october 2014 implementing the sydney strategic transport model (stm) in emme 4 dr peter hidas bureau...
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October 2014
Implementing the Sydney Strategic Transport Model (STM) in Emme 4
Dr Peter Hidas
Bureau of Transport Statistics, Transport for NSW
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Acknowledgements Team Effort Model Development & Estimation
RAND Europe Frank Milthorpe, BTS
Implementation in Emme4 and Python Peter Hidas and Ting Yu
Validation, testing The BTS Modelling Team
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History of STM
• First developed in 1970s• Major Updates 1986 & 1994/95• Redesigned in late 1990s
– Known as STM2– Still in use
• Extended and re-estimated in 2012-14– Known as STM3– Implemented – validation in progress
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Study Area – Sydney GMA• 2006 zoning (24,443 km2):
– 2715 Travel Zones– 25,000 Nodes– 90,000 Links– 1,350 Transit Lines– 445 Stations
• Rail, LR, Ferry
• 2011 zoning (31,407 km2)
– + 400 zones
Sydney
Newcastle
Wollongong
400 km250 mi
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Key Features of STM2
• Tour based model system– Home to Primary destination and return
• Disaggregate model– Person and household segmentation– Forecasting of licence holdings– Forecasting of car ownership– Segmentation: Population Synthesiser
• Joint mode-destination choice models– Nested/Multinomial Logit choice
• Demand is not constant– Tour frequency depends on accessibility
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STM3 Enhancements• Use most recent travel survey data (HTS)• Model base year: 2011• Explicit modelling of toll roads
– Toll-users, non-toll-users separate modes– (STM2: only one car driver option)
• Explicit modelling of access mode to rail– Park-&-Ride, Kiss-&-Ride, Bus, Walk– (STM2: only bus and walk)
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STM3 Travel Purposes• Home based purposes (to primary destination)
– Work– Business– Education
• Primary, Secondary, Tertiary
– Shopping– Other
• Non-Home based purposes– Work based business– Business detours as part of work tour
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STM3 Travel Modes
• Modes– Car Driver
• Toll users, Non-toll users
– Car Passenger– Train (includes Light Rail & Ferry)
• Park-&-Ride, Kiss-&-Ride, Walk, Bus
– Bus– Walk– Bicycle– Taxi
• (Crowding on PT not modelled)
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Segmentation• Extensive segmentation
– Different by purpose– Additional segments for frequency model
• Home Based work– Mode Destination Models
• Car availability/Licence holding (8 segments)• Work status (full time, part time)• Income (5 segments)
– Frequency Models• Age (3 segments)• Adult status (1/5 segments)
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Number of SegmentsPurpose Mode
DestinationAdditional
Frequency Total
Work 80 3/15 720
Business 24 24 576
Primary Education 10 4 40
Secondary Education 3 2 6
Tertiary Education 12 12 144
Shopping 36 36 1296
Other 25 56 1400
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STM3 Values of Time
• Values of Time Vary– Personal Income– Journey Purpose– Mode of Travel
• Use log and linear cost terms– Better fit for demand estimation– More difficult for economic benefit calculation
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STM3 - Implementation
• Model Development & Estimation– ALOGIT software (RAND Europe)– No direct linkage to Emme
• STM2 – Implemented using Emme macros
• STM3 – Emme 4 available– How best to utilise?
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STM3 - Software Platform
• Combination of Multiple Tools• Emme-4 API + Python• Python 2.7 64-bit
– Needed for memory requirements (min 20 Gb)
• Numpy – Efficient matrix operations library
• Cpython– Python library written in C (faster)– For some special methods (sorting)
• Run from DOS Batch file
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STM-3 Model Structure
Input Data Create Skims
Estimate Travel Frequency
Mode-Destination Choice
Create new CAR LOS Skims
Final Car/PT assignments
Emme-4 Emme-4 API
Python
Python
Emme-4 API
Emme-4 API
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STM3 Model Processes in Python
• Tour Frequency & Mode-Destination Choice– 7 HB + 2 NHB trip purposes – separate models– 6 purposes include car access to rail– Models are similar but many differences
• OOP structure– Shared code in base classes– Differences in derived classes
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CarSkims Hierarchy
cCarSkims_ZZ
cCarAccSkims_ZS
cCarNoTollSkims_ZZ
cCarTollSkims_ZZ
cHWcarNoTollSkims_ZZ
cHWcarAccSkims_ZS
cHWcarTollSkims_ZZ
Basic propertiesabstract class
not for usecommon methodsfor all sub-classes
Input Matricesabstract class
not for useDifferent by PurposeSpecific properties
daily averages
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Matrices in Emme vs Python
TZ to TZTZ to Stn
Stn to Stn
Stn to TZ
Ext
ern
al Z
on
es
External Zones
Emme: Full matrix Travel Zones Station Zones External Zones
Python: ZZ: TZ to TZ ZS: TZ to SZ SZ: SZ to TZ Freq/Mode-Dest. Models: ZZ Station choice: ZS + SZ
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Car Access to Rail: Station Choice
?
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Station Choice
• For each OD-pair– Calculate utility (car + rail) through each station– Select N (2-5) “best” stations
• By OD-pair (7.3 million)• Select from 450 stations• Run time
– If done by single OD-pair: ~ 9 hours– 3D matrix calc: from 1 O to all D: ~ 3 hours– 3D + Cpython partsort method: ~ 20 min– ( ALOGIT: ~ 3 days )
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Calculate Car Access to Rail utility Gen.time from O to D =
Car time from O to S
+ Rail time from S to D Must be ZZ-matrix
Car time
stations
z o
n e
s
Gen. timeRail time
stat
ion
s
z o n e s
z o
n e
s
z o n e s
+ =
Selected Station
z o
n e
s
z o n e s
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Calculate Utility – the easy way
For each OD-pair: get S from stations matrix Gt(OD) = Ct(OS) + Rt(SZ) 7.3 million OD-pairs!
Car time
stations
z o
n e
s
Gen. timeRail time
stat
ion
s z o n e s
z o
n e
s
z o n e s
Selected Station
z o
n e
s
z o n e s
+Rt
O
S
O
S
CtD
OO S
D
O
O
D
Gt
=
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Ct Ct Ct Ct Ct Ct
Calculate Utility – a faster way
Process Select all OD-pairs that use S
(mask) Get car times from All-O to S
re-shape vector to 2D (ZZ) Get rail times from S to All-D
re-shape vector to 2D (ZZ) Add the two matrices = Gt Repeat for each station S
Max 450 iterations! Masking, re-shape are
standard methods in Numpy This process is applied at
several places
Car time
stations
z o
n e
s
Rail time
stat
ion
s
Selected Stations
z o
n e
s
z o n e s
RtSCt
S
=
S
S
S
S
S
Gen. Time
z o
n e
s
z o n e s
GtGt
Gt
Gt
Gt
Gt
+
Rt
Rt
Rt
Rt
Rt
Rt
Rt
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Current Status
• STM3 model coded and tested• Validation:
– ALOGIT vs Emme/Python – finished – Comparison with Observed (HTS) data
• Key issues for further improvement– Run time– Use for PT Project Model (PTPM)
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Model Run Time• STM2:
– ~ 19 hours (macros, without car access to rail!)
• STM3:– Full Model (4-cycles): ~ 17 hours– One-cycle: ~ 7 hours
• New zoning system TZ11: +400 zones– Further increase in run time
• How to reduce?– Multi-threading?
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STM-3 Current Model StructureInput Data
Hb-OtherFinal Car/PT assignments
Create CAR LOS Skims
Hb-SecEd
Hb-PrimEd
Hb-Business
Hb-Work
Hb-Shop
Hb-TerEd
NHb-Work
NH-Business
Collate new CAR Demand
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STM-3 Parallel Model StructureInput Data
Hb-Other
Final Car/PT assignments
Create CAR LOS Skims
Hb-SecEd
Hb-PrimEdHb-BusinessHb-Work Hb-Shop
Hb-TerEdNHb-Work NH-Business
Collate new CAR Demand
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Summary• STM3 Implemented • Emme 4 API: major benefits
– Easy, user-friendly, powerful methods– Easy to combine with external code– New methods faster than macros
• Python, NumPy: major benefits– User-friendly, powerful, fast methods
• Run time less than for STM2 but still very long• Next challenges
– Improve run time– Implement Peak Spreading