Monitoring Hospital Visitors Could Enhance the Prediction of the Plastic Waste Collection Demand and Its Management

Richao Cong (), Toru Matsumoto and Atsushi Fujiyama
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Richao Cong: Department of Regional Cultural Policy and Management, Graduate School of Cultural Policy and Management, Shizuoka University of Art and Culture, 2-1-1 Chuo, Chuo-ku, Hamamatsu 430-8533, Japan
Toru Matsumoto: Institute of Environmental Science and Technology, University of Kitakyushu, 1-1 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan
Atsushi Fujiyama: Institute of Environmental Science and Technology, University of Kitakyushu, 1-1 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan

Waste, 2025, vol. 3, issue 3, 1-20

Abstract: A novel framework is proposed to support the prediction of the plastic waste (PW) collection demand, route optimization, and overall management of PW from individual facilities. Based on electronic manifests from a local recycling company in Fukuoka, Japan, we developed a two-step artificial intelligence (AI)-based approach for predicting the demand for industrial PW (IPW) collection from a hospital. The daily hospital visitor numbers were introduced as a new independent variable in the IPW collection demand prediction. The stability (robustness) of each model was measured by its variance through experiments for two variable groups in four validation months. We found that introducing the visitor variables into IPW collection demand predictions was effective. A high monthly mean accuracy (85.06%) was achieved in predicting the daily IPW collection demand, which exceeded the accuracy of predictions using models without visitor records (84.44%). The stability of the Fine tree model with the highest prediction accuracy for March 2020 was 0.0466 ∓ 0.0174. Based on the findings of this study, we propose several strategies for waste management: enhancing prediction models, controlling visitor flows, and analyzing working patterns. This study successfully links AI techniques with a human mobility monitoring system (location data) for waste management using MATLAB.

Keywords: AI; future prediction; industrial plastic waste; location data; plastic waste management (search for similar items in EconPapers)
JEL-codes: Q1 Q16 Q18 Q2 Q20 Q23 Q24 Q25 Q28 Q3 Q31 Q38 Q5 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jwaste:v:3:y:2025:i:3:p:23-:d:1706279

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