san francisco’s dynamic traffic assignment model

SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

San Francisco’s Dynamic Traffic Assignment Model

Background

SFCTA DTA Model Peer Review Panel MeetingJuly 25th, 2012


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Outline

• Project Background• Modeling tool suite

• SF-CHAMP 4.3.0 Fury• Previous DTA work


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SF-CHAMP

• Simulates the travel pattern of every Bay Area Resident based on:• Demographics• Land use• Transportation Network

SF-CHAMP Model Basics 4

SF-CHAMP

HOME

WORK

INTERMEDIATE STOP ON WAY TO WORK

WORK-BASEDDESTINATION

HOME-BASED TOURDESTINATION

1

7

6

5

4 3

2

= Tour

= Trip

Number indicates trip order

PRIMARY TOUR:Home-based Work

WORK-BASEDSUB-TOUR

SECONDARY HOME-BASED

TOUR

Primary destinations vs.Intermediate stops

Consequences of choices: Do you have a car available? Did you leave the car at home? Do you have a complicated day?

A tour is an entire chain of trips: from your primary origin, to all of your destinations, and then back again.


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SF-CHAMP Data

• Most models BATS 1996• New estimations (BATS 2000):

• Mode choice • Auto Availability

• New calibration (ACS)• Workplace Location Choice• Auto Availability• Tour mode choice

• New validation • 2010 APCs and Ridership• Recent Traffic Counts


CHAMP SEQUENCING – PART 1

Workplace locationVehicle Availability:

Accessibility of home Accessibility of work Accessibility between home and work

Tours Generated Accessibility of home Accessibility of work Accessibility between home and work

Initially Schedule ToursPrimary destinations for non-work tours

Initial tour schedule Accessibility


CHAMP SEQUENCING – PART 2

Tour scheduling Accessibility by time of day for chosen

destinationTour mode

Accessibility to destinations for that time of day by mode

Choose intermediate stops along tours (tours become trips)

Trip mode choice Cost Travel Time

Assign trips by mode to specific transit lines and highway paths Cost Travel Time


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SF-CHAMP – in Flow Chart Form


Trips are aggregated into “zones”

981 zones in San Francisco

1,275 in other Bay Area counties

Spatial Detail – Analysis Zones


Spatial Detail - Transit

Every transit stop

Every transit line

Every street


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SF-CHAMP – Roadway validation

EA AM MD PM EV0

5,000

10,000

15,000

20,000

25,000 Golden Gate Bridge - Southbound

Observed - SB Estimated - SBWeekday Time of Day

Flow

EA AM MD PM EV0

5,000

10,000

15,000

20,000

25,000 Golden Gate Bridge - Northbound

Observed - NB Estimated - NBWeekday Time of Day

Flow


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EA AM MD PM EV0

10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000

SF/SM County Line - Northbound

Observed - NB Estimated - NBWeekday Time of Day

Flow

EA AM MD PM EV0

10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000

100,000 SF/SM County Line - Southbound

Observed - SB Estimated - SBWeekday Time of Day

Flow


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EA AM MD PM EV0

10,000 20,000 30,000 40,000 50,000 60,000 70,000

Oakland Bay Bridge - Eastbound

Observed - EB Estimated - EBWeekday Time of Day

Flow

EA AM MD PM EV0

10,000 20,000 30,000 40,000 50,000 60,000 70,000

Oakland Bay Bridge - Westbound

Observed - WB Estimated - WBWeekday Time of Day

Flow


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Time of DayArea Type EA AM MD PM EV 24HR

0/1/2 1.330 0.969 1.041 1.035 1.009 1.0303 1.289 1.051 1.169 1.050 1.163 1.128

4/5 1.281 1.104 1.235 1.084 1.289 1.191All Locations 1.303 1.035 1.135 1.054 1.124 1.105

Time of DayVolume Classification EA AM MD PM EV 24HR

<40k 1.385 1.085 1.178 1.084 1.120 1.13540-80k 1.309 1.092 1.200 1.015 1.181 1.140>=80k 1.288 0.982 1.079 1.082 1.088 1.073

All Locations 1.303 1.035 1.135 1.054 1.124 1.105

Time of DayVolume Classification EA AM MD PM EV 24HR

<40k 0.218 0.090 0.116 0.077 0.143 0.10740-80k 0.097 0.050 0.067 0.045 0.083 0.057>=80k 0.129 0.048 0.047 0.069 0.067 0.050

All Locations 0.080 0.032 0.036 0.037 0.047 0.035

Estimated/Observed

Estimated/Observed

%RMSE


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SF-CHAMP – Transit validation

0 5,0

00

10,00

0

15,00

0

20,00

0

25,00

0

30,00

0

35,00

0

40,00

0

45,00

0

50,00

0 0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Daily Muni BoardingsModeled vs Observed

Muni BusesMuni Cable CarsMuni LRTy=x

Observed Boardings

Mod

eled

Boa

rdin

gs

An additional tool in the toolbox - DTA


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SF-CHAMP

Dynamic TrafficAssignment

TrafficMicrosimulation

Time-dependent user equilibriumwith realistic, but simplified vehicle simulation

Regional static user equilibriumactivity-based model

Highly realistic simulation of vehicle behavior and interactions

Northwest quadrant DTA network


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200 Internal Zones60 External Zones3,000 Nodes7,000 Links240 Signals83 Transit Lines

How have we used DTA?

• Presidio Parkway Construction• Where do vehicles re-route when

intra-SF ramps are taken away on Doyle Drive?


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How have we used DTA?

• Geary Bus Rapid Transit• Where do vehicles re-route when we

take a lane away from Geary?• How do diversions affect other

streets?


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20

25th

24th

23rd

22nd

21st

20th

19th

18th

17th

16th

15th

14th

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11th

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-500

-400

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0

100

200

300

400

500

-2,500

-2,000

-1,500

-1,000

-500

0

500

1,000

1,500

2,000

2,500

2008 No Project - Geary Blvd - Westbound - Model Simulation

WB RT Off WB LT Off SB RT On NB LT On WB Through WB Mainline CountsWB Turn Counts - Mainline

Turn

Vol

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Mai

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Geary BRT DTA – Diversions (draft)

Issues that arose from current DTA use

• DTA represents ideal world network LOS knowledge• Drastic shifts away from main-line

roadways when their capacity reduced

• Hides potential congestion on Geary• Subarea reliant on demand from

external stations• Validation of individual turn movements

important• “Surgical Adjustments” still required• Reliant on centroid-connector

placement• Conflicting validation data


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San Francisco’s Dynamic Traffic Assignment Model

Model Development Process

SFCTA DTA Model Peer Review Panel MeetingJuly 25th, 2012


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Outline - Model Development Process

• Objectives for now versus the future• Overall Approach• DTA Package Selection• Project Tasks and Schedule


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DTA Model Development Objectives (for now)

• Have a working DTA model with results that make sense for the PM Peak period in San Francisco

• Have seamless process from SF-CHAMP to DTA results:• Little human intervention• Reduce human error• Use SF-CHAMP demand directly

• Behaviorally consistent• Allow SF-CHAMP to take advantage of

all fixes• Test having SF-CHAMP use DTA as it’s

network model


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Left for “the Future”

• Rigorous validation for specific subareas

• Reconfiguring centroid loadings to be at parking

• Trip chaining and tours• Loading miiples from SF-CHAMP with

their individual characteristics• Influence of non-motorized travel on

traffic flow• 24 hour model; bigger subarea?• Assigning people to transit trips

• The future is now (FAST-TrIPs)


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DTA Model Development Approach

• Write code when possible for repeated human tasks :• Data format conversion• Network manipulation and validation

for DTA• Output validation and visualization

• Develop in an open source environment• Don’t re-write code that exists in our

DTA package• Make a counts database that interacts

with our network (CountDracula)


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DTA Model Development Approach

• Use as much ‘real’ data as possible• Signal timings• Stop signs• Transit • Traffic flow parameters

• Fix all issues “at the source” if possible• Network geometry• Turn prohibitions• Demand (SF-CHAMP)


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DTA Package Selection

• Evaluated alternatives that we knew about

• Selected Dynameq because:• Mature UI• Developer responsive with

development pathway• Detailed network representation:

• Lane-based delay• Transit vehicles and schedules explicit

• Was able to run• Results made sense when we tested

it


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Tasks and Schedule

Project ManagementModel Development

Tools Development

Application/Integration

Evaluation/Reporting

2011 2012A S O N D J F M A M J J A S O

Demand PreparationNetwork Preparation

Data PreparationModel Calibration

Network ConversionDemand Conversion

Signal ConversionTransit Conversion

Summary

Integration MemoApplications

Peer ReviewWebinar

san francisco’s dynamic traffic assignment model

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