Developing a simulation Model of Solid Waste Collection Operations using GPS data

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Developing a simulation Model of Solid Waste Collection Operations using GPS data - Bruce Wilson, University of New Brunswick

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Developing a simulation Model of Solid Waste Collection Operations using GPS data

  1. 1. Development of a simulation model of solid waste collection operations using GPS data Dr. Bruce G. Wilson, P.Eng. Thuy Nguyen Department of Civil Engineering University of New Brunswick
  2. 2. Overview  Some history  Outline the purpose of the model  Sources of data  Data analysis  Results  Other things we observed
  3. 3. History of Waste Collection Models  First computer simulation of MSW collection published in 1965  Quon, Tanaka, and Charnes  At least 12 more since then  First paper on MSW collection route optimization published in 1962  Mei-Ko (“Chinese Postman Problem”)  Several hundred papers since then  Very little actual progress
  4. 4. History (this project)  City of Hamilton installed GPS units on five collection vehicles in 2003  City provided the data to the University of New Brunswick (UNB)  UNB has produced several studies • Potential uses of GPS data • Queuing at transfer stations • Estimating fuel consumption on route • Estimating GHG emissions on route • Simulation model in a GIS environment
  5. 5. Simulation Model - Goal • To develop a reliable model of an MSW collection system in GIS environment using GPS data • Purpose of the model is to predict: • Time required to complete various stages of a route (mean and variance) • Fuel consumption for various stages (mean and variance)
  6. 6. Data Used  GPS Data • 700+ days from several vehicles • 1 minute or 15 second sampling  GIS Data • Road network (street length & topology) • Number of houses on each block face  Fuel consumption data • Daily fuel records • published rates for idling vehicles  Weigh scale records • time & weight
  7. 7. GPS Data  GPS data provide a snapshot at a specific time • position (longitude and latitude) • speed • heading angle • 1 minute or 15 sec intervals
  8. 8. GPS Data Analysis  Need to: • Assemble the data into a contiguous collection route (i.e. connect the dots) • Follow the road network • Assign a point to a particular side of the street (based on direction, other points) • Infer activity (travelling, loading, deadheading, etc.)
  9. 9. Assembling a Route
  10. 10. Contiguous Route
  11. 11. Tracing Tool Results  Actual route followed  Total distance traveled  Number and type of turns made (right, left, & U turns)  We still don’t know what the driver was doing at any point
  12. 12. Assigning an Activity
  13. 13. Activity inside a Buffer Area  Each GPS point = 1 min (or 15 s)  If speed ~0, then loading waste  If speed >0 • If truck is loading waste  stop to stop travel • If truck is not loading waste  Deadheading  Estimate average speed, average loading time
  14. 14. Geofences  Similar to buffers, only larger  Count time spent in a specific area  Examples • Garage • Assigned collection area • Unloading Facility  Transfer station, MRF  Allows calculation of travel time between areas, etc.
  15. 15. 2 3 1 4 Dundas Transfer Station Geofences
  16. 16. Putting the pieces together  Simulation model built in a GIS environment (ArcGIS 9.2, VBA scripts)  Information from 3 digital map layers: • (i) street segments, • (ii) collection district parcels, and • (iii) transfer stations (TS)  Model parameters from GPS, fuel consumption, and weigh scale records
  17. 17. 3 required map Main tool layers to start the model 1 collection area (land parcel)
  18. 18. Simulation model description
  19. 19. Results stored in a text file
  20. 20. Model Validation Route 8 Route 9 Route 5 Route 10 Route 7 Garage MRF Route 1 Dundas TS Route 2 Kenora Route 3 TS Route 10 Route 4 Mountain TS
  21. 21. Model Validation  Model validated for 10 routes • 5 low density, 3 medium density, 2 high density • Compared mean and variance of time to complete collection route  Excellent agreement on low density routes (mean 5%, variance ~ equal)  Mixed success on medium and high density routes (mean 15%, variance ~ equal • Traffic?, multifamily?, street topology?
  22. 22. Other Observations  In building the model, we observed several interesting things: • The route followed did not significantly impact total time on the route • Time and fuel required to collect a kg of waste varied significantly depending on the route
  23. 23. 500 Drivers rarely 480 460 440 followed the same Time (min) route from week to 420 400 380 y = 129.25x + 266.84 week 360 2 R = 0.0584 340 320 Drivers were 300 capable of finding a 0.95 1 1.05 1.1 1.15 1.2 Collection efficiency 1.25 1.3 1.35 minimum distance path through the 240 collection area 230 Even when they 220 210 200 followed a very poor Time (min) 190 180 170 path, it added very 160 150 y = 47.977x + 124.75 little time to their collection day 2 140 R = 0.0586 130 120 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5 1.55 Collection efficiency
  24. 24. Collection Efficiency Population Truck Average waste L fuel per kgCO2E per density quantity Tonne-waste Tonne-waste (T/day) Low density Co-collection truck 6.56 33.5 3.5 92.4 9.6 Garbage packer 8.39 14.6 1.4 40.3 3.9 Med. density Co-collection truck 5.90 10.1 0.7 27.8 2.0 High density Co-collection truck 6.33 5.8 0.8 16.0 2.1 Garbage packer 14.93 3.2 0.4 8.9 1.1
  25. 25. Conclusions  The large quantity of GPS data allowed us to develop a statistically reliable simulation model of MSW collection operations  Accuracy of the model could be improved by more frequent sampling of GPS data (~ 1 - 5 seconds)  Integrating the model with a GIS allows the model to be interpreted visually  Many of the variations (time, fuel, efficiency) appear to be related to housing density  Routing of trucks within a collection area may not be as important as we’ve been led to believe  Modeling allows us to determine basic efficiency measures
  26. 26. Acknowledgements City of Hamilton, Dept. of Public Works Natural Sciences and Engineering Research Council of Canada (Dr.) Thuy Nguyen, (P.Eng.) Betsy Agar, P.Eng. Julie Vincent, P.Eng.
  27. 27. Questions? Dr. Bruce G. Wilson, P.Eng. Dept. of Civil Engineering University of New Brunswick Fredericton, N.B., Canada wilsonbg@unb.ca

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