Intelligent waste collection optimization using AI and IoT in urban environments Authors
Bibliographic record
Abstract
Efficient waste collection remains a critical challenge in rapidly urbanizing cities, where conventional fixed-route systems often result in redundant trips and overflow incidents. This study proposes an integrated framework that combines IoT-based fill-level sensing, short-term prediction using long short-term memory (LSTM) networks, and route optimization through a capacity-constrained traveling salesman problem (TSP) model solved by genetic algorithms. A three-month pilot was conducted across 150 collection points in a high-density district, with two experimental groups using the AI-IoT framework and one control group operating under conventional schedules. The results demonstrated that the experimental districts achieved a 24.8% reduction in redundant collection trips and a 30.2% decrease in overflow incidents, both statistically significant at p < 0.05. Furthermore, the LSTM prediction module reached a coefficient of determination of R² = 0.92 with a mean absolute error of 0.07, outperforming baseline regression methods. These findings confirm that integrating predictive analytics and IoT-enabled monitoring within a closed feedback loop can deliver measurable environmental and operational benefits. The framework not only enhances immediate efficiency but also provides a scalable model for sustainable urban waste management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".