Smart City Gnosys
Smart city article details
| Title | A Sustainable Framework To Manage Plastic Waste In Urban Environments Using Open Data |
|---|---|
| ID_Doc | 5375 |
| Authors | Sidhu N.; Terroso-Sáenz F.; Ortiz G.; Muñoz A. |
| Year | 2023 |
| Published | EAI/Springer Innovations in Communication and Computing, Part F633 |
| DOI | http://dx.doi.org/10.1007/978-3-031-30514-6_12 |
| Abstract | With the continuous increase in plastic waste, efficient urban planning policies and plastic management can often be a demanding task. This chapter promotes the use of open data to plan an urban waste management setting in cities. Thus, we have collected the demographics, the number of venues, the number of street segments, and the location of municipal bins in four Western cities: New York City, Malaga, Madrid, and Stavanger. These data were used to plan an urban waste management setting in the target cities in the Philippines and India, where there is a lack of plastic waste management. A linear regression analysis was performed on the reference city data to determine relevant variables in the urban planning model. A Weighted Least Square analysis was applied to the different variables to identify the needed number of bins in the target cities. Finally, we have integrated these results with our previous work on a low-cost smart household bin in order to design routes for waste pickers. The simulation scenarios proposed in India and the Philippines show an average collection rate of over 80% by promoting the use of sustainable means of transport such as bikes and on foot in the routes suggested to the waste pickers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Collaborative systems; Foursquare; Intelligent environments; Location-based services; Machine learning; Open data; OpenStreetMap; Route prediction; Sensors; Smart city; Sustainability; Urban planning; Urban venues; Waste management; Weighted least squares |
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