Development of a Trip Generation Model for Rapidly Transforming African Cities: The Case Study of Upper Hill, Nairobi

Abstract

Rapid urban transformation in commercial centers has intensified travel demand, necessitating context-specific approaches to trip generation analysis. This study develops a localized, empirically validated framework to examine how land use characteristics and socioeconomic factors influence trip generation patterns in Upper Hill, Nairobi. A quantitative cross-sectional design was adopted, with data collected from 20 establishments and 115 travelers using structured surveys, field observations, and spatial mapping techniques. Descriptive statistics, Pearson correlation, and Multiple Linear Regression (MLR) were used to analyze relationships among key variables. The results indicate that Gross Floor Area (GFA) is the most significant predictor of trip generation, exhibiting a very strong positive correlation (r = 0.938, p < 0.001) and a dominant regression effect (β = 0.909, p < 0.001). The developed model demonstrates high explanatory power (R2 = 0.927; Adjusted R2= 0.914) and is statistically significant (F = 68.133, p < 0.001), confirming its robustness in predicting travel demand. Socioeconomic factors, particularly income, also exhibit a strong relationship with travel behavior (r = 0.710, p < 0.001), influencing vehicle ownership and trip frequency. Theoretically, this study contributes to trip generation methodology by providing one of the first establishment-level empirical models for a rapidly transforming commercial district in East Africa, integrating land use and socioeconomic variables within a unified regression framework. Practically, the findings highlight the need for integrated land use–transport planning policies that account for both structural intensity and socioeconomic diversity in rapidly transforming urban commercial centers in the African context.