Abstract
Urban waste collection is a time-consuming and inefficient procedure for city municipalities. The loads on disposal units might differ by area, day or season. However, garbage trucks empty bins according to predefined routes and days, visiting bins that are often still not filled and increasing unnecessary expenses. This work aims to develop an urban waste collection management solution based on providing intelligence to garbage bins, using an IoT prototype with sensors. The Municipality of Delft (South Holland, Netherlands) is used as a case study. A grid-based approach is designed in order to estimate the Waste Stress Index and to identify 15 out of 1323 bins to be equipped with ultrasonic sensors able to read bins filling levels. The collected sensors data are used to predict future bins filling rates through a Convolutional Neural Network (CNN). Bins are then ranked according to their need to be emptied, and bins to be reached in each shift are selected solving a knapsack problem. To further reduce operational costs, the optimal set of routes for the fleet of garbage trucks are determined solving a graph-based asymmetric Vehicle Routing Problem (VRP). The optimized routes are then displayed both on a web-based Smart Waste Management Dashboard and on a mobile web app provided to waste collectors. As future work, 50 new sensors will be installed to further refine the predictions and increase the ranking accuracy. The CNN will be improved to support changes in garbage behaviour caused by events that take place in the municipality area.
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D’Agostini, M., Venturi, S., Vigo, E. (2022). Smart Urban Waste Management System: The Case Study of Delft, Netherlands. In: Borgogno-Mondino, E., Zamperlin, P. (eds) Geomatics for Green and Digital Transition. ASITA 2022. Communications in Computer and Information Science, vol 1651. Springer, Cham. https://doi.org/10.1007/978-3-031-17439-1_13
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