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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Comparison of waste bin placement optimization methods: A case study in Zurich.

Silas Schweizer¹, Nicolas Seemann-Ricard¹, Jakub Tkaczuk¹

¹Global Health Engineering, Department of Mechanical and Process Engineering, ETH Zürich, Zürich 8092, Switzerland.

Waste Management (New York, N.Y.)

|June 13, 2026

Summary

To improve recycling participation, this study optimized locations for new collection points. Using a mixed-integer programming model, 12 new sites significantly reduced the underserved population by 30%, enhancing accessibility for residents.

Keywords:

Facility location optimization GIS Mixed integer linear programming Municipal solid waste Pedestrian accessibility Recycling collection points

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Area of Science:

Urban Planning
Environmental Science
Operations Research

Background:

Recycling participation is sensitive to the convenience of collection points.
In Zurich, over 29,000 residents exceed the 10-minute walking distance to current recycling facilities.
Inequitable access to recycling services can lead to lower participation rates.

Purpose of the Study:

To develop and evaluate an accessibility-based framework for optimal placement of additional Recycling Collection Points (RCPs).
To identify the minimum number and best locations for new RCPs to meet Zurich's 10-minute walking accessibility target.
To compare the effectiveness of spatial clustering and mathematical optimization methods for siting RCPs.

Main Methods:

Developed an accessibility-based siting framework using elevation-aware network routing.
Compared two Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methods against a Mixed Integer Linear Programming (MILP) p-median formulation.
Evaluated site selection from a screened set of 305 candidate car park locations.

Main Results:

Mixed Integer Linear Programming (MILP) optimization achieved 2-5% better coverage than DBSCAN methods.
Adding 12 new Recycling Collection Points (RCPs) reduced the underserved population by approximately 30% (from 29,320 to 20,451 residents).
Average walking time decreased marginally, indicating concentrated improvements for the most underserved populations.

Conclusions:

The MILP optimization approach is more effective for selecting optimal Recycling Collection Points (RCPs) compared to clustering methods.
The proposed framework successfully identifies strategic locations to improve pedestrian accessibility to recycling services.
Future research should incorporate multi-objective optimization including operational factors and agent-based demand modeling.