Recycling Route Optimization Simulation

ISEF Category: Environmental Engineering

Subcategory: Recycling and Waste Management  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A garbage truck does not care how smart your route looks on paper. Real streets, traffic patterns, and pickup density can make the same route waste fuel or save a lot of it. You can model that difference with real city maps and data. That turns a local cleanup problem into a testable research project.

What Is It?

This project studies how to plan recycling pickup routes so trucks use less fuel and produce fewer emissions for each ton they collect. You build a simulation, then let different route rules compete. Think of it like giving the trucks a map and asking, “Which path gets the job done with the least waste?”

An agent-based simulation means you model simple actors, like trucks, streets, and pickup zones, then watch how they behave together. OpenStreetMap gives you real road data. Waste-generation density tells you where recycling builds up faster. When you combine those, you can test whether a route should follow short roads, dense pickup areas, or a balance of both.

Why This Is a Good Topic

This is a strong science fair topic because you can test real routing ideas without a city fleet or a lab. You can change one variable at a time, like pickup density, truck capacity, or street layout, and measure how fuel use or emissions change. The topic connects to waste management, air pollution, and city planning. You also learn data cleaning, map analysis, simulation design, and result comparison, which makes the project feel like real research.

Research Questions

• How does adding waste-generation density to route planning change fuel use per ton collected?
• What is the effect of truck capacity on total route length and emissions per ton collected?
• Does a shortest-distance route always reduce fuel use compared with a density-aware route?
• To what extent do neighborhood street patterns change the best recycling pickup order?
• Which routing rule produces the lowest emissions when collection stops are spread across a city grid?
• How does using real OpenStreetMap road data change route performance compared with a simplified street map?

Basic Materials

• Computer with internet access.
• OpenStreetMap city map data.
• Spreadsheet software for data cleaning and tables.
• Python installed with pandas and networkx, or another route analysis setup.
• Free GIS software such as QGIS for map viewing.
• Public data on recycling pickup areas or neighborhood waste estimates.
• Measuring tools for model outputs, such as a calculator and notebook.

Advanced Materials

• Computer with strong memory for large simulations.
• Python with OSMnx, pandas, networkx, numpy, and matplotlib.
• QGIS for spatial inspection and map layers.
• Public municipal open data on waste collection routes or recycling volumes.
• EPA or government emission factor data for heavy-duty trucks.
• OpenStreetMap extracts or regional road network files.
• Optional Monte Carlo simulation tools for uncertainty testing.

Software & Tools

• Python: Runs the simulation, route comparisons, and statistical analysis.
• QGIS: Lets you inspect road networks, neighborhood boundaries, and pickup zones.
• OSMnx: Downloads and builds real street-network graphs from OpenStreetMap data.
• pandas: Cleans route, stop, and waste-density tables.
• matplotlib: Plots route length, fuel use, and emissions across scenarios.

Experiment Steps

1. Define the routing question you will test, such as shortest path versus density-aware pickup order.
2. Choose one city or neighborhood and build a street network from OpenStreetMap data.
3. Assign waste-generation values to pickup zones so each stop has a measurable demand.
4. Design the route rules you will compare and keep the truck assumptions the same across tests.
5. Build a simulation that calculates distance, stop count, fuel proxy, and emissions per ton collected.
6. Compare outcomes with clear graphs, then check whether your best route still wins after changing key assumptions.

Common Pitfalls

• Using street maps without cleaning dead ends or disconnected roads, which can make the route solver fail or choose impossible paths.
• Mixing up pickup density and total waste mass, which makes the demand model look realistic but behave wrong.
• Comparing routes with different truck capacity settings, which hides the real effect of the routing rule.
• Relying on one neighborhood only, which can make a citywide claim from a tiny sample.
• Measuring success by distance alone, which can miss stop time, fuel use, and emissions per ton collected.

What Makes This Competitive

A stronger project goes past simple shortest-path routing. You can test multiple city layouts, compare more than two routing strategies, and include emissions per ton instead of just miles driven. You can also add uncertainty analysis, like changing waste density or truck capacity and checking whether your result still holds. That kind of careful modeling looks much more like real engineering research.

Project Variations

• Test the same routing model on recycling pickup in suburbs versus dense downtown blocks.
• Add traffic delay or one-way street constraints to see how they change the best route.
• Compare electric and diesel truck emission models using the same route simulation.

Learn More

• OpenStreetMap Wiki: Search the wiki for road network data, tags, and map structure basics.
• MIT OpenCourseWare, Introduction to Algorithms: Use the free course materials to review shortest paths and graph ideas.
• QGIS Documentation: Find tutorials for loading shapefiles and viewing neighborhood layers.
• NASA Earthdata: Look for free mapping and geospatial data tutorials if you want a broader spatial analysis skillset.
• EPA Emission Factors Hub: Search EPA resources for vehicle emission factor guidance and related public documents.
• PubMed: Search for review articles on municipal solid waste collection optimization and route planning.