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Evaluation of Potential ITS Strategies under Evaluation of Potential ITS Strategies under Non-recurrent Congestion Using Microscopic Non-recurrent Congestion Using Microscopic SimulationSimulation
Lianyu Chu, University of California, Irvine
Henry Liu, Utah State University
Will Recker, University of California, Irvine
IntroductionIntroduction
• Microscopic simulation– a software tool to model traffic system, including roads,
drivers, and vehicles, in fine details.– models:AIMSUN2, CORSIM, MITSIM, PARAMICS, VISSIM…
• Why simulation?– Infeasibility / inadequacy of mathematical treatment of traffic
processes– answer “why if” questions
• Applications– model and Evaluate ITS– calibrate / optimize operational parameters of ITS strategies– develop / test new models, algorithms, control strategies
ObjectivesObjectives
• Evaluating ITS– study on how potential ITS strategies can help
solve non-recurrent traffic congestion
• Involved ITS strategies– incident management – adaptive ramp metering– arterial management – traveler information– integrated control: combination of several ITS
components
PARAMICSPARAMICS
• PARAMICS: PARAllel MICroscopic Simulation– a suite of software tools for microscopic traffic
simulation, including: – Modeller, Analyzer, Processor, Estimator, Programmer
– developer: Quadstone, Scotland
• Features– large network simulation capability– modeling the emerging ITS infrastructures– Application Programming Interfaces (API)
– access core models of the micro-simulator– customize and extend many features of Paramics – model complex ITS strategies
Modeling complex ITS strategy via API Modeling complex ITS strategy via API
API Library from Vendor
Developed API Library
Advanced Algorithms (complex ITS strategies)
Adaptive Signal Control
Adaptive Ramp Metering
Integrated Control Strategy
ATMIS Modules
Data Handling
Routing
Ramp
Signal
CORBA
Databases
Demand
XML
Study network / Identified problemStudy network / Identified problem
Irvine, CA
An shoulder incident on I-405N, at 7:45
NB I-405 is highly congested: 7:30-8:30 AM
Evaluation scenariosEvaluation scenarios
Scenario
ITS components
Signal Control
Ramp Metering
Traveler Information
IncidentManagement
(1) IM-33 Actuated Fixed time N/A 33 mins
(2) IM-26 Actuated Fixed time N/A 26 mins
(3) IM-22 Actuated Fixed time N/A 22 mins
(4) RMA Actuated ALINEA N/A 26 mins
(5) RMB Actuated Bottleneck N/A 26 mins
(6) TIS Actuated Fixed time 20% compliance 26 mins
(7) C-1 Actuated ALINEA 20% compliance 26 mins
(8) C-2Arterial Management ALINEA 20% compliance 26 mins
Procedure of evaluation studyProcedure of evaluation study
• Simulation network coding
• Calibration of simulation model
• Implementation of ITS components / strategies
• Simulation runs / getting performance
measures from simulation
• Data analysis
• Evaluation results
Calibration procedureCalibration procedure
N
Y
Calibration of driving behavior models
Total OD estimation
Route choice adjustment
Reconstruction of time-dependent OD demands
Model Fine-tuning
Volume, Travel time match?
Overall model validation / evaluation
Basic data input / Network coding
Calibration of routing behavior model
Reference OD from planning model
Overall model validation / evaluation (I)Overall model validation / evaluation (I)
0
100
200
300
400
500
600
700
6:05 6:30 6:55 7:20 7:45 8:10 8:35 9:00 9:25 9:50
405N0.93ml-sim 405N0.93ml-real
0
50
100
150
200
250
6:05 6:30 6:55 7:20 7:45 8:10 8:35 9:00 9:25 9:50
405N1.93ff-sim 405N1.93ff-real
0
200
400
600
800
1000
405N3.04ml-sim 405N3.04ml-real
0
200
400
600
800
1000
405N3.86ml-sim 405N3.86ml-real
0
200
400
600
800
1000
405S3.31ml-sim 405S3.31ml-real
0
50
100
150
200
133s9.37ml-sim 133s9.37ml-real
5-min traffic count calibration at major freeway measurement locations(Mean Abstract Percentage Error: 5.8% to 8.7%)
Overall model validation / evaluation (II)Overall model validation / evaluation (II)
Comparison of observed and simulated travel time of SB / NB I-405
0
100
200
300
6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00
Trav
el t
ime
(sec
)
simulation observation
0
200
400
600
6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00
Tra
ve
l tim
e (
se
c)
simulation observation
3.1% (SB) 8.5% (NB)
• Incident and incident management– an incident plug-in with the following parameters
– location of the incident – time when the incident happens– incident detection time and response time – incident clearance time
– incident management– modifying incident clearance time
• Actuated signal control– using the plug-in we previously developed
Implementation of ITS components (I)Implementation of ITS components (I)
Implementation of ITS components (II)Implementation of ITS components (II)
• Adaptive ramp metering – ALINEA: local adaptive ramp metering
– proposed by Papageorgiou, et al.
– BOTTLENECK: coordinated adaptive ramp metering– applied in Seattle, Washington
– using the plug-in we previously developed– having been calibrated in the target network
))(*()(~)( ttOOKttrtr R
)))(/)((),(()1,(),(1
jijijreduction
n
ion WFWFtiQMAXtjQtjr
Implementation of ITS components (III)Implementation of ITS components (III)
• Traveler information systems – all kinds of traveler information systems
– using dynamic feedback assignment– instantaneous traffic information is used for traveler’s
route choice. – control parameter: compliance rate (familiarity factor)
• Arterial management (along diversion routes) – change timing for diverted traffic during incident
– off-line optimization using SYNCHRO based on estimated traffic volume
– applied in integrated control scenarios (S7 and S8)
Implementation of ITS components (IV)Implementation of ITS components (IV)
• Integrated control scenarios– Scenario #7:
– freeway control + traffic information
– Scenario #8: – freeway control + arterial traffic management + traffic
information
– implements through API programming– Incident detection and response time: 5 minutes, then:– CMS showing messages (for S7 and S8)– adaptive ramp metering using ALINEA (for S7 and S8)– arterial management: using timing under incident (for S8)
Performance measuresPerformance measures
• MOE #1 system efficiency: – Vehicle Hour Traveled (VHT)
• MOE #2 system reliability measure: – average std. of OD travel times over the whole period
(Std_ODTT)
• MOE #3 freeway efficiency measure – average mainline travel speed during the peak period
(peak_AMTS) and the whole period (AMTS)
• MOE #4 on-ramp efficiency measure– total on-ramp delay (TOD) – time percentage of the on-ramp queue spillback (POQS)
• MOE #5 arterial efficiency measure– average travel time of an arterial (ATT) and its std.
(std_ATT)
Determining number of runsDetermining number of runs
• Using two MOEs: VHT and AMTS
N
Y
Original nine runs
Start
Calculating the mean and its std of each performance measure
Is current # of runs enough?
End
Calculating the required # of runs for each performance measure
Additional one simulation run
22/ )(
tN
• μ, δ: – mean and std of
MOE based on the already conducted simulation runs
• ε: allowable error• α: confidence
interval
Evaluation results: overallEvaluation results: overall
• Simulation period: from 5:45 to 10:00 a.m.• All scenarios were compared with Scenario #1:
– no incident management scenario (33 minutes)
• overall performance:– all ITS strategies have positive effects– more ITS components, more benefits
0%
3%
6%
9%
IM-26 IM-22 RMA RMB TIS C-1 C-2
VH
T s
avin
g (
%)
0%
10%
20%
30%
40%
50%
IM-26 IM-22 RMA RMB TIS C-1 C-2
Rel
iab
ilit
y in
crea
se (
%)
Evaluation results: VHTEvaluation results: VHT
IM-33 IM-26 IM-22 RMA RMB TIS C-1 C-2
IM-33 • 0.85 0.99 0.995 0.9995 0.9995 0.9995 0.9995
IM-26 • 0.85 0.9 0.95 0.9995 0.9995 0.9995
IM-22 • 0.7 0.8 0.9995 0.9995 0.9995
RMA • 0.6 0.9995 0.9995 0.9995
RMB • 0.9995 0.9995 0.9995
TIS • 0.995 0.999
C-1 • 0.975
C-2 •
* A higher confidence interval corresponds to a more significant VHT difference of
the two scenarios.
Confidence intervals of VHT differences of any two scenarios*
Freeway performance (NB I-405)Freeway performance (NB I-405)
ScenarioAMTS (mph)
AMTS Increase
(%)peak_AMTS
(mph)Increase of
peak_AMTSTOD (hour)
POQS (%)
(1) IM-33 50.5 38.6 42.8 1.9
(2) IM-26 51.4 1.7% 39.9 3.4% 42.0 2.0
(3) IM-22 51.5 1.9% 40.2 4.3% 40.9 1.8
(4) RMA 51.7 2.4% 40.6 5.4% 47.6 1.1
(5) RMB 51.8 2.6% 40.5 5.0% 73.4 2.1
(6) TIS 54.1 7.1% 43.8 13.5% 51.8 2.7
(7) C-1 54.7 8.3% 45.2 17.3% 41.0 1.0
(8) C-2 54.6 8.2% 44.9 16.4% 43.3 1.2
Notes: AMTS – Average mainline travel speed of entire simulation period (6 – 10 AM)peak_AMTS – Average mainline travel speed of congestion period (7:30 – 9:30)TOD – Total on-ramp delayPOQS – Time percentage of vehicles on the entrance ramps spillback to the surface streets
Evaluation results: Evaluation results: incident management and adaptive ramp metering incident management and adaptive ramp metering
• Incident management:– incident duration from 33 minutes to 26 minutes
– statistically improve VHT at 85% confidence interval
– from 33 minutes to 22 minutes– statistically improve VHT at 99% confidence interval
• Adaptive ramp metering– ALINEA and BOTTLENECK cannot effectively
improve VHT or freeway travel speed.– comparing ALINEA and BOTTLENECK:
– BOTTLENECK performs better in terms of VHT– ALINEA performs better in terms of on-ramp MOEs
Evaluation results: traffic informationEvaluation results: traffic information
• Scenario #6 has the greater benefits than – adaptive ramp metering (S4 and S5) – improved incident management (S3)
• Reason for the good performance: network topology • In terms of on-ramp performance (TOD and POQS): S6 does
not show a good performance
0%
1%
2%
3%
4%
5%
6%
0% 10% 20% 30% 40% 50%
Compliance rate (%)
VH
T s
avin
g (
%)
• VHT vs. compliance rate
• Sensitivity analysis:– 15-20%: VHT saving reaches a
stable maximum
– 20%: used in S6, S7 and S8.
Evaluation results: integrated controlEvaluation results: integrated control
• S7: only freeway agency responds• S8: both freeway and arterial agencies involve.• Compared to S6,
– S7 and S8 significantly improved VHT.
• Comparing #7 and #8, – VHT: S8 got improved at 97.5% confidence interval. – std_ODTT, AMTS and peak_AMTS: S7 and S8 have
comparable performance– TOD and POQS: S8 introduces more on-ramp delays – ATT and std_ATT: S8 introduces shorter arterial delays
• S8 shows the best performance• S7’s performance is also good
Discussion: Discussion: Benefit of ITS during the congestion period Benefit of ITS during the congestion period
• Incident is injected at 7:45, S6, S7 and S8 clearly "recovers" faster than S2
• No matter what ITS strategies are applied – a worst time of congestion
always exists
20
30
40
50
60
70
6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00
Time of day (incident period: 7:45-8:11)
Fre
eway
ave
rag
e sp
eed
(m
ph
)
IM-26 TIS C-1 C-2
• Reasons:– dynamic feedback assignment, based on instantaneous travel time
information feedback from simulation– Incident detection and response time
• If instantaneous information used for traffic management– a worst time of traffic congestion will not be avoided
ConclusionConclusion
• Most important ITS component: – traffic information
• Adaptive ramp metering: – not effective under incident scenario
• Fast incident management: – needs to be applied with other ITS strategies
• Proper combination of ITS strategies: – yields greater benefits
• Integrated control performs the best