Traffic congestion is a contemporary urban issue plaguing transportation planners, land developers, policy-makers, and citizens. While many studies have investigated the impact of built environments on traffic behavior in large metropolises on a regional scale, little attention has been paid to smaller urban areas, in China’s context, especially on a neighborhood level. This study investigates the spatial–temporal pattern of traffic congestion in a small-scale city, Xining, in China. By applying multivariate least-square regression analysis to social-sensing hyperlocal travel data, the results indicate that Xining is experiencing morning and evening traffic peaks on the weekdays and pre-weekends and only the evening peak during the weekends or holidays. The pre-weekend congestion is significantly worse than on a normal weekday, implying that stronger measures to consolidate traffic management should be implemented during this time. Educational land use and residential areas were found to contribute significantly to traffic congestion in Xining, and their combined effects tend to exacerbate the situation. The study furthers the understanding of traffic congestion in small urban areas, providing urban planners and policy-makers with new insights to formulate evidence-based strategies for mitigating traffic congestion.