Can Multi-Resolution Dynamic Traffic Assignment live up to the Expectation of Reliable Analysis of
Incident Management Strategies
Lili (Leo) Luo, P.E., ITS & Safety EngineerSarath Joshua, P.E., Ph.D., ITS & Safety Program Manager
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Basic need for evaluation of ITS operational strategies
Low cost Efficient Reliable Capable of evaluating area
wide impact
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How did we get started? Neither the static Macroscopic Travel
Demand Model nor the Microscopic simulation model met our needs
Decision to test the emerging Mesoscopic simulation-based DTA model through a Case Study
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The Case Study Approach Convert the 4-step TDM model into DTA
(DynusT) model Study a major freeway traffic incident that
occurred on I-10 during morning peak period Calibrate the subarea baseline (Normal)
model Perform scenario analysis Convert stretch of interest into microscopic
simulation model for visualization The modeling effort was conducted in house
at MAG
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Purpose of the case study Test if the DTA model can be used as
an operations planning tool to evaluate strategies
Obtain hands-on experience on DTA model from model calibration to scenario analysis
Experiment with the concept of Multi-Resolution modeling method
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MAG Regional DTA model 2006 TAZs Over 10,000
SQ Miles 20508 Links 9893 Nodes 2364
Signalized intersections
2.78M Trips during 5 hours of morning peak
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Impacted Area of a major Incident
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Model Calibration—Count Calibration
87 sensors Collected
over a three year time frame
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
DUE Count Calibration
UE
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Model Calibration – AM Peak Period Travel Time V.S. GPS Run
Freeway Morning Peak Period GPS Run Travel Time Model Travel Time
I-10 WB 46MIN 24SEC 46MIN 30SEC
Freeway Morning Peak Period GPS Run Travel Time Model Travel Time
US60 WB 20MIN 20MIN 30SEC
Freeway Morning Peak Period GPS Run Travel Time Model Travel Time
202 Santan WB 5MIN 5MIN 24SEC
Freeway Morning Peak Period GPS Run Travel Time Model Travel Time
202 RedMountain WB 20MIN 18MIN 54SEC
Freeway Morning Peak Period GPS Run Travel Time Model Travel Time
101 NB 32MIN 31MIN
Freeway Morning Peak Period GPS Run Travel Time Model Travel Time
143 NB 5MIN 4MIN 42SEC
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Space-Time Diagram showing bottleneck locations
Loop202 Red Mountain WB
5:00
10:0
0L1
01
Rur
al
24th S
t
Airp
ort
Loop 101 NB
Fry
US60
5:00 10:00
McKellipsUS 60 WB
5:00
10:
00
Loop
101
Rur
al R
d
Mill
Ave
I-10
Prie
st D
r.
Loop 202 Santan WB
5:00
10:0
0L1
01
Kyr
en
I-10
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Time-Dependent Travel Time By Freeway
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Real-life Incident
February 4, 2010 Location: Salt River
Bridge on I-10 Westbound
Time 6:20AM Duration: 3 hour 20
minutes Severity: Three lanes
blocked all the time, I-10 completely shut down three times.
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Incident Scenario
Scenario 0 — Baseline (Normal Condition)
Scenario 1— With ITS Infrastructure (DMS and Ramp Metering) turned on
Scenario 2 — Without ITS Infrastructure
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Traveler Information and Congestion Response Pre-trip and In-route information DMS congestion warning Driver congestion response behavior
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Scenario 1 With ITS Infrastructure
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Scenario 1: With ITS infrastructure Tempe to Downtown Phoenix Travel
TimeSouth Tempe & Northwest Chandler
Downtown Phoenix
South Tempe/Northwest Chandler To Phoenix Downtown Travel Time
Baseline Incident With DMS & Ramp Meter
28 Min 52 Min
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Affected Routes
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3 Mile Upstream of Crash Site on I-10
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Samples of impacted vehicle
Vehicle ID: 682622 Departure Time: 7:02 AM
Origin:Santan Freeway @ Alma School Road
Destination: Glendale Avenue @ 16th Street
Scenarios BaselineDMS & Ramp
MeteringTravel Time 78MIN 139MIN
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Another Vehicle
Vehicle ID: 481654 Departure Time: 6:40 AM Origin: US 60 @ Dobson RdDestination: Dunlap Ave @ 43th Ave
Scenarios BaselineDMS & Ramp
MeteringTravel Time 45MIN 110MIN
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Scenario 2 (No ITS Infrastructure) Tempe to Downtown Phoenix Travel
Time
South Tempe & Northwest Chandler
Downtown Phoenix
South Tempe/Northwest Chandler To Phoenix Downtown Travel Time
BaselineIncident without ITS Infrastructure
28 Min 51 Min
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Scenario 2 (No ITS Infrastructure)
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Baseline VS incident with ITS VS Incident without ITS
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Scenario 2 (No ITS Infrastructure)
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Vehicles under No ITS scenario
Vehicle ID: 682622 Departure Time: 7:02 AM
Origin:Santan Freeway @ Alma School Road
Destination: Glendale Avenue @ 16th StreetScenarios Baseline No ITSTravel Time 45MIN 123MIN
Vehicle ID: 682622 Departure Time: 7:02 AM
Origin:Santan Freeway @ Alma School Road
Destination: Glendale Avenue @ 16th Street
Scenarios BaselineDMS & Ramp
MeteringTravel Time 45MIN 139MIN
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Another Vehicle
Vehicle ID: 481654 Departure Time: 6:40 AM Origin: US 60 @ Dobson RdDestination: Dunlap Ave @ 43th Ave
ScenariosBaseline Incident Without ITS
Travel Time 45MIN 129MIN
Vehicle ID: 481654 Departure Time: 6:40 AM Origin: US 60 @ Dobson RdDestination: Dunlap Ave @ 43th Ave
ScenariosBaseline Incident with DMS &
Ramp MeteringTravel Time 45MIN 110MIN
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Summary Vehicle Hours Travel (VHT)
Freeways Baseline DMS & Ramp MeterNo ITS
infrastructure
I-10 4154.858 6004.067 6196.473
US60 4413.682 6795.597 6891.945
L202RedMountain 2698.81 2885.447 2848.178
South Tempe/Northwest Chandler To Downtown Phoenix Travel Time
Baseline Incident with DMS & Ramp Meter
Incident without ITS infrastructure
28 Min 52 Min 51 Min
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Meso-Micro conversion Selected area subarea cut for VISSIM micro
simulation using DynusT-VISSIM converter All time-dependent routes and flows are
converted A little more network clean up More detail timing and crash setup
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VISSIM Microscopic Simulation
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Conclusions Mesoscopic Simulation-based DTA
model DynusT: Capable of demonstrating the status of
our freeway system operation in a capacity restraint and time-dependent manner.
Able to match field observed data following relatively simple procedure for model calibration.
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Conclusions Logical scenario analysis outcomes Demonstrate the benefit of traveler
information/ITS during an incident with region wide impact
Reasonable cost and effort Combined with Macro and Micro models
for Multi-Resolution Modeling to answer complicated questions
Appear useful for operations planning
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Lessons Learnt Leverage data collection efforts with Travel
Demand Model More sensitive to network and data errors
—Very Important!!! Could extract more useful information from
the model Start with the entire regional model
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