This paper proposed an original analytical approach to quantify waste generation and recycling behaviors in Regina, a Canadian capital city, using Google's Community Mobility Reports (GCMR) mobility data and ANN modeling. Residential waste collection rate (RWCR) in Regina appears to have changed abruptly during the pandemic. Compared to the pre-COVID-19 lockdown period, the correlation between the percent changes in RWCR and GCMR mobility became increasingly stronger and statistically significant during and after COVID-19 lockdown. It appears that the government imposed public health measures improved the consistency of waste generation and recycling behaviors. All 16 models predicting RWCR performed satisfactorily, with R2 near or over 0.70. The univariate Model A along with multivariate Model B and Model G performed better, with MAE of 11.5 to 15.1 tonnes/day and RMSE of 14.5 to 19.7 tonnes/day. The prediction scores of Model A using a Simple Additive Weighting (SAW) based ranking system seems to be negatively influenced by the distribution of the training and testing sets. There is no distinctive trend in the SAW-based prediction scores among the univariate model and the multivariate models. Instead of absolute values, percent changes and SAW relative ranking are adopted, making the proposed approach appropriate for cross-jurisdictional studies.