nterfacing the morpc regional model with dynamic traffic simulation interfacing the morpc regional...

INTERFACING THE MORPC NTERFACING THE MORPC REGIONAL MODEL WITH REGIONAL MODEL WITH

DYNAMIC TRAFFIC SIMULATIONDYNAMIC TRAFFIC SIMULATION

David Roden (AECOM)Supin Yoder (FHWA)

Nick Gill and Zhuojun Jiang (MORPC)Rebekah Anderson and Greg Giaimo

(ODOT)

FHWA – TRANSIMS Deployment Project

Agenda

Study Overview Network Conversion and Debugging Trip and/or Tour Conversion User Equilibrium Assignment and

Convergence Output Results and Sensitivity Tests

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Purpose of the Study

AECOM, MORPC, ODOT, and FHWA are participating in a study to route and simulate MORPC’s tour-based demand on a TRANSIMS network Create a time-dependent TRANSIMS network Route and simulate TP+ trips on the TRANSIMS network Route and simulate MORPC tours on the TRANSIMS

network Feedback travel times from TRANSIMS to the tour

model Create a time-dependent transit network and tour

routing

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Network Conversion Process

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TransimsNet

Synthetic Network

TP+ Network

TPPlusNet

Node DataLink Data

Signal/Sign Warrants

IntControl

Traffic Controls

Speed-Capacity

ArcNet

Network Shapefiles

Conversion Script

Zone DataLink Details

Zone Connector Keys

TRANSIMS Network

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TRANSIMS Coding Concepts

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Original/Default TRANSIMS Network

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Zone Connector Activity Locations

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Freeway Access Problems

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Franklin

IR 2

70

Main

Ramp

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-Bix

by R

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n R

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Centroid Connector

IR 2

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Main

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Ram

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IR 2

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IR 2

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IR 2

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Main

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Ramp

Main

Ramp

IR 2

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Ram

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Franklin

IR 2

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Ramp

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-Bix

by R

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n R

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Centroid Connector

IR 2

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Main

Main

IR 2

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Main

Ram

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IR 2

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IR 2

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-Bix

by R

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Ramp

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Ram

p

Loop ramps were added to the TP+ network to improve results

TRANSIMS Travel Demand Concepts

TRANSIMS models individual persons for 24+ hours

Trips between specific activity locations, at specific times of day, using a specific travel mode and vehicle Activity locations – street locations / block faces Time of day (start/end/duration) – seconds Modes – walk, bike, drive, ride, transit, P&R, K&R, etc.

Convert aggregate trip tables to individual travelers at specific locations and trip start times Zones activity locations within the zone Daily/time period second of the day

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Trip Table Conversion Process

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Diurnal Distributions

MORPC Diurnals

SmoothDataTP+ Scripts

Trip TablesActivity Location

ConvertTrips

Trip File

Population File

Vehicle FileHousehold File

Activity LocationSubzone Factors

LocationData

MORPC Zone Data Non-HH Trip TablesZone BoundariesMORPC HH-ToursBlock DataBlock Boundaries Traffic Counts

Diurnal Smoothing Results

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Activity Location Weights

Use subzone socio-economic data to calculate trip attraction weights by trip purpose and orientation for each activity location within a TAZ

MORPC/ODOT provided a block data file to calculate the attraction weights

Inconsistencies between the TAZ and block file boundaries and socio-economic attributes necessitated complex data processing

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TAZ – Block Data Integration Issues

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MORPC Tours TRANSIMS Tours

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Activities havelocations, start times and durations

Trips connectactivities

Time Home Stop1 Work Stop2 Stop1 Shop Stop25:306:006:307:007:308:008:309:009:3010:0010:3011:0011:3012:0012:3013:0013:3014:0014:3015:0015:3016:0016:3017:0017:30

Work Tour At Work Sub-Tour

TRANSIMS Router and Microsimulator

Router builds a unique path for each trip Between origin and destination activity locations

(link-offset) Starting at a specific second of the day Using a specified travel mode and vehicle Based on network travel times in15-minute

increments Microsimulator moves vehicles between link-

lane-cells on a second-by-second basis Cells are 6 meters long Vehicles move 0, 1, 2, 3, 4, 5, or 6 cells each second

Speeds = 0, 13.5, 27.0, 40.5, 54.0, 67.5 or 81.0 mph

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Microsimulator Feedback Loops

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Microsimulator

Network Travel Paths

Bottlenecks Travel Times

Router

Trips / Tours

Change? Change? StopNo

YesYes

Convergence Statistics

Convergence is defined using multiple statistics Simulation stability and network performance

Number and location of “lost” vehicles by time of day Difference between the average link delay and the

Microsimulator link delay – vehicle hours of travel by link and time of day

User Equilibrium – no traveler can improve their travel time (impedance) by changing paths Difference between the simulated path and the minimum

impedance path for each traveler – vehicle hours of travel by trip

The percentage of travelers with significant differences

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Lost Vehicle Problems

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Iteration 1 Iteration 25

Trip-Model Convergence Statistics

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Trip Gap by Time of Day

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0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Trip

Tim

e G

ap

Departure Hour

2

3

4

5

6

7

8

9

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Link VHT Gap by Time of Day

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0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Lin

k V

HT

Gap

Time of Day

1

2

3

4

5

6

7

8

9

10

11

12

13

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18

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ATR 601: I-70 at Brice Rd.

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Total Volume: All Stations

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Operational Impact Test

Used the turning movement volumes from the simulation to update the signal timing plans for all signals in the region

Applied Progression to calculate signal offsets

Applied Router-Microsimulator to convergence

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Signal Timing and Progression

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Aggregate Wait Time Problems Signal Progression Corridors

Daily Cycle Failures – Original

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Daily Cycle Failures – Operational Test

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Next Steps

Implement global iterations between the tour-model and the network simulation

Perform sensitivity tests and future forecasts

Refine operational details in downtown to provide demand data for a VISSIM subarea analysis

Upgrade the model to TRANSIMS Version 5 Studio and Visualizer

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