The New Trend of Travel Demand The New Trend of Travel Demand ModelModel

Lessons learned from the New York Best Lessons learned from the New York Best Practice ModelPractice Model

Kuo-Ann ChiaoKuo-Ann ChiaoDirector of Technical ServicesDirector of Technical Services

New York Metropolitan Transportation CouncilNew York Metropolitan Transportation Council

Many years ago at NYMTC….. We used a Mainframe computer to run our travel demand model

The problem was..When there was a problem,

Even we took apart the computer,

OrDig a hole on the floor,

It was very difficult to find out what was the problem,AndWhere was the problem..

The old fashion computer print outs did not help us too much

NYBPM Study Area

• 20,000,000 population20,000,000 population• 100 population segments100 population segments• 4,000 Transportation Analysis Zones 4,000 Transportation Analysis Zones • 4 time periods4 time periods• 6 trip purposes6 trip purposes• 10 motorized modes10 motorized modes• 4 urban types4 urban types

Location Distribution 1997 Household Travel Survey

A joint project between NYMTC and NJTPA

Location-based 11,000

households 28,000 people 118,000 trips

Uni-directional coding & Ramps

Highway Network

Very large network (52,794 links in 28 county 3 state NY metro area)

4,950 High-level facilities26,385 Arterials10,694 Centroid and external connectors10,765 Other

Unidirectional / dualized codingConflated the network geographyGIS Street Network – TIGER (or LION) Developed in TransCAD SoftwareSOV, HOV2, HOV3+, taxi, truck, other commercial Classified by 21 Primary Link Types for capacities, initial speeds and VDF’s

Link Attributes

Zones System–Census Tract Based

BPM zone boundaries

Transit NetworkTransit Network

Extremely detailed transit coding based on information from MTA and NJ TransitDeveloped in TransCAD 4.0 Each route variation coded as a distinct route:

100 NYC subway routes900 Commuter rail routes2,300 bus routes73,000 transit stops.50 ferry routesIncludes sidewalk network in Manhattan Walk access/egress linksPark - and - Ride

Transit Network

Highlights of NYBPMHighlights of NYBPM

Micro-Simulation choice modelsPopulation synthesis and intra-household travel interactionsJourney-based travel units modeledNon-motorized (pre-mode choice)Mode-Destination Choice (nested logit)Stop frequency and location sub-modelFull multi-modal analysis / assignment

Route-Deviation ConceptRoute-Deviation Concept

Someone give me an example of how do you come to the office today.

Route-Deviation ConceptRoute-Deviation Concept

Origini

Destinjdij

Stopkdik dkj

A journey reflects the real travel characteristics It also reduces the number of trip purposes needed

Trip

JourneJourneyy

General Modeling StructureGeneral Modeling Structure

Journey Generation

Mode & Destination

Time of Day

Assignment

Mic

ro-S

imula

tion

Journey GenerationJourney Generation

Journey Generation

Mode & Destination

Time of Day

Assignment

Synthetic Population

Auto Ownership

Journey Frequency

Socio-Economic Targets

Accessibility

Seed PUMS

LUM

5-Percent Census Public Use Microdata Sample (PUMS) Files

WorkersNon-

WorkersChildren

Man

dato

ryM

aint

enan

ceD

iscr

etio

nary

Work

School

At Work

M

M

M

SchoolSchoolUniversity University

Intra-Household Interaction

Indi

vidu

al T

ime-

Spa

ce C

onst

rain

t

D

D

D

Journey Frequency ModelJourney Frequency Model

Mode & DestinationMode & Destination

Journey Generation

Mode & Destination

Time of Day

Assignment

Pre-Mode

Mot.Dest.

Stop Frequency

Lan

d U

se A

ttra

ctors

LOS

Skim

s

Density

NM Dest.

Mode

Stop Location

Pre-mode Choice: Nested StructurePre-mode Choice: Nested Structure

Non-motorizedMode

MotorizedMode

Destination Choice

Drive AloneTransit/

Shared RideTaxi

Purpose-specific

attractions

Density of attractions

Mode & Destination ChoiceMode & Destination Choice

Pre-Mode Choice

DestinationDestination

Stop Frequency

Motorized

DetailedSub-Mode

ChainUtility

Non-Motorized

Mode

Stop Location

Non-Motor

Mode

Stop-Density

Accessibility

TotalActivityControl

Stop Frequency by PurposeStop Frequency by Purpose

0%10%20%30%40%50%60%70%80%90%

Work-low

Work-med

Work-high

School Univ Atwork

Maint Discr

No stops Outbound Return Both

Stop Frequency by ModeStop Frequency by Mode

0%

10%

20%

30%

40%

50%

60%

70%

80%

Drivealone

Sharedride

Transit Commutrail

Taxi Schoolbus

Other

No stops Outbound Return Both

Stop Distribution by DurationStop Distribution by Duration

0%

10%

20%

30%

40%

50%

60%

70%

80%

< 1 h 1-2 h 2-3 h 3-4 h 4-5 h > 5 h

Activity duration, hours

Mode & Destination ChoiceMode & Destination Choice

Pre-Mode Choice

DestinationDestination

Stop Frequency

Motorized

DetailedSub-Mode

ChainUtility

Non-Motorized

Mode

Stop Location

Non-Motor

Mode

Stop-Density

Accessibility

TotalActivityControl

Stop-Frequency Choice Model

Choice Alternatives Structural Dimensions Utility Components

0 -

No

sto

ps

1 -

Ou

tbo

un

d

2 -

Ret

urn

3 -

Bo

th

Work

School

University

Maintenance

Discretionary

. . . . . . . . .

Journey Purpose

Person Type

WorkSchool

UniversityMaintenance

Discretionary

At Work

Worker

Non-Worker

Child

Income

Car Sufficiency

Mode

SOV, Taxi HOV

Transit

Journey Distance

Stop-Location(Density) Log-Sum

HouseholdComposition

Other JourneysJourney Purpose

At Work

Stop-Location Choice Model

Choice Alternatives Structural Dimensions Utility Components

5 miles

5 miles

20%

Journey Purpose

Person Type

WorkSchool

University

Maintenance

Discretionary

Worker

Non-Worker

Child

Mode

SOV, Taxi HOV

Transit

Journey Leg

Outbound Return

Stop Density (Size)

CombinedImpedance

Route Deviation

Stop Activity

WorkSchool

University

Maintenance

Discretionary

Time of DayTime of Day

Journey Generation

Mode & Destination

Time of Day

Assignment

Stage 1:

Journey Split by Legs and Periods

Stage 2:

(Current)Journey Split by Trips and Periods LOS Skims

Predetermined Set of TOD Distributions

Stage 3:

TOD Choice Model

Timing & Durational

Utility

calibrationn : the act of checking or adjusting (by comparison with a standard) the accuracy of an estimated coefficients.

validationn : the act of finding or testing the truth of something

Stages of CalibrationStages of Calibrationand Validation Sourcesand Validation Sources

Disaggregate Calibration

by PurposeAggregate Calibration

Of Destination Choice

Aggregate Calibration

Of Mode Shares

Highway and Transit

Assignment

Household Survey

Household Survey; PUMS

Household Survey; PUMS

Traffic Counts; Screenline Database; MATRIX; HPMS

Fractional ProbabilityFractional Probability

Tour

Destination 1 (0.15)

Destination 2 (0.75)

Destination 3 (0.10)

Mode 1 (0.05)

Mode 2 (0.03)

Mode 3 (0.07)

Mode 1 (0.15)

Mode 2 (0.25)

Mode 3 (0.35)

Mode 1 (0.05)

Mode 2 (0.02)

Mode 3 (0.03)

Micro-SimulationMicro-Simulation

Tour

Destination 1 (0.15)

Destination 2

Destination 3 (0.10)

Mode 1 (0.15)

Mode 2 (0.25)

Mode 3

X

X

XX

Aspects of Micro-Simulation Aspects of Micro-Simulation for NYBPM Processingfor NYBPM Processing

Nearly 9 million households in base yearJourney productions file over 500 Meg

Mode destination choice stops model processes over 25 million paired journeys by 8 trip purposes

Output files over 300 Meg

6 highway classes and 4 transit trip tables for each of 4 time periods

Combined file size about 2.5 Gig

Hardware: 4 GB RAM / Dual Processor / 1.5 Ghz / 120+ GB Hard Drive

Stratum = 1,2 ... 100

Core Destination & Mode Choice Probability

Destination

Ori

gin Mode = 1O = 1 Mode = 2 ... Mode = 10

O = 2 ...

O = 4000

D = 1 D = 4000D = 2 ...

Conventional FractionalProbability Array

Micro-Simulation

(0=4000)*(D=4000)*(M=10)*(S=100) =

16,000,000,000

20,000,000 Stratified Individuals

23,000,000 Origin-Based Journeys

23,000,000 Destination Choices

23,000,000 OD-Based Mode Choices

Dimension of Choice Probability in Dimension of Choice Probability in NYBPMNYBPM

Processing Time For BPM Model RunProcessing Time For BPM Model Run

STEP BPM PROCEDURE HOURS

1 CREATE NEW SCENARIO 10 min.

2 RUN HIGHWAY NETWORK BUILDER 15 min. 5 min.

3 NETPREP .20 min. 4 min.

4 HIGHWAY PRESKIMS 12 hrs. 5 hrs 30 min.

5 TRANSIT NETWORK DATABASE & SKIMS 48 hrs. 12 hrs 6 min.

6 ACCESSIBILITY INDICIES 2 hrs. 26 min.

7 HOUSEHOLD AUTO JOURNEY (HAJ) 1 hrs. 4 min.

8 MODE DESTINATION STOPS CHOICE (MDSC) 18 hrs. 9 hrs 45 min.

9 TRUCKS/COMMERCIAL VEHICLES MODEL 2 hrs.

10 EXTERNAL MODEL 5 min.

11 PRE-ASSIGNMENT PROCESSING/TIME OF DAY (PAP) 1 hrs.

12 HIGHWAY ASSIGNMENT 16 hrs.

13 TRANSIT ASSIGNMENT 72 hrs. 42 hrs 40 min.

TOTAL              173 hrs 78 hrs(>7 days) (3 days)

6 hrs 43 min.

current improvements

Status of On-Going ImprovementsSpeed up the running time

Software EngineeringMemory Handling

allocated the memory only once, using a flag to determine if the memory had already been allocated

memory could be allocated in one block

Input/Output

Remove messages (one per 33 million lines in the HAJ trip file) to the screen, reduced processing time from 22 minutes to 20 seconds

Parameter Passing

Passing information of a pointer to a structure rather than an entire structure (e.g., the memory used to call about 260,000 times of one function with 92 bytes could be reduced significantly by passing a pointer to the structure that only requires 4 bytes)

In-lining Function Calls

Very short functions that are called frequently can cause bottlenecks (function consists of just a few lines (e.g., Calling a function, which was being called between 300,000 to 600,000 times, was taking up 10% of the total program time. In-lining the function reduced it to 0.3% of the total program time)

Additional optimization

Hardware optimization

BPM Structure –“GUI” for User Documentation

Applications of BPM at NYMTCApplications of BPM at NYMTC

Conformity Analysis

Regional Transportation Plan

Congestion Management Systems

Testing Scenarios for emission reduction strategies

Request for Data Manipulation and Runs from other agencies

Applications of BPM .. ProjectsApplications of BPM .. Projects

Tappan Zee BridgeGowanus ExpresswayBronx Arterial NeedsBruckner Sheriden ExpresswayLong Island East Side StudyCanal Area Transportation StudyLower Manhattan Development CorporationSouthern Brooklyn Transportation StudyRegional Freight Plan StudyHackensack Meadowland Development Corp.

Model UpdateModel Update

Study of Post 9/11 Travel Pattern Changes New Set of Socioeconomic and Demographic ForecastsCollection of 2002 traffic and transit dataUpdated 2002 base year Model

Model Improvements

Better Highway -Transit Connection

Improve transit models

Integrate BPM with the Land Use Model

Web Applications

Model output analysis

Model runs

Distributed Process

Better GUI (flowchart-based, on-line help & document)

More project applications

BPM User’s Group Support & Meetings

Advisory CommitteeAdvisory Committee

Patrick T. Decorla-Souza, Federal Highway Administration

Frederick W. Ducca, Federal Highway Administration

Ron Jensen-Fisher, Federal Transit Administration

Elaine Murakami, Federal Highway Administration

Bruce Spear, Federal Highway Administration

John Thomas, Environmental Protection Agency

T. Keith Lawton, Metro, Portland, Oregon

Charles Purvis, Metropolitan Transportation Commission

David Zavattero, Chicago Area Transportation Study

Arnim H. Meyburg, Cornell University

The Late Eric Paas, Duke University

Frank Spielberg, S. G. Associates

That’s it for today Thanks