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
Noe
-Bix
by R
d
Mc
Nau
ghte
n R
d
Centroid Connector
IR 2
70
Main
Main
IR 2
70
Main
Ram
p
Mc
Nau
ghte
n R
d
IR 2
70
IR 2
70
IR 2
70
Ramp
Noe
-Bix
by R
d
Main
IR 2
70
Ramp
Main
Ramp
IR 2
70
Ram
p
Franklin
IR 2
70
Main
Ramp
Noe
-Bix
by R
d
Mc
Nau
ghte
n R
d
Centroid Connector
IR 2
70
Main
Main
IR 2
70
Main
Ram
p
Mc
Nau
ghte
n R
d
IR 2
70
IR 2
70
IR 2
70
Ramp
Noe
-Bix
by R
d
Main
IR 2
70
Ramp
Main
Ramp
IR 2
70
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
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
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
14
15
16
17
18
19
20
21
22
23
24
25
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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