accomplishments for arc pecas for atlanta regional commission san diego, california december 2010...

Accomplishments for ARC PECAS for

Atlanta Regional Commission

San Diego, CaliforniaDecember 2010

PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

Accomplishments for PECAS Atlanta

We made a short-term plan for Atlanta:1. Trip length calibration2. Option Size (weight) calibration to math aggregate economic

flows and average prices3. Adjust floorspace to match prices for floorspace by zone4. Design a system to adjust Activities I dispersion parameter5. Iterate steps 2 and 36. Rerun floorspace synthesizer with new space totals7. Run the system throw time8. Integrate with travel model


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)


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.


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/)PECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

https://projects.hbaspecto.com/


Trip length calibrationTrip 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 1st opportunity has been taken by another agent– The 1st opportunity has a less attractive price than a

more distant one, and the transport cost does not nullify the more attractive price

– The 1st opportunity is not the best because the random component of the utility function is not high enough

Trip length calibrationTrip 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 buyersPECAS - for Spatial Economic Modelling – Accomplishments for ARC PECAS for Atlanta Regional Commission

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 files description

TLC TargetI.csv Trip length targets

TLCGroupsI.csv Groups of commodities

Histograms.csv Histogram of data – trip length

CommoditiesI.csv File rewritten by TLC.py for each adjusted set of parameters

TLCCalib.csv Report the results of running TLC.py – an ongoing track of parameters, target values, model results of each commodity by iteration (output file)

RunAA.cmd Command to run AA module

Property file Some specifications can be changed by the analyst like: the maximum and minimum increase in parameter value between iterations, number of iterations, etc.


Trip length calibration –targets

Group Target ParamCV Heavy 40 5CV Med/Light 35 5HB Other 17.5 10HB School 15.48 10HB Univ 28.95 10HB Shop 16.4 10HB Work 34 5Work-Other 18 10

Commodity BuyingSelling GroupCG01AgMinDirection selling Work-OtherCG02AgMinOutput buying CV HeavyCG03ConDirection selling Work-OtherCG04ConOutput buying CV HeavyCG05MfgDirection selling Work-OtherCG06MfgOutput buying CV Med/LightCS07TCUDirection selling Work-OtherCS08TCUOutput buying CV Med/LightCS09WsOutput buying CV Med/LightCS10RetailOutput buying HB ShopCS11FIREOutput buying Work-OtherCS13OthServOutput buying HB OtherCS14HealthOutput buying HB OtherCS15GSEdOutput buying HB SchoolCS16HiEdOutput buying HB UnivCS17GovOutput buying Work-OtherCL23WhiteCollar selling HB WorkCL24Services selling HB WorkCL25Health selling HB WorkCL26Retail selling HB WorkCL27BlueCollar selling HB WorkCL28Military selling HB Work

Trip length calibration –groups

Trip length calibration – commoditiesICommodity BuyingDispersionParameter SellingDispersionParameterCG01AgMinDirection 68.1252851 68.1252851CG02AgMinOutput 49.78801179 49.78801179CG03ConDirection 68.1252851 68.1252851CG04ConOutput 49.78801179 49.78801179CG05MfgDirection 68.1252851 68.1252851CG06MfgOutput 203.591045 203.591045CS07TCUDirection 68.1252851 68.1252851CS08TCUOutput 203.591045 203.591045CS09WsOutput 203.591045 203.591045CS10RetailOutput 55.30563015 55.30563015CS11FIREOutput 68.1252851 68.1252851CS13OthServOutput 225.1105299 225.1105299CS14HealthOutput 225.1105299 225.1105299CS15GSEdOutput 67.92086603 67.92086603CS16HiEdOutput 30.45320301 30.45320301CS17GovOutput 68.1252851 68.1252851CF18TaxReceipts 100 100CF19GovSupReceipts 100 100CF20InvestReceipts 100 100CF21ReturnInvestReceipts 100 100CF22CapitalTransferReceipts 100 100CL23WhiteCollar 12.61146422 12.61146422CL24Services 12.61146422 12.61146422CL25Health 12.61146422 12.61146422CL26Retail 12.61146422 12.61146422CL27BlueCollar 12.61146422 12.61146422CL28Military 12.61146422 12.61146422CA29AgMin 0.3 0.3CA30Indust 0.3 0.3CA31Retail 0.3 0.3CA32Offi ce 0.3 0.3CA33Instit 0.3 0.3CA34Military 0.3 0.3CA35DetResid 0.3 0.3CA36HiDenResid 0.3 0.3

• Final dispersion parameters estimated are rewritten in commoditiesI file and only 3 columns are presented from the file:

• Commodity• Buying DP• Selling DP


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


Trip length calibration - internal mechanismWhen 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

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:Iteration Group Parameter Target Model Error

-1 CV Heavy 5 40 41.99974 0.049994-1 CV Med/Light 5 35 40.5237 0.15782-1 HB Other 10 17.5 36.17237 1.066992-1 HB School 10 15.48 35.87879 1.317751-1 HB Univ 10 28.95 36.2689 0.252812-1 HB Shop 10 16.4 35.7822 1.181842-1 HB Work 5 34 38.48391 0.13188-1 Work-Other 10 18 31.30991 0.7394390 CV Heavy 6 40 41.93778 0.0484450 CV Med/Light 6 35 40.41807 0.1548020 HB Other 12 17.5 35.86947 1.0496840 HB School 12 15.48 34.75646 1.245250 HB Univ 12 28.95 35.50324 0.2263640 HB Shop 12 16.4 34.58481 1.108830 HB Work 6 34 37.89962 0.1146950 Work-Other 12 18 30.11248 0.6729151 CV Heavy 13.2 40 41.56704 0.0391761 CV Med/Light 13.2 35 39.95471 0.1415631 HB Other 26.4 17.5 34.06505 0.9465741 HB School 26.4 15.48 27.7125 0.7902131 HB Univ 26.4 28.95 30.30342 0.046751 HB Shop 26.4 16.4 26.63757 0.6242421 HB Work 12.67421406 34 33.95269 -0.001391 Work-Other 26.4 18 24.29315 0.349622 CV Heavy 29.04 40 40.87025 0.0217562 CV Med/Light 29.04 35 39.46775 0.127652 HB Other 58.08 17.5 30.71534 0.7551622 HB School 51.40697264 15.48 19.17192 0.2384962 HB Univ 30.14804829 28.95 29.05432 0.0036042 HB Shop 44.9499715 16.4 19.21768 0.171812 HB Work 12.59422014 34 34.00503 0.0001482 Work-Other 41.97250732 18 21.12699 0.1737223 CV Heavy 48.82337896 40 40.03789 0.0009473 CV Med/Light 63.888 35 38.53573 0.1010213 HB Other 127.776 17.5 23.89269 0.3652973 HB School 62.21698659 15.48 16.61281 0.0731793 HB Univ 30.46108628 28.95 28.94956 -1.52E-053 HB Shop 51.99427879 16.4 17.21629 0.0497733 HB Work 12.60191266 34 34.00352 0.0001033 Work-Other 57.35232231 18 19.11044 0.061691


Trip length calibration • Outputs and results?

CV Heavy

CV Med

/Ligh

t

HB Other

HB School

HB Univ

HB Shop

HB Work

Work-

Other

0

5

10

15

20

25

30

35

40

45

Target

Model

TargetModel

1. 9 iterations were performed and the results of the TLC process by group of trips is shown below:


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


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.


Option Weight calibration – reformatting input file

Activity OptionName OptionWeight CL23WhiteCollar CL24Services CL25Health CL26Retail CL27BlueCollar CL28MilitaryAH30HHlt20_3p whiteCollarLessLowDensity |more| CL23WhiteCollar 5935.356773 20217.97445 0 0 0 0 0AH30HHlt20_3p CL23WhiteCollar | more |CA35DetResid 0.017395456 20217.97445 0 0 0 0 0AH30HHlt20_3p whiteCollarLessHighDensity |more|CL23WhiteCollar 5935.356773 20217.97445 0 0 0 0 0AH30HHlt20_3p CL23WhiteCollar | more |CA36HiDenResid 0.006286832 20217.97445 0 0 0 0 0AH30HHlt20_3p ServicesLessLowDensity | more | CL24Services 2871.483511 0 20217.97445 0 0 0 0AH30HHlt20_3p CL24Services | more |CA35DetResid 0.008412016 0 20217.97445 0 0 0 0AH30HHlt20_3p ServicesLessHighDensity | more | CL24Services 2871.483511 0 20217.97445 0 0 0 0AH30HHlt20_3p CL24Services | more |CA36HiDenResid 0.003045236 0 20217.97445 0 0 0 0AH30HHlt20_3p HealthLessLowDensity |more| CL25Health 1043.823671 0 0 20217.97445 0 0 0AH30HHlt20_3p CL25Health| more |CA35DetResid 0.003056838 0 0 20217.97445 0 0 0AH30HHlt20_3p HealthLessHighDensity |more| CL25Health 1043.823671 0 0 20217.97445 0 0 0AH30HHlt20_3p CL25Health| more |CA36HiDenResid 0.001107897 0 0 20217.97445 0 0 0AH30HHlt20_3p RetailLessLowDensity |more| CL26Retail 4369.250473 0 0 0 20217.97445 0 0AH30HHlt20_3p CL26Retail | more |CA35DetResid 0.01281694 0 0 0 20217.97445 0 0AH30HHlt20_3p RetailLessHighDensity |more| CL26Retail 4369.250473 0 0 0 20217.97445 0 0AH30HHlt20_3p CL26Retail | more |CA36HiDenResid 0.004636865 0 0 0 20217.97445 0 0AH30HHlt20_3p BlueCollarLessLowDensity |more| CL27BlueCollar 7265.960359 0 0 0 0 20217.97445 0AH30HHlt20_3p CL27BlueCollar| more |CA35DetResid 0.021280219 0 0 0 0 20217.97445 0AH30HHlt20_3p BlueCollarLessHighDensity |more| CL27BlueCollar 7265.960359 0 0 0 0 20217.97445 0AH30HHlt20_3p CL27BlueCollar| more |CA36HiDenResid 0.00770909 0 0 0 0 20217.97445 0AH30HHlt20_3p MilitaryLessLowDensity |more| CL28Military 0 0 0 0 0 0 20217.97445AH30HHlt20_3p CL28Military| more |CA35DetResid 0 0 0 0 0 0 20217.97445AH30HHlt20_3p MilitaryLessHighDensity |more| CL28Military 0 0 0 0 0 0 20217.97445AH30HHlt20_3p CL28Military| more |CA36HiDenResid 0 0 0 0 0 0 20217.97445

• 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:

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

0

2000000000

4000000000

6000000000

8000000000

10000000000

12000000000

14000000000

16000000000

18000000000

20000000000

AH29HHlt20_12

AH30HHlt20_3p

AH31HH2050_12

AH32HH2050_3p

AH33HH50100_12

AH34HH50100_3p

AH35HHge100_12

AH36HHge100_3p

AH29HHlt20_12AH30HHlt20_3pAH31HH2050_12AH32HH2050_3pAH33HH50100_12AH34HH50100_3pAH35HHge100_12AH36HHge100_3p

Option Weight calibration – targets for residential space • 8 HH categories CONSUMING 2 residential categories:

detached residential and high density residential

CA35DetResid

0

100000000

200000000

300000000

400000000

500000000

600000000

700000000

800000000

900000000

AH29HH...

AH30HH...

AH31H...AH32H...

AH33HH...

AH34HH...

AH35HH...

AH36HH...

AH29HHlt20_12 AH30HHlt20_3p AH31HH2050_12

AH32HH2050_3p AH33HH50100_12 AH34HH50100_3p

AH35HHge100_12 AH36HHge100_3p


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)


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:

Activity Commodity MorU Target Amount ErrorAH29HHlt20_12 CA35DetResid U -109770400.9 -113237267.8 1.20E+13AH29HHlt20_12 CA36HiDenResid U -132151805.1 -134993387.5 8.07E+12AH29HHlt20_12 CL23WhiteCollar M 741799663.5 741743062.2 3203711433AH29HHlt20_12 CL24Services M 272410144 272308729.7 10284854004AH29HHlt20_12 CL25Health M 130471010.1 130461047.1 99260623.67AH29HHlt20_12 CL26Retail M 485235563.1 485213022.1 508095971.7AH29HHlt20_12 CL27BlueCollar M 629907239.1 629859568.7 2272470251AH29HHlt20_12 CL28Military M 540206.0281 535814.0657 19289333.57


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

Commodity Demand InternalBought Exports Supply InternalSold Imports RMSSurplus AveragePriceCL23WhiteCollar 8.25E+10 8.25E+10 9878.365275 8.25E+10 8.25E+10 5921.634725 966.743037 0.88740169CL24Services 9.56E+09 9.56E+09 4733.930623 9.56E+09 9.56E+09 11066.06938 221.9272732 1.204678857CL25Health 7.42E+09 7.42E+09 4393.902077 7.42E+09 7.42E+09 11406.09792 158.7629407 1.241667361CL26Retail 1.98E+10 1.98E+10 4136.573266 1.98E+10 1.98E+10 11663.42673 579.9524187 1.260048929CL27BlueCollar 2.62E+10 2.62E+10 2609.927121 2.62E+10 2.62E+10 13190.07288 530.3880787 1.340129191CL28Military 3.71E+08 3.71E+08 7454.464901 3.71E+08 3.71E+08 8345.535099 39.46299485 1.127426078CA35DetResid 3.31E+09 3.31E+09 -41.0961712 3.31E+09 0 3.31E+09 17088.51932 23.31885717CA36HiDenResid 2.55E+09 2.55E+09 -18.47601523 2.55E+09 0 2.55E+09 11999.27103 13.77448735


Some events during the option weight calibration• Initially the 1st 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.


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.


Option Weight calibration -results

• How good were the results?• AH29HHlt20_12

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200000000

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• How good were the results?• AH30HHlt20_3p

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• How good were the results?• AH31HH2050_12

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• How good were the results?• AH32HH2050_3p

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• How good were the results?• AH33HH50100_12

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• How good were the results?• AH34HH50100_3p

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• How good were the results?• AH35HHge100_12

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• How good were the results?• AH36HHge100_3p

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Target

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TargetAmount

accomplishments for arc pecas for atlanta regional commission san diego, california december 2010...

Documents

arc pecas

buyers pecas

commodity categories

base year calibration

activity variety approach

zone variety impact

transport cost

model results