Modelling Transport
– CUBE Guide
Examples based on the book
‘Modelling Transport’, 4th edition
by Ortuzar/Willumsen
(© 2011 John Wiley & Sons, Ltd)
Citilabs © 2011
§
About Cube : a brief intro to the modelling system and its modules
§About the tutorial : The Guide and Book in tandem
§
Click on the app boxes below to get to the examples
§Chapter 3 : Data and Storage§Chapter 4 : Trip Generation Modelling §Chapter 5 : Trip Distribution Modelling
§Chapter 6 : Modal Split and Direct Demand Models
§Chapter 8 : Specification and Estimation of Discrete Choice Models §Chapter 10 : Assignment
§Chapter 11 : Equilibrium and Dynamic Assignment §Chapter 12 : Simplified Transport Demand Models §Chapter 14 : Activity Based Models
Desktop Professional Modeling
a comprehensive suite for passenger, freight, land use, and
traffic simulation
–
Cube Cloud Services - Modeling on the Internet
web-accessed, cloud computing framework for model application
–Modeling Extensions for ArcGIS
modeling and analysis tools for non-modelers
Cube: Professional Desktop Modeling
Suite
Click to edit Master text styles
Second level
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Third level
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Fourth level
●
Fifth level
System Interface
§
Cube Base – comprehensive interface for data editing, mapping, reporting,
model development and scenario creation and management
Demand Modeling
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Cube Voyager: urban, regional and long distance demand forecasting and
assignment
§
Cube Land : land use model for combined transport-land use modeling
§Cube Cargo: commodity-based freight forecasting
Simulation
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Cube Avenue: meso-scopic traffic simulation (DTA)
§Cube Dynasim: multimodal micro-simulation
Specialized
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Cube Cluster: reduces run-times by allocating calculations over multiple
processors and machines
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Cube Analyst: advanced matrix estimation for all modes
Overview
Cube Base – Built for Scenario
Testing
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Easy to use environment to create, test, manage and analyze scenarios.
§Menus to prompt user for inputs and parameters of test
Overview
Cube Base – Flow-Chart: Easy Model
Development
§
Famous for its flow-charting environment for designing and building transportation
models
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Modules are accessed through pull-down menus
§Dropped into a flow chart
Overview
Cube Base – A Transportation-GIS Built on
ESRI
§
Only modeling system that comes
with a complete transportation GIS
built on ESRI’s marketing leading
GIS technology.
§
Store all data directly in ESRI’s
geodatabase format.
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No need to convert data back and
forth between the GIS department
and the modeling team
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ArcGIS Extension for network editing
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Cube GIS
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Click-drag-drop functionality for all typical modelling tasks
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Cube integrates GIS powered by ESRI
§
Uses ESRI geodatabase technology
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Supports other formats; Shape, CAD, Raster
§
Cube Model Builder (Application Manager)
§
Flow-chart system for model design, coding and documentation
§
Visual creation of the model ‘macro’
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Gives direct access to data and scripts
§
Cube Scenario Manager
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Highlights key model parameters and data for easy creation and testing
of scenarios
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Creates customised run menu for defining and running scenarios
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Provides easy links to main output data for viewing and comparing
Overview
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Cube Reports
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Charts and Tables integrated in model
View and compare scenario specific statistics
Cube Base – the complete software
interface
Comprehensive and Integrated
Covers all facets of transportation modeling
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people
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goods
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land use
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region-wide traffic simulation
§multi-modal microsimulation
Open system
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Seamless links to other software
§Microsoft Office (Access, Excel)
§User programs
This system of Cube modelling tutorials is based on Modelling Transport 4th Edition
(the 'book')
§
The book has been used since its first edition in 1990 by transport modellers around
the world to find and select appropriate methods to their modelling tasks and for
understanding the theoretic base behind these methods
§
The book includes a wealth of examples of how to model, in terms of approach and
mathematics, the various aspects of transport.
§
This guide provides a set of applications showing the examples of the book coded
up in Cube scripts, grouped according to the book's chapters.
About the book : Modelling Transport 4th
edition
§
Suggestions for the MODELLER :
•
find and study the appropriate method in
the book
•
study the relevant example in the book
•see how this example is coded in Cube
•use the script as a base for your model
and its data
§
Suggestions for the STUDENT :
•
read the relevant chapter/model type
and its modelling methods in the book
•
study the examples
•
see how these example are coded in
Cube
§
About the MT Cube Guide :
Version 1.0 September 2011
Authors : Citilabs professionals
© Citilabs 2011
§
About the 'book' :
Modelling Transport 4th Edition, © 2011 John Wiley & Sons,
Ltd
§
Modelling Transport" 4th Edition, John Wiley & Sons
§Available from http://
eu.wiley.com/WileyCDA/WileyTitle/productCd-0470760397.html
§
Also available from www.Amazon.com and www.Amazon.co.uk
§
Available as hard copy and electronic book for Kindle, iPad and other
tablets
§
The authors have been involved with transport modelling research for over 40 years
About the book : Availability and authors
§
Juan de Dios Ortuzar
●
Present
•
Professor of Transport
Engineering at
Pontificia Universidad
Católica de Chile
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Advisor to governments and
international agencies
§
Luis (Pilo) Willumsen
●
Present
•
Director of Luis Willumsen
Consultancy
•
Visiting Professor at University
College London
●
Past
§
Part of the Cube’s Learning Center on
http://
www.citilabs.com/support-services/learning-center
§
Comes with downloadable Cube catalog with examples scripted in Cube
§Open to all users of Cube and to those who are evaluating the system
§
This guide will make it easier for modellers to put the lessons learned in the book to
practical use in models
§
The main purpose of this guide is to promote sound modeling practices based on
established methods described and discussed in the book
§
This guide is also meant to encourage the modelling community to keep up to date
on modeling methods and principles; the book is an ideal starting point
§
The main page with links to the apps with examples from each chapter of the book
Chapter 4 : Trip Generation Modelling
Ch4-TGM : Ex4.1 - Growth-factor Modelling
Ch4-TGM: Ex4.1 - Growth - base year
RUN PGM=MATRIX PRNFILE="C4MAT00A.PRN" MSG='Example 4.1 - Base Year'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter4\Results_4_1_BaseYear.DBF", FIELDS=Z, GEN
FILEI DBI[1] = "C:\Modelling Transport_CG\Input\Chapter4\SocioEconData_4_1_BY.dbf" PAR ZONES=1
C=DBIReadRecord(1,1)
; read input data
ZONEN=DI.1.Z ; zone numbers (only 1 zone in the current example)
X1=DI.1.NO_CAR ; number of households without a car
X2=DI.1.WITH_CAR ; number of households with 1 or more cars ; calculation of total number of trips per day
GEN=X1*{GenRateNCar}+X2*{GenRateCar} ; trips per day
; ti=250 x 2.5 + 250 x 6.0 = 2125 trips/day (See page 144) ; save results to output file
RO.Z=ZONEN RO.GEN=GEN WRITE RECO=1
Ch4-TGM: Ex4.1 - Future year
growth-factor
RUN PGM=MATRIX PRNFILE="C4MAT00B.PRN" MSG='Example 4.1 - Future Year - Growth-factor method'
FILEI DBI[3] = "C:\Modelling Transport_CG\Output\Chapter4\Results_4_1_BaseYear.DBF", SORT=Z FILEI DBI[2] = "C:\Modelling Transport_CG\Input\Chapter4\SocioEconData_4_1_FY.dbf", SORT=Z
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter4\Results_4_1_FutureYear.DBF", FIELDS=Z GEN FILEI DBI[1] = "C:\Modelling Transport_CG\Input\Chapter4\SocioEconData_4_1_BY.dbf"
PAR ZONES=1
C=DBIReadRecord(1,1)
; read input data Base Year
ZONEN=DI.1.Z ; zone numbers (only 1 zone in the current example)
X1_B=DI.1.NO_CAR ; number of households without a car
X2_B=DI.1.WITH_CAR ; number of households with 1 or more cars
D=DBISeek(2,1)
; read input data Future Year
X1_F=DI.2.NO_CAR ; number of households without a car
X2_F=DI.2.WITH_CAR ; number of households with 1 or more cars
E=DBISeek(3,1)
; read ti
GEN_B=DI.3.GEN
; calculation of total number of trips per day
Factor2=(X2_F/(X1_F+X2_F))/(X2_B/(X1_B+X2_B))
; Fi=C_di/C_ci (See page 144)
GEN_F=Factor2*GEN_B
; Ti = 2 x 2125 = 4250 trips/day (See page 144) ; save results to output file
Ch4-TGM: Ex4.1 - Future year growth
applying
trip rates
RUN PGM=MATRIX PRNFILE="CHAPTER4\C4MAT00C.PRN" MSG='Example 4.1 - Future Year - Applying Trip Rates'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter4\Results_4_1_FutureYear_TR.DBF", FIELDS=Z, GEN
FILEI DBI[1] = "C:\Modelling Transport_CG\Input\Chapter4\SocioEconData_4_1_FY.dbf" PAR ZONES=1
C=DBIReadRecord(1,1)
; read input data
ZONEN=DI.1.Z ; zone numbers (only 1 zone in the current example)
X1=DI.1.NO_CAR ; number of households without a car
X2=DI.1.WITH_CAR ; number of households with 1 or more cars ; calculation of total number of trips per day
GEN=X1*{GenRateNCar}+X2*{GenRateCar} ; trips per day
; ti=250 x 2.5 + 250 x 6.0 = 2125 trips/day (See page 144) ; save results to output file
RO.Z=ZONEN RO.GEN=GEN WRITE RECO=1
Ch4-TGM : Ex4.3 - Linear regression
Ch4-TGM: Ex4.3 – Linear regression
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER4\E4MAT01A.PRN“, MSG='Exmple 4.3 - Linear regression with intercept=0'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter4\RegrCoeffAndStats_4_3.DBF", FIELDS=B(10.8) R2(10.8) SERR(10.8) T_VALUE(10.8)
FILEI DBI[1] = "C:\Modelling Transport_CG\Input\Chapter4\SocioEconData_4_3.dbf" PAR ZONES=1
; Linear regression with intercept=0 ; Y = a + bX
; a=0
LOOP K=1,DBI.1.NUMRECORDS C=DBIReadRecord(1,K)
; read input data
X1=DI.1.X1 ; number of workers in household
Y=DI.1.Y ; observed trips per household ; b= sum(xy)/sum(x^2)
_XY=X1*Y+_XY _X=X1^2+_X ENDLOOP
Ch4-TGM: Ex4.3 – Linear regression
; script continued from previous page
B=_XY/_X
LOOP K=1,DBI.1.NUMRECORDS C=DBIReadRecord(1,K)
; read input data
X1=DI.1.X1 ; number of workers in household
Y=DI.1.Y ; observed trips per household
Y_MOD=B*X1 _SSRESID=(Y-Y_MOD)^2+_SSRESID _SSTOTAL=(Y^2)+_SSTOTAL _SSX=(X1^2)+_SSX ENDLOOP
N=DBI.1.NUMRECORDS-1 ; sample size minus number of regressors (1 in this case)
R2=1-(_SSRESID/_SSTOTAL) ; R squared
SERR=SQRT(_SSRESID/N)/SQRT(_SSX) ; Standard error for coefficient B
T_VALUE=B/SERR ; t-value for coefficient B ; save results to output file
Ch4-TGM : Additional examples
Ch4-TGM: A – Processing tabular data in
MATRIX
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER4\E4MAT01D.PRN"
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter4\CrossTabulation.dbf", FIELDS=INC_CLASS(50) ZERO_CARS ONE_CAR TWOPL_CARS TOTAL
FILEI RECI = "C:\Modelling Transport_CG\Input\Chapter4\ExampleTable.DBF", SORT=INCOME ARRAY CARS=4 ARRAY CLASS=5 CLASS[1]=125 CLASS[2]=250 CLASS[3]=500 CLASS[4]=750 CLASS[5]=2000 IF (_ID=0) _PCLASS=0 _CLASS=1 ENDIF
IF(_INCOME>=CLASS[_CLASS] & _ID>0)
RO.INC_CLASS='Between'+Str(CLASS[_PCLASS],4,0)+' and '+Str(CLASS[_CLASS],4,0)+' (US$/month)' RO.ZERO_CARS=CARS[1]
RO.ONE_CAR=CARS[2]
Ch4-TGM: A – Processing tabular data in
MATRIX
; script continued from previous page
WRITE RECO=1 CARS[1]=0 CARS[2]=0 CARS[3]=0 CARS[4]=0 _CARS=0 _PCLASS=_PCLASS+1 _CLASS=_CLASS+1 ENDIF _ID=RI.ID _CARS=RI.CARS+1 _INCOME=RI.INCOME CARS[_CARS]=1+CARS[_CARS]
Ch4-TGM: A – Processing tabular data in
MATRIX
; script continued from previous page
IF(I=0)
RO.INC_CLASS='Between'+Str(CLASS[_PCLASS],4,0)+' and '+Str(CLASS[_CLASS],4,0)+' (US$/month)' RO.ZERO_CARS=CARS[1] RO.ONE_CAR=CARS[2] RO.TWOPL_CARS=CARS[3]+CARS[4] RO.TOTAL =CARS[1]+CARS[2]+CARS[3]+CARS[4] WRITE RECO=1 CARS[1]=0 CARS[2]=0 CARS[3]=0 CARS[4]=0 _CARS=0 _CLASS=_CLASS+1 ENDIF ENDRUN
Ch4-TGM: B – Use of GENERATION Program
RUN PGM=GENERATION PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER4\E4GEN01A.PRN"
FILEO PAO[1] = "C:\Modelling Transport_CG\Output\Chapter4\TripEnd.dbf", FORM=8.0, LIST=Z(2),
P[1] P[2] P[3] P[4] A[1] A[2] A[3] A[4],DBF=T, NAMES = TAZ WORKP SCHOOLP SHOPP TOTP,WORKA SCHOOLA SHOPA TOTA FILEI ZDATI[1] = "C:\Modelling Transport_CG\Input\Chapter4\cubetown.mdb\Demographics"
; set number of zones
PARAMETERS ZONES = 4 PROCESS PHASE=ILOOP
; --- calculate generations by purpose
P[1] = (0.960 * zi.1.HH1 + 1.370 * zi.1.HH2 + 1.995 * zi.1.HH3 + 1.778 * zi.1.HH4) P[2] = (0.900 * zi.1.HH1 + 0.382 * zi.1.HH2 + 0.325 * zi.1.HH3 + 0.500 * zi.1.HH4) P[3] = (0.480 * zi.1.HH1 + 1.370 * zi.1.HH2 + 1.465 * zi.1.HH3 + 1.524 * zi.1.HH4)
; --- calculate attractions by purpose
A[1] = 1.450 * zi.1.TOTAL_EMP
A[2] = 0.144 * 9.0 * zi.1.RETAIL + 1.8 * zi.1.SERVICE + 0.5 * zi.1.OTHER + 0.9 * zi.1.HOUSEHOLDS A[3] = 0.220 * 9.0 * zi.1.RETAIL + 1.8 * zi.1.SERVICE + 0.5 * zi.1.OTHER + 0.9 * zi.1.HOUSEHOLDS ENDPROCESS
PROCESS PHASE=ADJUST
; --- adjust zonal attractions so total attractions match total productions
BALANCE A2P=1,2,3; balance attrs to prods for purposes 1, 2, 3
;for details on the method see pag.156 4.2.6 Matching Generations and Attraction totals
P[4] = P[1] + P[2] + P[3] A[4] = A[1] + A[2] + A[3] ENDPROCESS
Chapter 5: Trip Distribution Modelling
Ch5-TDM: Ex5.1 - Uniform Growth-factor
Ch5-TDM: Ex5.1 - Uniform Growth-factor
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT00A.PRN“, MSG='Example 5.1 - Uniform Growth Factor (tau)=1.2'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_1.MAT", MO=1 NAME="Future matrix"
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Base_year_trip_matrix_5_1.dbf", PATTERN=IJ:V FIELDS=O D TRIPS
PAR ZONES=4
; multiply matrix per Tau (ratio of expanded over previous total number of trips) ; Tij = (tau)*tij (5.3) ; where: ; MW[1]=Tij ; MI.1.1=tij ; (tau)={tau} MW[1]=MI.1.1*{tau} ENDRUN
Ch5-TDM: Ex5.2 - Singly Constrained
Ch5-TDM: Ex5.2 – Preparing trip end data
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT02A.PRN“, MSG='Example 5.2 - total number of trips generated per zone'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_2_A.MAT", MO=1
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Total_number_of_trips_generated_Base_year_5_2.DBF", FIELDS=ZONE SUM_J
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Base_year_trip_matrix_5_1.dbf", PATTERN=IJ:V FIELDS=O D TRIPS
PAR ZONES=4
;initialize work variables
_Generated=0
;read tij
MW[1]=MI.1.1
;sum tij over destinations
_Generated= ROWSUM(1) ;sum over cell by row ;write totals per origins
RO.ZONE=I
RO.SUM_J=_Generated WRITE RECO=1
Ch5-TDM: Ex5.2 – Singly constrained
growth
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT02B.PRN“, MSG='Example 5.2 - Expanded origin-constrained growth trip table'
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Base_year_trip_matrix_5_1.dbf", PATTERN=IJ:V FIELDS=O D TRIPS
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_2.MAT", MO=1 DEC=1*5 NAME="Future matrix"
FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_future_year_5_2.dbf"
FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\Output\Chapter5\Total_number_of_trips_generated_Base_year_5_2.DBF" LOOKUP LOOKUPI=1, NAME=Base, LOOKUP[1]=ZONE, RESULT=SUM_J, FAIL[3]=0 LOOKUP LOOKUPI=2, NAME=Future, LOOKUP[1]=ZONE, RESULT=OI, FAIL[3]=0
;Set parameters and array
PAR ZONES=4 ARRAY TAU=4
;read tij
MW[1]=MI.1.1
; calculate (tau)I : (tau)i=Ti/ti
; Future(1,I)=Ti totals generated per zone in future matrix; Base(1,I)=Ti totals generated per zone in base year
TAU[I]=Future(1,I)/Base(1,I)
;multiply each origin-destination value per origin values (same value per each row in the matrix)
MW[1]=MW[1]*TAU[I]
Ch5-TDM: Ex5.2 – Alternative way with
FRATAR
§
This example uses Cube’s template system
RUN PGM=FRATAR PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5FRA02A.PRN“, MSG='Example 5.2 - alternative way using FRATAR'
FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\Output\Chapter5\Total_number_of_trips_generated_Base_year_5_2.DBF" FILEI MATI[1] = {MATI.Q}, PATTERN=IJ:V FIELDS=O D TRIPS
FILEI LOOKUPI[1] = {LOOKUPI.Q}
FILEO MATO[1] = {MATO.Q}, MO=1,NAME=FACTOREDMAT, DEC=1*D PARAMETERS ZONES=4, MAXITERS=1
; USE A LOOKUP FILE TO find appropriate factors (1.0 is assumed unless expressly provided)
LOOKUP LOOKUPI=1, LIST=Y, NAME=GROWTHF,
LOOKUP[1]=ZONE,RESULT=OI, FAIL[1]=1,FAIL[2]=1,FAIL[3]=1 LOOKUP LOOKUPI=2, LIST=Y,
NAME=GROWTHB,
LOOKUP[1]=ZONE,RESULT=SUM_J, FAIL[1]=1,FAIL[2]=1,FAIL[3]=1
;using fratar syntax we have:
; P[1]= generation values for purpose [1]
; A[1]=0 not used to constrain the final matrix; ; MW[1]=tij
; CONTROL[1]: define how update the original matrix
; CONTROL[1]=P set the matrix matching the generation values
SETPA P[1]=GROWTHF(1,I) A[1]=0 MW[1]=MI.1.1 CONTROL[1]=P
;alternative control values are:
; CONSTROL[1]=A for a destination-specific factors
; CONTROL[1]=PA the average value between generation and attraction values
ENDRUN
Ch5-TDM: Ex5.2 – Alternative way with
FRATAR
RUN PGM=DISTRIBUTION PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5DST02A.PRN“, MSG='Example 5.2 - alternative way using DISTRIBUTION'
FILEI ZDATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_future_year_5_2.dbf", Z=ZONE FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\FUTURE_ESTIMATED_TRIP_MATRIX_5_2_B.MAT", MO=1 DEC=5 NAME="Future Matrix"
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_2_A.MAT" PARAMETERS MAXITERS=1
;using DISTRIBUTION syntax we have:
; P[1]= generation values for purpose [1]
; A[1]=1 not used to constrain the final matrix; ; MW[21]=tij
; ATTSUM: total of generated ; PAF= (tau)i
MW[21]=MI.1.1
SETPA P[1]=ZI.1.OI A[1]=1 MW[1]=A[1]*MW[21]
ATTSUM=ROWSUM(1)
Ch5-TDM: Ex5.2 – Alternative way with
DISTRIBUTION
Ch5-TDM: Ex5.3 - Doubly Constrained
Ch5-TDM: Ex5.3 – Doubly constrained
distribution
§
This example uses Cube’s template system
RUN PGM=FRATAR PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5FRA03A.PRN“, MSG='Example 5.3 - using FRATAR "as steps from 2 to 9"'
FILEI MATI[1] = {MATI.Q}
FILEI LOOKUPI[1] = {LOOKUPI.Q} FILEO MATO[1] = {MATO.Q},
MO=1,NAME=FACTOREDMAT, DEC=1*5 PARAMETERS MAXRMSE=0 MAXITERS=25
; USE A LOOKUP FILE TO find appropriate factors (1.0 is assumed unless expressly provided)
LOOKUP LOOKUPI=1, LIST=Y, NAME=GROWTHF,
LOOKUP[1]=ZONE,RESULT=OI, LOOKUP[2]=ZONE,RESULT=DJ, FAIL[1]=1,FAIL[2]=1,FAIL[3]=1
SETPA P[1]=GROWTHF(1,I)*100 A[1]=GROWTHF(2,I)*100 MW[1]=MI.1.1*100 CONTROL[1]=AP MW[1]=MW[1]/100
ENDRUN
Ch5-TDM: Ex5.3 – Doubly constrained
distribution
§
This is a script generated by a template
Ch5-TDM: Ex5.3 – Step 1 - Preparing trip
end data
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03A.PRN“, MSG='Example 5.3 - total number of trips generated and attracted per zone'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_3_A.MAT", MO=1
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Total_#trips_gen_and_attr_Base_year_5_3.DBF", FIELDS=ZONE SUM_J SUM_I
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_2_A.MAT", PATTERN=IJ:V FIELDS=O D TRIPS
;read the base matrix
MW[1]=MI.1.1 MW[2]=MI.1.1.T
;calculate the generated and attracted values
_Generated= ROWSUM(1) ;sum over cell by row
_Attracted= ROWSUM(2) RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted
;save into table
WRITE RECO=1
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03B.PRN“, MSG='Example 5.3 - step (2)'
FILEO PRINTO[1] = "C:\Modelling Transport_CG\Output\Chapter5\ai_and_bj_values_5_3.txt", APPEND=T FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5LOO03A.DBF"
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5LOO03A.MAT", PATTERN=IJ:V FIELDS=O D TRIPS FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_3_1.MAT",
MO=1 DEC=1*6 NAME="Step 2"
FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_Dj_future_year_5_3.dbf" LOOKUP LOOKUPI=1, NAME=Base, LOOKUP[1]=ZONE, RESULT=SUM_J, FAIL[3]=0 LOOKUP LOOKUPI=2, NAME=Future, LOOKUP[1]=ZONE, RESULT=OI, FAIL[3]=0
;calculate (tau)i (as example 5.2) ;set parameters and array
PAR ZONES=4 ARRAY TAU=4 ;base matrix MW[1]=MI.1.1 ;(tau)i=Ti/ti TAU[I]=Future(1,I)/Base(1,I)
;multiply base matrix per (tau)i
MW[1]=MW[1]*TAU[I]
PRINT PRINTO=1 LIST='Iteration: ', @IDLOOP@(L) ' Zone ', I(L), ' ai= ' TAU[I](10.5)
ENDRUN
Ch5-TDM: Ex5.3 – Step 2 - Doubly
constrained
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03D.PRN“, MSG='Example 5.3 - total number of trips after (1)'
FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03B.DBF", FIELDS=ZONE SUM_J(10.6) SUM_I(10.6)
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_3_1.MAT"
;read the base matrix
MW[1]=MI.1.1 MW[2]=MI.1.1.T
;calculate the generated and attracted values
_Generated= ROWSUM(1) ;sum over cell by row
_Attracted= ROWSUM(2) RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted
;save into table
WRITE RECO=1
ENDRUN
Ch5-TDM: Ex5.3 – Step 2 – prepare trip
ends
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03C.PRN" MSG='Example 5.3 - step(3)'
FILEO PRINTO[1] = "C:\Modelling Transport_CG\Output\Chapter5\ai_and_bj_values_5_3.txt", APPEND=T FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03B.DBF"
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_3_1.MAT"
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03C.MAT", MO=1 DEC=1*6 NAME="Step 2" FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_Dj_future_year_5_3.dbf"
LOOKUP LOOKUPI=1, NAME=Base, LOOKUP[1]=ZONE, RESULT=SUM_I, FAIL[3]=0 LOOKUP LOOKUPI=2, NAME=Future, LOOKUP[1]=ZONE, RESULT=DJ, FAIL[3]=0
;calculate (tau)j (as example 5.2 but with a transposed matrix, since we have to work on destinations) ;set parameters and array
PAR ZONES=4 ARRAY TAU=4
;base matrix
MW[1]=MI.1.1.T ;this is the transposed matrix ;(tau)j=Tj/tj
TAU[I]=Future(1,I)/Base(1,I)
;multiply base matrix per (tau)j
MW[1]=MW[1]*TAU[I]
PRINT PRINTO=1 LIST='Iteration: ', @IDLOOP@(L) ' Zone ', I(L), ' bj= ' TAU[I](10.5)
ENDRUN
Ch5-TDM: Ex5.3 – Step 3 - Doubly
constrained
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03E.PRN“, MSG='Example 5.3 - traspose matrix'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_3_2.MAT", MO=1 DEC=1*6 NAME="Step 2"
FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03C.DBF", FIELDS=ZONE SUM_J(10.6) SUM_I(10.6)
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT03C.MAT"
;read the base matrix (note that next step will work on i values, the input matrix must be transposed)
MW[1]=MI.1.1.T MW[2]=MI.1.1
;calculate the generated and attracted values
_Generated= ROWSUM(1) ;sum over cell by row
_Attracted= ROWSUM(2) RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted
;save into table
WRITE RECO=1
ENDRUN
Ch5-TDM: Ex5.4 - Properties of the Gravity
Model
§
Click on the script file boxes below to see the scripts
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT14I.PRN“, MSG='Example 5.4 - matrix exp(-beta*cij)'
FILEI ZDATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_Dj_future_year_5_3.dbf", Z=ZONE FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT14F.MAT",
MO=1-2 DEC=2*5 NAME="Cost Matrix" "Exp Term"
FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT14D.DBF", FIELDS=TOTAL_E(10.5) TOTAL_F(10.5) TAU(10.5)
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Cost_matrix_base_year_5_4.dbf", PATTERN=IJ:V FIELDS=O D TIME
;set parameters
PAR ZONES=4
;read the base matrix
MW[1]=MI.1.1
;calculate exp(-(beta)*cij)
MW[2]=exp(-{beta}*MW[1])
;calculate the base matrix total
_TOTAL=ROWSUM(2)+_TOTAL
;calculate the future matrix total
_TOTAL_F=ZI.1.OI+_TOTAL_F IF(I=ZONES)
;calculate the expansion factor tau=T/t
RO.TOTAL_E=_TOTAL RO.TOTAL_F=_TOTAL_F
RO.TAU =_TOTAL_F/_TOTAL
Ch5-TDM: Ex5.4 – Gravity distribution with
FRATAR
§
This example uses Cube’s template system
RUN PGM=FRATAR PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5FRA14A.PRN“, MSG='Example 5.4 - Gravity model using "FRATAR" as steps from 3 to 9'
FILEI MATI[1] = {MATI.Q}
FILEI LOOKUPI[1] = {LOOKUPI.Q}
FILEO MATO[1] = {MATO.Q}, MO=1,NAME=FACTOREDMAT, DEC=1*5 PARAMETERS MAXRMSE=0 MAXITERS=50
; USE A LOOKUP FILE TO find appropriate factors (1.0 is assumed unless expressly provided)
LOOKUP LOOKUPI=1, LIST=Y, NAME=GROWTHF,
LOOKUP[1]=ZONE,RESULT=OI, LOOKUP[2]=ZONE,RESULT=DJ, FAIL[1]=1,FAIL[2]=1,FAIL[3]=1 MW[3]=MI.1.1
SETPA P[1]=GROWTHF(1,I)*100 A[1]=GROWTHF(2,I)*100 MW[1]=MI.1.2*100 CONTROL[1]=P MW[1]=MW[1]/100
FREQUENCY BASEMW=3,VALUEMW=1,RANGE=1-24,4,REPORT=99, TITLE='Ranges(min)'
ENDRUN
Ch5-TDM: Ex5.4 – Gravity distribution with
FRATAR
Ch5-TDM: Ex5.5 - Bi-proportional Fitting
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5MAT05A.PRN“, MSG='Example 5.5 - create tables 5.11 and 5.13'
FILEO MATO[2] = "C:\Modelling Transport_CG\Output\Chapter5\Future_estimated_trip_matrix_5_5_B.MAT", MO=2 NAME="Base Marix B"
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_5.MAT", MO=1 NAME="Base Marix A"
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_2_A.MAT"
;read the original data
MW[1]=MI.1.1 MW[2]=MI.1.1
;modify original data to perform exercises
;set to zero cells (2,1) (2,3) (2,4) and (2,2)=50 Table 5.11
JLOOP
IF(I=2&&J=1,3-4) MW[1]=0 IF(I=2&&J=2) MW[1]=50 ENDJLOOP
;set to zero cells (2,1) (2,3) and (2,2)=50 (2,4)= 1 Table 5.13
JLOOP
IF(I=2&&J=1,3) MW[2]=0 IF(I=2&&J=2) MW[2]=50 IF(I=2&&J=4) MW[2]=1 ENDJLOOP
Ch5-TDM: Ex5.5 – Bi-proportional Fitting,
step 1
Ch5-TDM: Ex5.5 – Bi-proportional Fitting,
step 2
§
This example uses Cube’s template system
RUN PGM=FRATAR PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5FRA05A.PRN“, MSG='Example 5.5 - 100 iterations Table 5.12'
FILEI MATI[1] = {MATI.Q}
FILEI LOOKUPI[1] = {LOOKUPI.Q} FILEO MATO[1] = {MATO.Q},
MO=1,NAME=FACTOREDMAT, DEC=1*5 PARAMETERS MAXRMSE=0 MAXITERS=100
; USE A LOOKUP FILE TO find appropriate factors (1.0 is assumed unless expressly provided)
LOOKUP LOOKUPI=1, LIST=Y, NAME=GROWTHF,
LOOKUP[1]=ZONE,RESULT=OI, LOOKUP[2]=ZONE,RESULT=DJ, FAIL[1]=1,FAIL[2]=1,FAIL[3]=1
SETPA P[1]=GROWTHF(1,I)*100 A[1]=GROWTHF(2,I)*100 MW[1]=MI.1.1*100 CONTROL[1]=AP MW[1]=MW[1]/100
ENDRUN
Ch5-TDM: Ex5.5 – Bi-proportional Fitting,
step 2
Ch5-TDM: Ex5.5 – Bi-proportional Fitting,
step 3
§
This example uses Cube’s template system
RUN PGM=FRATAR PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E5FRA05B.PRN“, MSG='Example 5.5 - 100 iterations Table 5.13'
FILEI MATI[1] = {MATI.Q}
FILEI LOOKUPI[1] = {LOOKUPI.Q} FILEO MATO[1] = {MATO.Q},
MO=1,NAME=FACTOREDMAT, DEC=1*5 PARAMETERS MAXRMSE=0 MAXITERS=100
; USE A LOOKUP FILE TO find appropriate factors (1.0 is assumed unless expressly provided)
LOOKUP LOOKUPI=1, LIST=Y, NAME=GROWTHF,
LOOKUP[1]=ZONE,RESULT=OI, LOOKUP[2]=ZONE,RESULT=DJ, FAIL[1]=1,FAIL[2]=1,FAIL[3]=1
SETPA P[1]=GROWTHF(1,I)*100 A[1]=GROWTHF(2,I)*100 MW[1]=MI.1.1*100 CONTROL[1]=AP MW[1]=MW[1]/100
ENDRUN
Ch5-TDM: Ex5.5 – Bi-proportional Fitting,
step 3
Ch5-TDM: Ex5.6 – A Tri-proportional
Problem
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.PRN“, MSG='Example 5.6 - costs and delta_ijm'
FILEO MATO[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.MAT", MO=2 NAME='Delta mij'
FILEI MATI[2] = "C:\Modelling Transport_CG\Output\Chapter5\Base_year_trip_matrix_5_3_A.MAT" FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.DBF",
FIELDS=TOTAL_E(18.10) TOTAL_F(18.10) TAU(18.10)
FILEI DBI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Target_TLD_future_year_5_6.dbf",SORT=RANGE_MIN RANGE_MAX FILEO RECO[2] = "C:\Modelling Transport_CG\Output\Chapter5\Delta_ijm.DBF", FIELDS=M I J DELTA
FILEI ZDATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_Dj_future_year_5_3.dbf", Z=ZONE
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.MAT", MO=3 DEC=1*5 NAME="Base Matrix" FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Cost_matrix_base_year_5_4.dbf",
PATTERN=IJ:V FIELDS=O D TIME
;set parameters and array
PAR ZONES=4
ARRAY CLASS_MIN =6 ;minimum in the n class
ARRAY CLASS_MAX =6 ;maximum in the n class ;read ranges per each class
IF(I=1) LOOP K=1,DBI.1.NUMRECORDS C=DBIReadRecord(1,K) MCLASS=DI.1.M RMIN =DI.1.RANGE_MIN RMAX =DI.1.RANGE_MAX
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
; SCRIPT CONTINUED FROM PREVIOUS PAGE ;read base cost matrix
MW[1]=MI.1.1 ;costs in minutes
MW[3]=1
;create the 3-dimension matrix for i, j and m with: ; 1 - if cost for the cell is in class m
; 0 - if cost for the cell is not in class m ;note:
; the same information can be represented by an i-j matrix where the od value is the class ; to make more efficient the application has been applied this representation.
; loop over classes
LOOP T=1,6 JLOOP IF(I=1&&J=2) MW[2]=3 RO.M=T RO.I=I RO.J=J RO.DELTA=1 WRITE RECO=2 ELSEIF(I=2&&J=3) MW[2]=4 RO.M=T RO.I=I RO.J=J RO.DELTA=1
WRITE RECO=2 ; script continues on NEXT page
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
; SCRIPT CONTINUED FROM PREVIOUS PAGE ELSEIF(I=3&&J=1) MW[2]=5 RO.M=T RO.I=I RO.J=J RO.DELTA=1 WRITE RECO=2 ELSEIF(CLASS_MIN[T]<MW[1]&&MW[1]<=CLASS_MAX[T]) MW[2]=T RO.M=T RO.I=I RO.J=J RO.DELTA=1 WRITE RECO=2 ELSE RO.M=T RO.I=I RO.J=J RO.DELTA=0 WRITE RECO=2 ENDIF ENDJLOOP
ENDLOOP ; script continues on NEXT page
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
; SCRIPT CONTINUED FROM PREVIOUS PAGE
;calculate the base matrix total
_TOTAL=ROWSUM(3)+_TOTAL
;calculate the future matrix total
_TOTAL_F=ZI.1.OI+_TOTAL_F IF(I=ZONES)
;calculate the expansion factor tau=T/t
RO.TOTAL_E=_TOTAL RO.TOTAL_F=_TOTAL_F RO.TAU =_TOTAL_F/_TOTAL WRITE RECO=1 ENDIF ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06B.PRN"
FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Target_TLD_future_year_5_6.dbf" FILEI MATI[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.MAT"
FILEO RECO[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06C.DBF", FIELDS=OTIME OTRIP(10.5) MTRIP(18.10)
FILEI DBI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.DBF" FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06B.DBF", FIELDS=ZONE SUM_J(18.10) SUM_I(18.10)
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06B.MAT", MO=3 DEC=1*5 NAME="Matrix Prepared"
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.MAT" ARRAY TFD =6
LOOKUP LOOKUPI=1, NAME=FUTVAL,
LOOKUP[1]=M, RESULT=TLD, INTERPOLATE=T
;initialize work variables
C=DBIReadRecord(1,1) _TAU=DI.1.TAU ; 1
;read rescaled base matrix
MW[1]=MI.1.1 MW[2]=MI.1.1.T
;multiply per tau value
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
; SCRIPT CONTINUED FROM PREVIOUS PAGE
;calculate the generated and attracted values
_Generated= ROWSUM(3) ;sum over cell by row
_Attracted= ROWSUM(4) RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted WRITE RECO=1
;classify trips per length classes
LOOP T=1,6 JLOOP IF(MW[5]=T) TFD[T]=TFD[T]+MW[3] ELSE TFD[T]=TFD[T] ENDIF ENDJLOOP ENDLOOP
; merge the trip length distribution for base year with forecasting year
IF(I=4) LOOP U=1,6 RO.OTIME=U RO.OTRIP=FUTVAL(1,U) RO.MTRIP=TFD[U] WRITE RECO=2 ENDLOOP ENDIF ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06C.PRN“, MSG='Example 5.6- ai calculation'
FILEO PRINTO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06K.PRN", APPEND=T FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6LOO06A.DBF"
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6LOO06A.MAT"
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06C.MAT", MO=1 DEC=1*10 NAME="Step 2" FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_Dj_future_year_5_3.dbf"
LOOKUP LOOKUPI=1, NAME=Base, LOOKUP[1]=ZONE, RESULT=SUM_J, FAIL[3]=0 LOOKUP LOOKUPI=2, NAME=Future, LOOKUP[1]=ZONE, RESULT=OI, FAIL[3]=0
;calculate (tau)i (as example 5.2) ;set parameters and array
PAR ZONES=4 ARRAY TAU=4 ;base matrix MW[1]=MI.1.1 ;ai=Ti/ti TAU[I]=Future(1,I)/Base(1,I)
;multiply base matrix per ai
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 1
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06E.PRN“, MSG='Example 5.6- revised totals after ai correction'
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06C.MAT" FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06D.DBF", FIELDS=ZONE SUM_J(18.10) SUM_I(18.10)
;read the base matrix
MW[1]=MI.1.1 MW[2]=MI.1.1.T
;calculate the generated and attracted values
_Generated= ROWSUM(1) ;sum over cell by row
_Attracted= ROWSUM(2) RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted
;save into table
WRITE RECO=1
ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 0
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06F.PRN“, MSG='Example 5.6 - bj calculation'
FILEO PRINTO[1] = "C:\Modelling Transport_CG\Applications\Chapter5\E6MAT06K.PRN", APPEND=T FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06C.MAT"
FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06D.DBF"
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06D.MAT", MO=1 DEC=1*10 NAME="Step 2" FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\Input\Chapter5\Target_Oi_Dj_future_year_5_3.dbf"
LOOKUP LOOKUPI=1, NAME=Base, LOOKUP[1]=ZONE, RESULT=SUM_I, FAIL[3]=0 LOOKUP LOOKUPI=2, NAME=Future, LOOKUP[1]=ZONE, RESULT=DJ, FAIL[3]=0
;calculate bj (as example 5.2 but with a transposed matrix, since we have to work on destinations) ;set parameters and array
PAR ZONES=4 ARRAY TAU=4
;base matrix
MW[1]=MI.1.1.T ;this is the transposed matrix ;bj=Tj/tj
TAU[I]=Future(1,I)/Base(1,I)
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 2
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.PRN“, MSG='Example 5.6- transpose matrix and Fm calculation'
FILEO PRINTO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06L.PRN", APPEND=T
FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\Input\Chapter5\Target_TLD_future_year_5_6.dbf" FILEI MATI[3] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.MAT"
FILEI MATI[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.MAT" FILEO RECO[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06F.DBF", FIELDS=OTIME OTRIP(18.10) MTRIP(18.10) FM(18.10)
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06D.MAT"
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06E.MAT", MO=1 DEC=1*10 NAME="Step 2 Tras" FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06E.DBF",
FIELDS=ZONE SUM_J(18.10) SUM_I(18.10) ARRAY TFD=6 LOOKUP LOOKUPI=1, NAME=FUTVAL, LOOKUP[1]=M, RESULT=TLD, INTERPOLATE=T MW[1]=MI.1.1.T MW[2]=MI.1.1
MW[3]=MI.2.1 ;base cost matrix
MW[4]=MI.3.1 ;classes
;calculate the generated and attracted values
_Generated= ROWSUM(1) ;sum over cell by row
_Attracted= ROWSUM(2)
; script continues on NEXT page
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 2
; SCRIPT CONTINUED FROM PREVIOUS PAGE
RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted
;save into table
WRITE RECO=1
;classify trips per length classes
LOOP T=1,6 JLOOP IF(MW[4]=T) TFD[T]=TFD[T]+MW[1] ELSE TFD[T]=TFD[T] ENDIF ENDJLOOP ENDLOOP
; merge the trip length distribution for base year with forecasting year
IF(I=4) LOOP U=1,6 RO.OTIME=U RO.OTRIP=FUTVAL(1,U) RO.MTRIP=TFD[U] _fm=FUTVAL(1,U)/TFD[U] RO.FM =FUTVAL(1,U)/TFD[U] WRITE RECO=2
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 2
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06I.PRN“, MSG='Example 5.6 - Fm* deltaijm calculation'
FILEI MATI[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.MAT" FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06F.DBF" FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06E.MAT" FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06F.MAT", MO=3 DEC=1*10 NAME="Step 3"
LOOKUP LOOKUPI=1, NAME=DetFunction, LOOKUP[1]=OTIME, RESULT=FM, FAIL[3]=0 MW[1]=MI.1.1 ;demand MW[2]=MI.2.1 ;classes JLOOP
;calculate the future demand based on the formulation: ; Tij=ai*bj*(Sum)m*Fm*(delta)ijm
MW[3]=MW[1]*DetFunction(1,MW[2]) ENDJLOOP
ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 3
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06J.PRN"
FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06G.DBF", FIELDS=ZONE SUM_J(18.10) SUM_I(18.10)
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06F.MAT"
;read the base matrix
MW[1]=MI.1.1 MW[2]=MI.1.1.T
;calculate the generated and attracted values
_Generated= ROWSUM(1) ;sum over cell by row
_Attracted= ROWSUM(2) RO.ZONE=I
RO.SUM_J=_Generated RO.SUM_I=_Attracted
;save into table
WRITE RECO=1
ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
step 3
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06D.PRN“, MSG='Example 5.6 - Tri-proportional Problem'
FILEI MATI[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.MAT" FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06F.MAT" FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06G.MAT", MO=1 DEC=1*10 NAME="Future matrix"
MW[1]=MI.1.1 MW[2]=MI.2.1
FREQUENCY BASEMW=2,VALUEMW=1,RANGE=1-6,1,REPORT=99, TITLE='Ranges(min)'
ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
results
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06M.PRN“, MSG='Example 5.6 - Final ai , bj and FM parameters'
FILEI DBI[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06A.DBF" FILEO RECO[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06I.DBF", FIELDS=N_ITER_F CLASS FM(18.10)
FILEI DBI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06L.PRN", DELIMITER=',' N_ITER_F=#1 CLASS=#2 FM=#3
FILEI RECI = "C:\Modelling Transport_CG\Applications\Chapter5\E6MAT06K.PRN" FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.DBF", FIELDS=N_ITER ZONE AI(18.10) BJ(18.10)
ARRAY AI=4,5 ;number of zones*number of iterations
ARRAY BJ=4,5 ;number of zones*number of iterations
ARRAY FM=6,5 ;number of classes *number of iterations ;initialize work variables
C=DBIReadRecord(2,1) _TAU=DI.2.TAU N_ITER =RECI.NFIELD[2] ZONE =RECI.NFIELD[4] _count=_count+1 IF(_count=1-4) AI[ZONE][N_ITER] =RECI.NFIELD[6] ELSE
Ch5-TDM: Ex5.6 – Tri-proportional problem,
results
; SCRIPT CONTINUED FROM PREVIOUS PAGE IF(I=0) LOOP K=1,4 ; LOOP T=1,3 ; RO.N_ITER=T RO.ZONE =K RO.AI =AI[K][1]*AI[K][2]*AI[K][3]*AI[K][4]*AI[K][5] RO.BJ =BJ[K][1]*BJ[K][2]*BJ[K][3]*BJ[K][4]*BJ[K][5] WRITE RECO=1 ; ENDLOOP ENDLOOP LOOP K=1,DBI.1.NUMRECORDS C=DBIReadRecord(1,K) N_ITER_F =DBI.1.NFIELD[1] CLASS =DBI.1.NFIELD[2] FM[CLASS][N_ITER_F]=DBI.1.NFIELD[3] IF(N_ITER_F=5) RO.N_ITER_F=N_ITER_F RO.CLASS =CLASS RO.FM=FM[CLASS][1]*FM[CLASS][2]*FM[CLASS][3]*FM[CLASS][4]*FM[CLASS][5]*_TAU WRITE RECO=2 ENDIF ENDLOOP ENDIF ENDRUN
Ch5-TDM: Ex5.6 – Tri-proportional problem,
results
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06G.PRN“, MSG='Example 5.6 - Check results'
FILEI LOOKUPI[2] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.DBF" FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter5\Estimated_matrix_5_6.MAT", MO=2 Name='Estimated matrix' DEC=10
FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06I.DBF" FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER5\E6MAT06H.MAT" LOOKUP LOOKUPI=1, NAME=DetFunction, LOOKUP[1]=CLASS, RESULT=FM, FAIL[3]=0 LOOKUP LOOKUPI=2, NAME=AiBj_value, LOOKUP[1]=ZONE, RESULT=AI, LOOKUP[2]=ZONE, RESULT=BJ, FAIL[3]=0 MW[1]=MI.1.1 ;classes JLOOP MW[2]=AiBj_value(1,I)*AiBj_value(2,J)*DetFunction(1,MW[1]) ENDJLOOP
Ch5-TDM: Ex5.6 – Tri-proportional problem,
results
Chapter 6: Modal Split and Direct Demand
Models
Ch6-MSM: Ex6.2 – Binomial and
Multinomial Split
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT01A.PRN“, MSG='Example 6.2 - car and bus costs'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Costs_6_2_A.MAT", MO=1-2 NAME='Costs CAR' 'Costs BUS'
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter6\Costs_6_2_A.DBF", PATTERN=IJM:V FIELDS=O D M COST
PAR ZONES=2
FILLMW MW[1]=MI.1.1(2)
ENDRUN
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT01B.PRN“, MSG='Example 6.2 - Multimodal-split model using XCHOICE'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Probabilities_6_2_A.MAT", MO=4-6 Name='Car propabibilty' 'Bus probability' 'Composite cost kijn'
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter6\Costs_6_2_A.MAT"
;costs
MW[1]=MI.1.1 ;car
MW[2]=MI.1.2 ;bus ;demand
JLOOP
IF(I=1&&J=2) MW[3]=1 ;demand (just to calculate probabilities the demand is defined equal 1)
ENDJLOOP XCHOICE,
ALTERNATIVES=car, bus, ; list choices
DEMANDMW = 3, ; input total demand
COSTSMW= 1, 2, ; input costs
ODEMANDMW= 4, 5, ; forecasted probabilities
SPLIT= Total {beta6} car bus, ; Model structure
SPLITCOMP= 6, ; Forecast composite cost
STARTMW= 10 ; working matrices
;Note on SPLITCOMP : composite cost by mode
; Cij = - 1/beta*log(exp(-beta*CCar)+exp(-beta*CBus))
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT01C.PRN“, MSG='Example 6.2 - car, red and blue bus costs'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Costs_6_2_B.MAT", MO=1-3 NAME='Costs CAR' 'Costs BLUE BUS' 'Costs RED BUS'
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter6\Costs_6_2_B.DBF", PATTERN=IJM:V FIELDS=O D M COST
PAR ZONES=2
FILLMW MW[1]=MI.1.1(3)
ENDRUN
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT01D.PRN" MSG='Example 6.2 - Multimodal-split model using XCHOICE'
FILEI MATI[1] = "C:\Modelling Transport_CG\Output\Chapter6\Costs_6_2_B.MAT"
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Probabilities_6_2_B.MAT",
MO=5-8 Name='Car probability' 'Blue Bus probability' 'Red Bus probability' 'Composite cost kijn'
;costs
MW[1]=MI.1.1 ;car
MW[2]=MI.1.2 ;blue bus
MW[3]=MI.1.3 ;red bus ;demand
JLOOP
IF(I=1&&J=2) MW[4]=1 ;demand (just to calculate probabilities the demand is defined equal 1)
ENDJLOOP XCHOICE,
ALTERNATIVES=car, bbus, rbus, ; list choices
DEMANDMW = 4, ; input total demand
COSTSMW= 1, 2, 3, ; input costs
ODEMANDMW= 5, 6, 7, ; Forecasted probabilities
SPLIT= Total {beta6} car bbus rbus, ; Model structure
SPLITCOMP= 8, ; Forecast composite cost
STARTMW= 10 ; working matrices ;Note on SPLITCOMP : composite cost by mode
Ch6-MSM: Ex6.3 – Hierarchical Multinomial
Split
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT01A.PRN“, MSG='Example 6.2 - car and bus costs'
FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Costs_6_2_A.MAT", MO=1-2 NAME='Costs CAR' 'Costs BUS'
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter6\Costs_6_2_A.DBF", PATTERN=IJM:V FIELDS=O D M COST
PAR ZONES=2
FILLMW MW[1]=MI.1.1(2)
ENDRUN
Ch6-MSM: Ex6.3 – Hierarchical Multinomial
Split
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT03D.PRN“, MSG='Example 6.3 - Hierarchich Multimodal-split model using XCHOICE'
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT03C.MAT" FILEO MATO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Probabilities_6_3.MAT",
MO=5-9 Name='Car prob‘ 'Blue Bus prob' 'Red Bus prob' 'Composite cost top level' 'Composite cost PT level'
;costs
MW[1]=MI.1.1 ;car
MW[2]=MI.1.2 ;blue bus
MW[3]=MI.1.3 ;red bus ;demand
JLOOP
IF(I=1&&J=2) MW[4]=1 ;demand (just to calculate probabilities the demand is defined equal 1)
ENDJLOOP XCHOICE,
ALTERNATIVES=car, bbus, rbus, ;list choices top level
DEMANDMW = 4, ;input total demand
COSTSMW= 1, 2, 3, ;input costs
ODEMANDMW= 5, 6, 7, ;Forecasted probabilities
; Model structure
SPLIT= Total {lambda1} car pt, ;Top lebel nest
SPLITCOMP= 8, ;Forecast composite cost top level
SPLIT = PT {lambda2} bbus rbus, ;PT nest
SPLITCOMP= 9, ;Forecast composite cost PT level
STARTMW= 10 ;working matrices
;Note on SPLITCOMP : composite cost by mode ; CB = CBB - 1/(lambda2)*log2
ENDRUN
Ch6-MSM: Ex6.3 – Hierarchical Multinomial
Split
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT03A.PRN“, MSG='Example 6.3 - Summary Table - see page 216'
FILEI LOOKUPI[1] = "C:\Modelling Transport_CG\Input\Chapter6\Lambda1_values_6_3.dbf"
FILEO PRINTO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Probability_car_6_3.DAT", APPEND=T FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT03C.MAT"
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT03A.MAT",
MO=5-9 Name='Car prob' 'Blue Bus prob' 'Red Bus prob' 'Composite cost top level‘ 'Composite cost PT level' LOOKUP LOOKUPI=1,
NAME=Lambda1,
LOOKUP[1]=ITER, RESULT=LAMBDA1, FAIL[3]=0
; example of use: v=Lambda1_values_(1,25)
; look for 25 in the ITER field and returns the LAMBDA1 value ;costs
MW[1]=MI.1.1 ;car
MW[2]=MI.1.2 ;blue bus
MW[3]=MI.1.3 ;red bus
Value=Lambda1(1,@IDLOOP@)
;demand
JLOOP
IF(I=1&&J=2) MW[4]=1 ;demand (just to calculate probabilities the demand is defined equal 1)
Ch6-MSM: Ex6.3 – Hierarchical Multinomial
Split
; SCRIPT CONTINUED FROM PREVIOUS PAGE
XCHOICE,
;list choices top level
ALTERNATIVES=car, bbus, rbus,
;input total demand
DEMANDMW = 4, ;input costs COSTSMW= 1, 2, 3, ; Forecated probabilites ODEMANDMW= 5, 6, 7, ; Model structure
; Top lebel nest
SPLIT= Total Value car pt,
; Forecast composite cost top level
SPLITCOMP= 8,
; PT nest
SPLIT = PT {lambda2} bbus rbus,
; Forecast composite cost PT level
SPLITCOMP= 9,
;working matrices
STARTMW= 10
;Note on SPLITCOMP : composite cost by mode ; CB = CBB - 1/(lambda2)*log2
JLOOP
IF(I=1&&J=2) PRINT PRINTO=1 CSV=F LIST='Lambda1: ', Value(5.3L), ' Car Probability: ',MW[5](5.3L) ENDJLOOP
ENDRUN
Ch6-MSM: Ex6.3 – Hierarchical Multinomial
Split
Ch6-MSM: Ex6.4 – Calibration of Binary
Logit
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04A.PRN“, MSG='Example 6.4 - Berkson-Theil trasformation'
FILEO RECO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04A.DBF", FIELDS=ZONEP Axis_x(10.2) Axis_Y(10.5)
FILEI RECI = "C:\Modelling Transport_CG\Input\Chapter6\Costs_and_choices_6_4.dbf" ZONEP=RI.ZONEP ;Zone pair
P1 =RI.P1 ;Observed probability for mode 1 (in percentage)
P2 =RI.P2 ;Observed probability for mode 2 (in percentage)
C1 =RI.C1 ;Cij1 'know' part of the generalized cost for mode 1
C2 =RI.C2 ;Cij2 'know' part of the generalized cost for mode 2 ;log[P1/(1-P1)]=log[P1/P2] depended variable
;C2 - C1 indipendent variable ;lambda - slope of the line ;lambda+delta - intercept
Axis_x = C2-C1
Axis_y=LN(P1/(100-P1)) WRITE RECO=1
ENDRUN
Ch6-MSM: Ex6.4 – Calibration of Binary
Logit
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04B.PRN“, MSG='Example 6.4 - Linear regression for lambda and delta'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter6\RegreCoeffAndStats_6_4.DBF", FIELDS=AVE_X(10.5) AVE_Y(10.5) A(10.5) B(10.5) R2(10.8) LAMBDA(10.5) DELTA(10.5) FILEI RECI = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04A.DBF"
ARRAY X=5 Y=5 Y_MOD=5 _Count=_Count+1 X[_Count]=RI.AXIS_X Y[_Count]=RI.AXIS_Y _SUm_X=X[_Count]+_SUM_X _SUM_Y=Y[_Count]+_SUM_Y IF(_Count=5) _AVERAGE_X=_SUM_X/_COUNT _AVERAGE_Y=_SUM_Y/_COUNT LOOP K=1,5 _SSX2=(X[K]-_AVERAGE_X)^2+_SSX2 _SSXY=(X[K]-_AVERAGE_X)*(Y[K]-_AVERAGE_Y)+_SSXY ENDLOOP
Ch6-MSM: Ex6.4 – Calibration of Binary
Logit
; SCRIPT CONTINUED FROM PREVIOUS PAGE
_A=_SSXY/_SSX2 ;this is lambda
_B=_AVERAGE_Y-_A*_AVERAGE_X ;this is lambda*delta
_DELTA=_B/_A LOOP K=1,5 Y_MOD[K]=_A*X[K]+_B _SSRESID=(Y_MOD[K]-_AVERAGE_Y)^2+_SSRESID _SSY2=(Y[K]-_AVERAGE_Y)^2+_SSY2 ENDLOOP _R2=_SSRESID/_SSY2 RO.R2=_R2 RO.A=_A RO.B=_B RO.AVE_X=_AVERAGE_X RO.AVE_Y=_AVERAGE_Y RO.LAMBDA=_A RO.DELTA =_DELTA WRITE RECO=1 ENDIF ENDRUN
Ch6-MSM: Ex6.4 – Calibration of Binary
Logit
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04D.PRN“, MSG='Example 6.4 - Read choices and costs as matrix'
FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04B.MAT", MO=1-4 NAME=C1 C2 P1 P2
FILEI MATI[1] = "C:\Modelling Transport_CG\Input\Chapter6\Costs_and_choices_6_4.dbf", PATTERN=IJM:V FIELDS=ORIG DEST 0 C1 C2 P1 P2
PAR ZONES=3
FILLMW MW[1]=MI.1.1(4)
ENDRUN
Ch6-MSM: Ex6.4 – Calibration of Binary
Logit
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04C.PRN“, MSG='Example 6.4 - Verify calibration using XCHOICE'
FILEI MATI[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04B.MAT" FILEO MATO[1] = "C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT04A.MAT",
MO=4-8 Name='Probabibilty 1' 'Probability 2' 'Composite cost kijn' 'Ril prob 1' 'Ril prob 2'
;costs MW[1]=MI.1.1 ;C1 MW[2]=MI.1.2 + 3.10978 ;C2 ;Rilevated choices MW[7]=MI.1.3 ;P1 MW[8]=MI.1.4 ;P2 ;demand JLOOP IF(MW[1]<>0)
MW[3]=1 ;demand (just to calculate probabilities the demand is defined equal 1)
ENDIF ENDJLOOP XCHOICE,
ALTERNATIVES=mode1, mode2, ;list choices
DEMANDMW = 3, ;input total demand
COSTSMW= 1, 2, ;input costs
ODEMANDMW= 4, 5, ;Forecast probabilities
SPLIT= Total 0.72105 mode1 mode2, ;Model structure
SPLITCOMP= 6, ;Forecast composite cost
STARTMW= 10 ;working matrices ;percentages
MW[4]=MW[4]*100 MW[5]=MW[5]*100
ENDRUN
Ch6-MSM: Ex6.4 – Calibration of Binary
Logit
Ch6-MSM: Ex6.5 – Direct Demand Models
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT05A.PRN“, MSG='Example 6.5 - Tij'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Tij_6_5_A.DBF", FIELDS=Tij(5.0) Tijd(5.0) PAR ZONES=1
;Inputs
t12=2 ;Time in hours
c12=1 ;Fare in dollars
q12=5 ;Service frequency in trips per day
_Alpha=-2 _Beta =-1 _Nu =0.8 ;model equation Tij=10000*t12^_Alpha*c12^_Beta*q12^_Nu Deltac12=0.2 Tijd=10000*t12^_Alpha*(c12*(1+Deltac12))^_Beta*q12^_Nu WRITE RECO=1 ENDRUN
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER6\E6MAT05B.PRN“, MSG='Example 6.5 - Plot feasible solutions'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter6\Feasible_solutions_6_5.DBF", FIELDS=DeltaQ(10.2) DeltaT(10.5)
PAR ZONES=1
;20values for variations on q and 10 values for variations on t ;Range between -1 and +1
LOOP K=1,41 DeltaQ=(-1+(K-1)*0.05) ;DeltaT=(-0.5+(K-1)*0.1) DeltaT=0.4*DeltaQ-0.1 WRITE RECO=1 ENDLOOP ENDRUN
Chapter 8: Specification and Estimation of
Discrete Choice Models
Ch8-MSM: Ex8.1 – Estimation of Models
from
Random Samples
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER8\E8MAT01A.PRN“, MSG='Example 8.1 - L(theta) for different values of theta'
FILEI DBI[1] = "C:\Modelling Transport_CG\Input\Chapter8\Choices_and_values_8_1.dbf"
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter8\Variation_L_theta_with_theta_8_1.DBF", FIELDS=THETA LTHETA(10.5) PAR ZONES=1 LOOP COUNT=1,100 _THETA=(COUNT-1)*0.025 LOOP K=1,DBI.1.NUMRECORDS C=DBIReadRecord(1,K) _CHOICE=DI.1.CHOICE X1Q =DI.1.X1Q X2Q =DI.1.X2Q IF(_CHOICE=1) _NUM_L=X1Q*_THETA+_NUM_L ELSE _NUM_L=X2Q*_THETA+_NUM_L ENDIF _DEN_L=_DEN_L+LN(EXP(X1Q*_THETA)+EXP(X2Q*_THETA)) _LTHETA=_NUM_L-_DEN_L ENDLOOP RO.THETA=_THETA RO.LTHETA=_LTHETA WRITE RECO=1 _DEN_L=0 _NUM_L=0 _LTHETA=0 ENDLOOP ENDRUN
Ch8-MSM: Ex8.11 – Stated Preference Data
and
Discrete Choice Modelling
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER8\E8MAT02A.PRN“, MSG='Example 8.11 - Travel time'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter8\Value_Trave_Time_8_11.DBF", FIELDS=LEVEL TRAVTIME DIFFERENCES Rating DifRating RatMinute
FILEI RECI = "C:\Modelling Transport_CG\Input\Chapter8\Alternatives_and_score_8_11.dbf", SORT=TravTime ARRAY TravTime=3,2 IF(RI.TravTime=25) LEVEL=1 IF(RI.TravTime=15) LEVEL=2 IF(RI.TravTime=35) LEVEL=3 TravTime[LEVEL][1]=RI.TravTime TravTime[LEVEL][2]=RI.Score + TravTime[LEVEL][2] _MEAN=TravTime[1][1] IF(I=0) LOOP K=1,3 RO.LEVEL=K RO.TRAVTIME=TravTime[K][1] RO.DIFFERENCES=TravTime[K][1]-TravTime[K-1][1] RO.Rating=TravTime[K][2]/3 RO.DifRating=(TravTime[K][2]-TravTime[K-1][2])/3 RO.RatMinute=(TravTime[K][2]-TravTime[K-1][2])/((TravTime[K][1]-TravTime[K-1][1])*3) WRITE RECO=1 ENDLOOP ENDIF ENDRUN
Ch8-MSM: Ex8.11 – Stated Preference and
Discrete Choice Models
RUN PGM=MATRIX PRNFILE="C:\Modelling Transport_CG\APPLICATIONS\CHAPTER8\E8MAT02B.PRN“, MSG='Example 8.11 - Fare Level'
FILEO RECO[1] = "C:\Modelling Transport_CG\Output\Chapter8\Value_Fare_8_11.DBF", FIELDS=LEVEL Fare DIFFERENCES Rating DifRating RatingperP
FILEI RECI = "C:\Modelling Transport_CG\Input\Chapter8\Alternatives_and_score_8_11.dbf", SORT=Fare ARRAY Fare=3,2 IF(RI.Fare=1.3) LEVEL=1 IF(RI.Fare=1) LEVEL=2 IF(RI.Fare=1.5) LEVEL=3 Fare[LEVEL][1]=RI.Fare Fare[LEVEL][2]=RI.Score + Fare[LEVEL][2] _MEAN=Fare[1][1] IF(I=0) LOOP K=1,3 RO.LEVEL=K RO.Fare=Fare[K][1] RO.DIFFERENCES=Fare[K][1]-Fare[K-1][1] RO.Rating=Fare[K][2]/3 RO.DifRating=(Fare[K][2]-Fare[K-1][2])/3 RO.RatingperP=(Fare[K][2]-Fare[K-1][2])/((Fare[K][1]-Fare[K-1][1])*3)
