The growing environmental impact of textile consumption has intensified the need for efficient post-consumer waste collection systems capable of supporting circular economy transitions. Designing effective logistics systems for textile waste is therefore crucial, and their proper dimensioning requires accurate forecasts of collected volumes. However, textile waste flows are highly heterogeneous and strongly influenced by behavioural factors, making reliable forecasting particularly challenging. This study investigates whether urban textile waste collection can be effectively predicted by combining stable bin-level heterogeneity with time-varying socio-spatial and behavioural indicators. Using panel data generated by a hybrid simulation model for the municipality of Parma, we implemented a fixed-effects econometric framework and compared its performance with traditional benchmarks, including seasonal means and Holt–Winters exponential smoothing. The results demonstrate that incorporating structural heterogeneity across collection points, together with behaviour-related dynamics, enhances prediction accuracy and significantly outperforms traditional univariate time-series approaches, both at the aggregate level (R² ≈ 0.81) and at the bin level (MAE ≈ 25). These findings also support the robustness and generalizability of the proposed panel-data econometric framework, which shows strong potential for application in other urban settings characterized by similar structural and behavioural features.

Urban textile-waste collection is becoming a strategic component of circular-economy policies, especially after the introduction of mandatory separate textile collection in the European Union. However, its design remains challenging because textile disposal depends not only on logistics, but also on irregular generation patterns, citizens’ willingness to participate, accessibility to bins, social influence, and perceived service reliability. In this context, simulation plays a fundamental role in supporting the sizing and redesign of collection systems, as demonstrated in a previously developed hybrid simulation framework, which reproduces the as-is scenario of the textile-waste collection system of Parma, Italy. In the present study, we extend our previous conference work by presenting two possible improvements where we increase bin numbers, rearrange their position, and consider different routing policies. The model reproduces real operational data and behavioural parameters derived from data on textile-disposal practice in Italy. Results show an improvement of service level, measured in terms of fill rate improvement from 89.7% to 94.1%, and increased waste interception from 58.1% to 64.5%. Nevertheless, these improvements are not enough to change citizens’ propensity to recycle, and therefore policy incentives remain necessary to close the gap between generated and recovered textile waste.