methods in their analyses. Some focus on geographic patterns, examining how spatial characteristics in different areas affect the levels of ride-hailing demand [60; 71; 72]. For example, city centers often exhibit higher demand due to dense populations and frequent commercial activities, while peripheral areas with less-developed transportation infrastructure may see lower demand. Other studies employ the concept of \"islands\", highlighting areas where ride-hailing demand is heavily concentrated or minimal. By identifying these \"demand islands\", researchers can reveal spatial distribution patterns and intrinsic characteristics of specific regions [73; 74]. This approach helps to isolate the relationship between the built environment and ride-hailing demand, particularly in high-demand \"hotspots\" and low-demand \"coldspots\". Meanwhile, temporal variation is also crucial. Differences in demand during peak and off-peak periods, as well as weekday versus weekend travel behaviors, illustrate how time heterogeneity significantly affects demand patterns [15; 53; 71; 72]. Including the temporal dimension not only highlights how built-environment factors may shift in importance over time but also enables a more comprehensive understanding of demand dynamics. Furthermore, incorporating socioeconomic variables broadens the examination of how the built environment relates to ride-hailing demand.