Development of Waste Collection Model Using Mobile Phone Data: A Case Study in Latvia
- 19 Downloads
In organizing household waste management and controlling waste collection and disposal, it is necessary to minimise risks to the environment and human health and, where possible, ensure that waste is recycled and returned to the economic cycle. Different models are being applied to increase waste collection management efficiency, but in recent years, the mobile phone data is widely used to solve various application problems. The research objective is to develop a waste collection model, which responds to the population’s current demands and allows planning waste container loading, based on mobile phone data statistics. The developed approach, techniques and data model can be used for waste container analysis and optimisation of their placement near small commercial structures, information kiosks, residential areas and other places attracting larger amounts of people. The developed relational data model includes information about mobile phone base stations, waste container data, calendar table and geographic location table. Further steps include data processing and data modelling in order to generate a data model for visual and quantitative analysis. The methods and data analysis techniques used in this research could be used to build a commercial product for mobile data operators allowing predicting the most appropriate placement of waste containers in any territory where mobile base station data is available. The choice of any of the proposed strategies allows achieving both direct benefits, like increasing the collected amount of recyclable glass, and indirect benefits – an increase in the amount of glass collected in the remaining containers.
KeywordsConceptual architecture Functionality model Principal Component Analysis
The research leading to these results has received funding from the research project “Development of Responsive Glass Waste Collection System”, the contract Nr. ZD2018/20580 signed between the University of Latvia and Eco Baltia Vide, Ltd.
- 4.Van Engeland, J., Beliën, J., De Boeck, L., De Jaeger, S.: Literature review: strategic network optimization models in waste reverse supply chains. Omega 91, 102012 (2020). https://doi.org/10.1016/j.omega.2018.12.001
- 10.Bwambale, A., Choudhury, C.F., Hess, S.: Modelling trip generation using mobile phone data: a latent demographics approach. J. Transp. Geogr. 76, 276–286 (2019). https://doi.org/10.1016/j.jtrangeo.2017.08.020
- 12.Arhipova, I., Berzins, G., Brekis, E., Opmanis, M., Binde, J., Steinbuka, I., Kravcova, J.: Pattern identification by factor analysis for regions with similar economic activity based on mobile communication data. Adv. Intell. Syst. Comput. 886, 561–569 (2019)Google Scholar
- 13.Arhipova, I., Berzins, G., Brekis, E., Kravcova, J., Binde, J.: The methodology of region economic development evaluation using mobile positioning data. In: 20th International Scientific Conference on Economic and Social Development, pp. 111–120. Varazdin Development and Entrepreneurship Agency, Prague, University North, Koprivnica, Croatia, Faculty of Management University of Warsaw, Poland (2017)Google Scholar
- 14.Arhipova, I., Berzins, G., Brekis, E., Binde, J., Opmanis, M.: Mobile phone data statistics as proxy indicator for regional economic activity assessment. In: 1st International Conference on Finance, Economics, Management and IT Business, pp. 27–36. SCITEPRESS – Science and Technology Publication, Lda., Crete, Greece (2019)Google Scholar