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)

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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

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405N1.93ff-sim 405N1.93ff-real

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405N3.04ml-sim 405N3.04ml-real

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405N3.86ml-sim 405N3.86ml-real

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405S3.31ml-sim 405S3.31ml-real

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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

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Trav

el t

ime

(sec

)

simulation observation

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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

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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 (

%)

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IM-26 IM-22 RMA RMB TIS C-1 C-2

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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

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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

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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