Dynamic Simulation Model for Urban Street Sweeping: Integrating Performance and Citizen Perception

Urban street sweeping infrastructure plays a critical role in municipal solid waste management by mitigating particulate matter resuspension and preventing contaminant mobilization into water bodies, thereby supporting public health and environmental sustainability. The primary objective of this study is to develop a dynamic evaluation model for urban street sweeping services in four localities of Bogotá, Colombia. Operating system variables are integrated with citizens’ perceptions to capture their coupled socio-environmental behavior. The methodology comprised four phases: a global literature review, a citizen-perception survey, the development of a dynamic simulation model integrating perceptions, and a statistical analysis of all collected data. The results demonstrate that technical efficiency in street sweeping operations, measured through the street cleanliness index, is insufficient to ensure service sustainability without incorporating citizen perception metrics. The model reveals that geometric, spatial, and climatic factors reduce the street cleanliness index by up to 100%, highlighting infrastructure vulnerability to external conditions. Model validation exposes a critical gap between operational cleanliness and citizen perception, with decreases of up to 64.2% in comprehensive service evaluation. The inclusion of perception indicators (Cronbach’s α = 0.770) underscores the significance of variables such as service punctuality and personnel attitude in determining citizen satisfaction and overall service assessment. The dynamic model constitutes a robust decision-support tool for optimizing resource allocation, mitigating socio-environmental impacts, and strengthening institutional legitimacy in urban infrastructure maintenance. Nevertheless, limitations in representing external factors (informal commerce and illegally parked vehicles) and spatial heterogeneity in cleanliness indices suggest future research directions incorporating stochastic modeling approaches and longitudinal studies on citizen perception dynamics.