October 2014

Implementing the Sydney Strategic Transport Model (STM) in Emme 4

Dr Peter Hidas

Bureau of Transport Statistics, Transport for NSW

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

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

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

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

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)

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

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)

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)

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

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

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?

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

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

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

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

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

Car Access to Rail: Station Choice

?

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 )

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

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

=

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

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)

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?

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

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

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