Download - Toward a Consistent and Robust Integrated Multi-Resolution Modeling Approach for Traffic Analysis
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Transportation Operations Group
Toward a Consistent and Robust Integrated Multi-Resolution Modeling
Approach for Traffic Analysis
May 17-21, 2009
Jeff Shelton, TTIYi-Chang Chiu, Univ. of Arizona
TRB – Transportation Planning ConferenceHouston, TX
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Transportation Operations Group
Outline
Introduction– Mesoscopic– Microscopic
Multi-Resolution Modeling– Concept– Conversion Process– Modeling Issues
Case Study
Applications
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Transportation Operations Group
Outline
Introduction– Mesoscopic– Microscopic
Multi-Resolution Modeling– Concept– Conversion Process– Modeling Issues
Case Study
Applications
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Transportation Operations Group
Introduction
Integrating mesoscopic dynamic traffic assignment (DTA) and microscopic traffic simulation and assignment models can be advantageous for region-wide operational planning projects– DTA – region-wide estimation of traffic redistribution– Microscopic – local operational analysis
The integration synergizes the strengths of both models.
Challenges remain in model translation and interface
Modeling issues to be addressed– Consistency– Situation in which feedback is needed
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Transportation Operations Group
Simulation-Based Dynamic Traffic Assignment (SBDTA) Address issues that may fall beyond the reach of
both:– Microscopic models: (dynamic but small-scale) typically
used by traffic engineers for project traffic studies– Macroscopic models: (large-scale but static) typically
used by transportation planners for long-range planning– SBDTA – dynamic and large-scale
The scenarios of interest may result in shifts of network or corridor-wide traffic flow patterns.– Significant change to roadway configuration– Certain corridor management strategies
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Transportation Operations Group
Mesoscopic Dynamic Traffic Assignment DynusT v2.0
– Free version available for DYNASMART-P users
Dynamic simulation and assignment tool for regional operational planning analysis
Equilibrium-based Dynamic Traffic Assignment– Assigned paths are based on
experienced (actual) travel time
Applications– Assess impacts of ITS
technologies– Work zone planning and traffic
management– Evaluate HOV/HOT lanes– Congestion pricing– Special event/emergency
evacuation
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Transportation Operations Group
Microscopic
VISSIM 5.1
A driver-behavior-based simulation tool capable of performing multiple applications including– Analyzing complex intersections – Border crossings inspection
booths– Managed lanes – University campus settings
Fined-grained analysis– Vehicle interactions– Individual lane analysis
Simulate multiple modes of transportation simultaneously 3-D graphics
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Transportation Operations Group
Outline
Introduction– Mesoscopic– Microscopic
Multi-Resolution Modeling– Concept– Conversion Process– Modeling Issues
Case Study
Applications
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Transportation Operations Group
Concept
What is multi-resolution modeling?– Integrating mesoscopic and microscopic models for the
purpose of achieving a specific goal» Analyze network at both the system-wide and
localized levels
Why is multi-resolution modeling so important?– Mesoscopic & microscopic models are not mutually
exclusive– They are complimentary to one another and can
accomplish optimal modeling capabilities.– Retain the best characteristics of both
» Realistic representation of regional traffic» Detailed interactions
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Transportation Operations Group
Concept
Mesoscopic Model Model Conversion
Process Integration
Tool
MicroscopicModel
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Transportation Operations Group
Concept
DynusT VISSIMVISUMSub-Area
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Transportation Operations Group
Multi-Resolution Modeling Framework
Regional Travel
Demand Model
Initial Network
Conversion (DynusT)
Calibration•Speed Profile•OD•Traffic Model
Sub-Area Cut
DVC
VISSIMCalibration
Network Modificatio
n
Field Data
Rerun
DTA
Detailed Analysis
No
Yes
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Transportation Operations Group
DTA Model Preparation
Convert the GIS layer of the Travel Demand Model to Mesoscopic format.
Disaggregate 24-hour matrix based upon car & truck– Home to work– Work to home– Home to private– Private to home– Thru– External Local– Non-home based external local
Multiply each matrix by corresponding hourly factor
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Transportation Operations Group
DTA Model Preparation
H-W W-H H-P P-HTHR
UEXLO
NHBEXLO
Multiply each matrix by
hourly factor
Summation of matrices gives you directional
1-hour matrix
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Transportation Operations Group
DTA Model Preparation
24 - one hour matrices
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Transportation Operations Group
Calibration
Traffic flow model– Traffic simulation in
DynusT is based upon the Anisotropic Mesoscopic Simulation (AMS) model
– Moves vehicles based upon speed-density (v-k) relationship
– v-k relationship is derived from Greenshields equation
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Transportation Operations Group
Calibration
Time-Dependent OD – Minimize the deviation
between simulated and actual screen line counts & speed profile
– Iterative process– Program solves
linearized quadratic minimization problem
– Results in updated OD matrices
Traffic Network Traffic Flow Model
Intersection Controls
Estimated Time-Dependent OD
Matrices
Traffic Assignment/Simulation
Linear Optimization
Model
Optimized Affected, Time-Dependent OD
Pairs
Results
Up
date
Dem
an
d
Assignment Results
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Transportation Operations Group
Conversion Process
Sub-area cut– Remove unneeded
sections of network– Renumbering of new
zones, nodes and links– Retains paths and flows
that travel through the sub-area
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Transportation Operations Group
Conversion Process
DynusT-VISSIM Converter– Developed by researchers
from TTI and UA– Converts roadway
network to VISUM network
– Retains network geometry
– Converts all time-dependent paths and flows
– Creates separate transportation systems (car, truck)
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Transportation Operations Group
Conversion Process
Microscopic model– Calibrate VISSIM model
to reflect realistic roadway conditions
– Perform detailed “fine-grained” analyses» Speed profile for
individual lanes» Lane-changing
behaviors» Vehicle interactions
at merge areas– Create 3-D graphics for
presentations
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Transportation Operations Group
Modeling Issues
Consistency– Network
» Lane configuration» Geometric design
– Paths and flow» Verify same origin/destination paths» Verify number of vehicles generated
– Speed profile» Perform field data collection to determine speed and
vehicle counts» Obtain v-k curve from simulation output» Calibrate models with field data
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Transportation Operations Group
Modeling Issues
Density (veh/mi)
Sp
eed
(m
ph
)
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Transportation Operations Group
Modeling Issues
When Feedback is Necessary
Rerun
DTA
Regional Travel
Demand Model
Initial Network
Conversion (DynusT)
Calibration•Speed Profile•OD•Traffic Model
Sub-Area Cut
DVC
VISSIMCalibration
Network Modificatio
n
Field Data
Detailed Analysis
No
Yes
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Transportation Operations Group
Outline
Introduction– Mesoscopic– Microscopic
Multi-Resolution Modeling– Concept– Conversion Process– Modeling Issues
Case Study
Applications
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Transportation Operations Group
Case Study
City Council proposes
ordinance to restrict trucks from using left
lane on I-10 corridor
How does the ordinance affect the
freeway and surrounding arterials?
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Transportation Operations Group
Case Study
Which type of model do I use?
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Transportation Operations Group
Case Study
Truck restricted lanes– A case study to analyze the effectiveness of restricting
trucks from left-most fast lane on freeway– 22-mile corridor of I-10 in El Paso, TX– Analyze a.m. peak, p.m. peak, & mid-day– Determine benefits
» Speed on left-most lane» Acceleration/Deceleration patterns» Vehicle interactions at merge areas
– DynusT estimates region-wide truck trajectories (route and flows)
– VISSIM models detailed IH-10 truck lane operations given truck trajectories
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Transportation Operations Group
Model Development
106 Origin/Destination links - 1895 Routes created
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Transportation Operations Group
Model Development
GPS unit was used to input freeway grading information
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Transportation Operations Group
Model Development
Field data collection-freeway speed
profile (PM peak hour)
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Transportation Operations Group
Model Development
Data provided by TxDOT Automatic Traffic Recorder Stations
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Transportation Operations Group
Model Development
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Transportation Operations Group
Case Study
56575859606162636465
0 3600 7200 10800 14400
Spee
d (m
ph)
Time (sec)
I-10 EB @ Sunland Park(7-11 am)
Base Restricted
-1.5
-1
-0.5
0
0.5
1
1.5
0 3600 7200 10800 14400
Acce
lera
tion
(ft/s
2)
Time (sec)
I -10 EB @ Sunland Park(7-11 am)
Base Restricted
SpeedAccel/Decel
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Transportation Operations Group
Case Study
0
20
40
60
80
0 3600 7200 10800 14400
Spee
d (m
ph)
Time (sec)
I-10 EB @ Paisano(3-7 pm)
Base Restricted
-1-0.5
00.51
1.52
0 3600 7200 10800 14400
Acce
lera
tion
(ft/s
2)
Time (sec)
I-10 EB @ Paisano(3-7 pm)
Base Restricted
SpeedAccel/Decel
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Transportation Operations Group
Case Study
0
20
40
60
80
0 3600 7200 10800 14400
Spee
d (m
ph)
Time (sec)
I-10 WB @ PaisanoLeft Lane (3-7 pm)
Base Restricted
0
20
40
60
80
0 3600 7200 10800 14400
Spee
d (m
ph)
Time (sec)
I-10 WB @ PaisanoRight Lane (3-7 pm)
Base Restricted
Speed – Left vs. Right Lane
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Transportation Operations Group
Outline
Introduction– Mesoscopic– Microscopic
Multi-Resolution Modeling– Concept– Conversion Process– Modeling Issues
Case Study
Applications
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Transportation Operations Group
Applications
Managed lanes– Truck restricted lanes– HOV lanes– HOT lanes– Time-dependent
variable pricing
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Transportation Operations Group
Applications
Geometric design alternatives– Freeway direct connect
» Various design configurations
– Ramp reconfiguration» Braided ramps» “X” ramps
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Transportation Operations Group
Applications
Traffic impact studies– New retail shopping
centers» Driveways» Pedestrian crossings
– University campus planning» Integrating various
modes of transportation (e.g. student, faculty, staff, pedestrians, transit)
» New parking facilities» Campus core closure
– Traffic calming
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Transportation Operations Group
Questions ?