1. Accomplishments for ARC PECAS for Atlanta Regional Commission San Diego, California December 2010 PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

2. Accomplishments for PECAS Atlanta We made a short-term plan for Atlanta: 1.Trip length calibration 2.Option Size (weight) calibration to math aggregate economic flows and average prices 3.Adjust floorspace to match prices for floorspace by zone 4.Design a system to adjust Activities I dispersion parameter 5.Iterate steps 2 and 3 6.Rerun floorspace synthesizer with new space totals 7.Run the system throw time 8.Integrate with travel model PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

3. Trip length calibration Objective: to calibrate the dispersion parameter (DP) in the third level of AA module in PECAS, which is the DP for the commodity exchange location choice. Some ideas in the concept: This DP has the control of different things in PECAS: – The degree to which the remaining parameter values are inadequate in explaining behaviour – The importance of variety (having different options to choose from) – It has an inversed relationship with variety (higher the DP less important is the variety) PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

4. Trip length calibration Some ideas in the concept (Continuation): This DP is representing 2 choices for each commodity: – Where to ship a commodity for sale? = selling DP – Where to purchase a commodity? = buying DP Specifically: – These two parameters control the degree to which within- zone VARIETY impact results. Different from other DP which control the degree to which between-zone VARIETY impact results. PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

5. Trip length calibration There are 4 approaches to adjust these DP: 1.Direct commodity variety approach, 2.Activity variety approach, 3.Trip length distribution approach (this is what we decide to use) 4.Approach through inspection of commodity utility output (link on wiki : https://projects.hbaspecto.com/)https://projects.hbaspecto.com/ PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

6. Trip length calibration Trip length distribution approach: a) There are 3 reasons why people travel beyond the closest opportunity when they are selling or buying something in PECAS: – The 1 st opportunity has been taken by another agent – The 1 st opportunity has a less attractive price than a more distant one, and the transport cost does not nullify the more attractive price – The 1 st opportunity is not the best because the random component of the utility function is not high enough PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

7. Trip length calibration Trip length distribution approach: b) Some explanations: – If in the base year calibration AA runs ‘constrained’ mode the analyst can not do anything to influence reason1 – But, changes in transport cost can affect reason 2. – If transport cost are set the other influence on trip length associated commodities is reason 3. An increase in VARIETY within commodity categories causes trip length increase as more distant options are embraced due to their random terms. A decrease in SellDP and in BuyDP leads to longer trips for sellers and buyers PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

8. Trip length calibration How do we do this calibration ? We applied a program called trip length calibration written in phyton code (TLC.py) The TLC.py repeatedly runs AA to match trip length for commodities or groups of commodities to specified target values, adjusting the DP for buying and selling in a file called CommoditiesI.scv Input and output files? Input filesdescription TLC TargetI.csvTrip length targets TLCGroupsI.csvGroups of commodities Histograms.csvHistogram of data – trip length CommoditiesI.csvFile rewritten by TLC.py for each adjusted set of parameters TLCCalib.csvReport the results of running TLC.py – an ongoing track of parameters, target values, model results of each commodity by iteration (output file) RunAA.cmdCommand to run AA module Property fileSome specifications can be changed by the analyst like: the maximum and minimum increase in parameter value between iterations, number of iterations, etc.

9. Trip length calibration –targets Trip length calibration – groups PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

10. Trip length calibration – commoditiesI Final dispersion parameters estimated are rewritten in commoditiesI file and only 3 columns are presented from the file: Commodity Buying DP Selling DP

11. Trip length calibration – TLC script Script settings should be adjusted for the study case, indicating: – targetFileName – groupFileName – histofileName – commodFilename – model command (for example, RunT05.py for scenario T05) – filesToVersion – Others: upperClip, lowerClip, maxIts, gapRange and initScale PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

12. Trip length calibration - internal mechanism When TLC runs, it first reads in the group and target information, then follows a number of steps: 1.Write a CommoditiesI.csv (with the most recent DP) 2.Run AA 3.Read the resulting trip length data which Are compared to the targets and used to adjust the DP 4.DP are written to CommoditiesI.csv AA is run once for the initial guesses at DP specified in TLCTargetsI and a second time with DP altered by a fixed proportion (1.2 by default) If the model is greater than the target TLC will scale the DP up by multiplying by this proportion, otherwise it will divide by this proportion PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

13. Trip length calibration Outputs and results? 1.To check if TLC is doing a good job we should check TLCCalib.csv. A partial view of the output file is shown below:

14. Trip length calibration Outputs and results? 1.9 iterations were performed and the results of the TLC process by group of trips is shown below: PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

15. Option Weight calibration Objective: to calibrate Option Size (weight) term in TechnologyOptionsI.csv to match aggregate economic flows and average prices for non-space Some ideas in the concept: There are different levels of preference (more or less than the average) from activities making or using commodities in different rates (for example, HH using low density or high density residential space). This option weight term is a constant or a weight added to the utility function affecting activities' decision of making or using commodities (for example, this term affects how much people from HH wants to work in retail? As a blue collar? As a white collar? among other options) How do we do this calibration ? We applied a program called OSC or OptionSizeCalibI.py PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

16. Option Weight calibration Inputs and adjustments? 1.To apply OSC TechnologyOptionsI will need some reformatting “|More|” to indicate OSC which technology options affect which commodities. 2. OptionSizeCalibI.csv is the target specification file. Each row specifies activity, commodity, relationship (m or u), and target (amount to be produced or consumed). 3. The OSC is a phyton code program, the file name is optionsizecalib.py. It can be easily run locally or using a program called putty.exe to run from the server. The user only should make the adjustments of the input files indicating the correct path and file directly in the script. PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

17. Option Weight calibration – reformatting input file Preparing the necessary reformatting for TecnollogyOptionsI.csv including the flag “|more|” + name of commodity indicating a relationship of M or U between activities and commodities participating as options in the preferences. An example of activity AH30HHlt20_3p choosing to produce labour of different categories in different rates is presented bellow: PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

18. Option Weight calibration – targets for labour production 8 HH categories PRODUCING 6 labour categories

19. Option Weight calibration – targets for residential space 8 HH categories CONSUMING 2 residential categories: detached residential and high density residential

20. Option Weight calibration – OSC script Script settings should be adjusted for the study case, indicating: – sourceFile (MakeUse.csv) – targetFile (OptionSizeCalibI.csv) – techfile (reformatted TecnologyOptionsI) – outFile (OptionSizeCheck.csv) – model command (for example, RunT05.py for scenario T05) – Others: maxUp, maxDown, maxIts, and KeepAllFiles option (True to track OSC as it adjust parameters for every iteration in AA) PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

21. Option Weight calibration Outputs and results? 1.To check if OSC is doing a good job we should check OptionSizeCheck.csv_##.csv where ## is the iteration number. We can open this files during the OSC job to see if we are getting results in the wright direction. An example of this output file is presented below for 1 activity: PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

22. Option Weight calibration Outputs and results? 1.We should also check ExchangeResutsI.csv to see what’s happening with the commodities average prices, which should be always positive, but sometimes they can turn negative, depending on the option weight term in TechnologyoptionsI.csv PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

23. Some events during the option weight calibration Initially the 1 st time we run the OSC program we started getting problems with the prices for all the labour categories. The prices were turning negative. Applying some strategies (like multiplying the option weight term by 10, 100, 1000, etc) and running the OSC again, we solved the problem for all of the labour categories, except for military labour. Military labour had a different behaviour turning negative while the others were turning positive. Then, we realized that most of the military labour were in the activity “import providers military”. More than the 80% of the military labour is being produced by this activity. Only a smaller quantity is being produced by the HH categories. As a result, we included the imports and exports in the target file OptionsSizeCalib.csv, and run again. Also applying some strategies during the calibration process (like dividing the option weight term by 10, 100, 1000, etc) but still military prices was turning negative. PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

24. Some events during the option weight calibration For the exports we used as targets of some labour categories the amount reported in the BaseAmount column in the ActivityTotalsI.csv file. Then we decide to make the import of military labour elastic, with two options for importers: – a) less than the average rate (with an option weight of 1 and 80% of the supply) – b) more than the average rate (with an option weight of 0.00001 and 120% of the supply). After this, we got positive prices and good results for this labour category. The next step was to include the targets for residential floorspace, detached and high density. In general, could be said that the calibration process was very straight forward. Any problem came out during the floorspace calibration process and the results from the calibration are more close to the targets than the ones gotten from the labour categories. PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

25. Option Weight calibration -results How good were the results? AH29HHlt20_12 PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

26. Option Weight calibration -results How good were the results? AH30HHlt20_3p PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

27. Option Weight calibration -results How good were the results? AH31HH2050_12 PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

28. Option Weight calibration -results How good were the results? AH32HH2050_3p PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

29. Option Weight calibration -results How good were the results? AH33HH50100_12 PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

30. Option Weight calibration -results How good were the results? AH34HH50100_3p PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

31. Option Weight calibration -results How good were the results? AH35HHge100_12 PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

32. Option Weight calibration -results How good were the results? AH36HHge100_3p PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission