integration of dynamic traffic assignment in a four-step model framework – a deployment case study...

Integration of Dynamic Traffic Assignment in a Four-Step Model Framework –

A Deployment Case Study in PSRC Model

13TH TRB National Transportation Planning Applications Conference

By:

Robert Tung, PhD

With:

Yi-Chang Chiu, PhD (U of Arizona)

Sarah Sun (FHWA)

WSDOT

PSRC

Motives

• Static trip based macro model is limited in solving modern transportation issues.

• Activity Based Model (ABM) is promising by may be costly to implement.

• DTA tools are increasingly sophisticate and efficient in handling large multimodal network.

• Combination of 4-Step model and DTA is potentially a Low-Hanging Fruit & cost-effective approach to add temporal dynamics to static trip based models.

Tung & Chiu : Integration of DTA in a 4-Step Model Framework 2

Objectives

• Implement a full DTA feedback mechanism in a static 4-step trip based model framework (PSRC)

• Document the findings and issues learned from the process.

• Focus on network development, calibration and validation, scenario analysis, and computing resources.

• Deriving insights from comparing the proposed DTA-embedded approach with the existing method.

• Understand the cost and benefit of integrating DTA in the 4-step process.

3Tung & Chiu : Integration of DTA in a 4-Step Model Framework

Multi-Resolution Modeling (MRM)


MACRO MICRO

MESO•Static/Instantaneous Paths•Region Wide•Zonal Trips•Analytical Equilibrium•Demand Driven•Planning/Forecasting

•Static Paths•Corridor/Intersection•Individual Vehicles•Simulation One-Shot•Supply Driven•Operational

•Dynamic/Time Varying Paths•Subarea / Corridor•Vehicle Platoons

•Simulation Equilibrium•Supply Driven•Planning/Operational

O/D

DTA

MRM Issues


Macro-Micro Approach:Pros:

• Widely used in practice. Many tools are available.Cons:

• Macro demand are not consistent with micro network.• No temporal dynamics on demand slices.• No feedback.

Macro-Meso-Micro Approach:Pros:

• Meso demand are more consistent with micro network.• Demand reflect temporal dynamics.

Cons:• Learning curve for planners.• Require more computing resource.• Mostly auto only.• No feedback.

DTA Primer


STA DTA MICRO

Loading Analytical Meso Sim Micro Sim

Shortest Path Instantaneous Time Dependent Instantaneous

Route Choice FW/OBA/TAPAS GFV Logit/MSA

Connectivity Link Link/Lane Lane/Turn

Resolution Hour Minute Second

Solution UE DUE Non-UE

Convergence Unique Non-Unique Non-Unique

Speed Static Average Time Varying Time Varying

Flow Model VDF Speed-Density Car Following

Arrival Time Profile No Yes Yes

DTA Integration in PSRC


Land Use

Trip Generation

Trip Distribution

Modal Choice

Time of Day

Trip AssignmentDTA Auto

Skims

DTA Integration Concept


Land Use

Generation

Distribution

Modal Choice

Assignment

Land Use

Generation

Distribution

Modal Choice

DTA

Task Outline

• Network Conversion & Enhancement• Intersection Controls• Time-of-Day Model and 24-Hour Demand• Interface between DTA and TDM• 24-Hour Continuous DTA Simulation &

Assignment• Calibration and Validation• Scenario Analysis (HOT, Tolling, Work Zone)


Network Conversion


Centroids:• From single point to multi-point loading• Use arterial links as trip generation and apply

loading weights• Use standard nodes as trip destination

Links/Nodes:• Maintain realistic connectivity and GIS shape• Nodal orientation is important

Controls:• Use actuated signals as default if real data are not

available• Use reasonable max and min green times

Demand Conversion


Period SOV HOV Truck Total

AM 983,292 176,292 104,580 1,264,164

Mid-day 1,721,472 536,416 201,042 2,458,930

PM 1,124,537 382,502 116,784 1,623,823

Evening 888,251 410,576 57,164 1,355,991

Night 490,499 105,715 44,203 640,417

Daily 5,208,051 1,611,501 523,773 7,343,325

0:001:30

3:004:30

6:007:30

9:0010:30

12:0013:30

15:0016:30

18:0019:30

21:0022:30

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

0.0600

SOVHOV

• Use temporal (departure) profile derived from survey or TDM with directionality and peaking characteristics retained

• Assemble 24-hour demand from time varying period O-D tables

• Use smaller time interval as possible (15-minute)• Separate demand by mode and purpose

PSRC 2006 Diurnal Profile

PSRC 2006 Auto Demand by Period

DynusT


• Simple , lean and easy to integrate with macro and micro models

• Developed since 2002, tested (in test) for 20 regions since 2005

• Used in several national projects• Memory efficient

• Capable of large-Scale multimodal 24-hr simulation assignment

• Fast simulation/computation• Multi-threaded

• Realistic microlike mesoscopic traffic simulation• Anisotropic Mesoscopic Simulation (AMS)

• Managed Open Source in 2010/2011

DynusT Algorithmic Structure


Method of Isochronal Vehicle Assignment

Epoch k Traffic Assignment

Time-Dependent Shortest-Path Algorithm

Gap Function Vehicle Based TrafficAssignment Algorithm

k = k + 1

Stop

All Epochs Assigned?No

Assignment Converged? No

Iteration n Traffic Simulation

Generated Vehicles with AssignedAttributes

Anisotropic Mesoscopic Simulation (AMS)

Information Strategy Initial Path

Time-dependent OD, networkInitial/Intermediate Vehicle Paths

Model MoEsEvacuation Time, Exposure Level, Casualty, etc.

n = n + 1

Yes

TD O-D

TD Network

AMS Simulation

TD SP

Assignment

Convergence

Anisotropic Mesoscopic Simulation (AMS)


• Stimulus-response model

• Net influence for speed adjustment primarily comes from traffic in the front (SIR)

• Can define different “average traffic conditions” to model uninterrupted and interrupted flow conditions

Uninterrupted Flow

Interrupted Flow

right lane

Vehicle i

Speed Influencing Region SIRi

l

left lane

right lane

Vehicle i

Speed Influencing Region SIRi

l

AMS q-k-v Curves


1 20 40 60 80100

120140

160180

200220

240260

010203040506070

Speed Density Curve

Density (k)

Spee

d (v

)

1 20 40 60 80100

120140

160180

200220

240260

0

500

1000

1500

2000

2500

Flow Density Curve

Density (k)

Flow

(q)

0 500 1000 1500 2000 25000

10203040506070

Speed-Flow Curve

Flow (q)

Spee

d(v)

• Modified Greenshield’s model:

AMS Examples


α=3.35 Jam Density = 200 Density Breakpoint = 25Free Flow Speed = 60 Minimum Speed = 6 Speed Intercept=92

1 13 25 37 49 61 73 85 97 1091211331451571691811930

10203040506070

AMS Speed Density Curves

Freeway

Density (k)

Spee

d (v

)

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 1930

400

800

1200

1600

2000

AMS Flow Density Curves

Freeway

Density (k)

Flow

(q)

AMS Examples continued…


Jam Density = 200 Density Breakpoint = 25Free Flow Speed = 60 Minimum Speed = 6

1 5 9 13 17 21 25 29 33 37 410

10

20

30

40

50

60

70

AMS Speed Density Curves

α=3.75α=3.35

Density (k)

Spee

d (v

)

1 6 11 16 21 26 31 36 410

500

1000

1500

2000

2500

AMS Flow Density Curves

α=3.75α=3.35

Density (k)

Flow

(q)

0 50 100 150 200 2500.0

0.5

1.0

1.5

2.0

2.5

BPR Flow-Density Curve

Density

V/C

Ratio

0.0 0.5 1.0 1.5 2.0 2.50.01.02.03.04.05.06.07.08.0

BPR Volume-Delay Curvet = t0[1+0.15(v/c)4]

V/C Ratio

Trav

el T

ime

Fact

or

0.0 0.5 1.0 1.5 2.0 2.50

10

20

30

40

50

60

70

BPR Speed-Flow Curve

V/C Ratio

Spee

d

Compare BPR to AMS


0 50 100 150 200 2500

10

20

30

40

50

60

70

BPR Speed-Density Curve

Density

Spee

d

BPR Examples


0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.00

10

20

30

40

50

60

70

BPR Speed Curves

α=0.15 β=4.0α=0.72 β=7.2α=0.60 β=5.8

V/C Ratio

Spee

d

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.00.0

5.0

10.0

15.0

20.0

25.0

BPR Travel Time Curve

α=0.15 β=4.0α=0.72 β=7.2α=0.60 β=5.8

V/C Ratio

Trav

el T

ime

Fact

or

STA vs. DTA ComparisonSimple Network Example


BPR: α=0.6 β=5.8 AMS: α=3.35

STA vs. DTA ComparisonSimple Network Example


Demand STA DTA

250x3 2.8 2.2

350x3 3.1 2.4

450x3 4.6 6.6

550x3 8.7 14.0

650x3 18.5 21.7

750x3 38.9 29.1

1,000x3 194.7 47.8

1,500x3 2,017.7 85.0

Average Trip Time by Demand Level

250 350 450 550 650 750 1,000 1,5000.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

STADTA

Demand

Avg

Trip

Tim

e

Time Dependent Shortest Path


• The key feature in DTA

• Able to produce Experienced travel time and route that is far more realistic than Instantaneous travel time and route produced in STA.

• Experienced travel time is affected by vehicles departing earlier and later

• Experienced travel time can only be realized after the trip is completed (Arrival Time Profile)

PSRC Time of Day Model


Discrete Logit Choice Model by 30-Minute IntervalAggregated to five periods: AM, MD, PM, EV & NI

Uijkpm = ak + c1kDijk + c2kDijkSE + c3kDijkSE2 + c4kDijkSL + c5kDijkSL2 + v + d

Where: i = Production zone j = Attraction zonek = Time interval p = Purpose (HBW, HBO, HBShop)m= Mode (SOV, HOV) D = Delays SE = Shift early factor SL = Shift late factorV = Socio-demographic variablesd = Dummy variables

0:001:00

2:003:00

4:005:00

6:007:00

8:009:00

10:0011:00

12:0013:00

14:0015:00

16:0017:00

18:0019:00

20:0021:00

22:0023:00

-0.0500

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

A-PP-A

PSRC Time of Day Model


Time of Day Choice ModelPros & Cons


Variations of TOD Profiles by Period

AM MD PM EV NI

• Comparing to static TOD model, choice model adds temporal dynamics that enables peak spreading

• The Shift variables can reasonably spread peak trips over shoulder periods

• The model is sensitive to changes in delays or generalized costs that is crucial for congestion relief studies

• Because TOD was calibrated based on base year HH survey and skims data, the model coefficients become questionable for future years of much higher demand and congestion, and resulting TOD profiles are often unrealistic.

DTA Based TOD Model


Time of Day Model

24-Hour Temporal

24-Hour DTA

Time Varying Skims

Baseline Year Model Development: Start from initial departure time profile Delay calculated by DynusT can be fed back by

30 min increment to the TOD model TOD model will adjust the departure time

profile Iterative process until convergence Consistency between TOD and DTA is

establishedFuture Year Development Considerations:

Departure or arrival time profiles based on trip purposes

Minimizing total schedule delay + travel time based on trip purposes

Decisions applied to future years

DTA Based TOD Model


1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 970.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

Average Trip Time by Departure Time

SOV_06TRK_06HOV_06SOV_30TRK_30HOV_30

15-Minute Interval

Trip

Tim

e

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 970.00000.00200.00400.00600.00800.01000.01200.01400.01600.01800.0200

Departure & Arrival Time Profiles

Departure_06Arrival_06Departure_30Arrival_30

15-Minute Interval

TOD

Shar

es

Next…


• On-going research project funded by FHWA to investigate the costs and benefits of integrating DTA in a 4-step framework. Results are pending in 2012.

• Findings of this project will be shared with modeling community.

• Contact Robert Tung [email protected] for more information.

Thank you !

mailto:[email protected]

integration of dynamic traffic assignment in a four-step model framework – a deployment case study...

Documents

integration of dta

step model framework

dta integration concept

dta tools

dta primer tung chiu

model abm

step process

dta feedback mechanism