Bottleneck Identification and Calibration for Corridor Management

Planning

Xuegang (Jeff) Ban

Lianyu Chu

Hamed Benouar

California Center for Innovative Transportation (CCIT)

University of California – Berkeley

January 22, 2007

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Outline Introduction Bottleneck Identification Bottleneck Calibration A Real World Example Concluding Remarks

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Introduction Corridor Management

Corridor Management Planning Integrated Corridor Management

Micro-simulation in Corridor Management Performance Evaluation Improvement Scenario Evaluations

Bottleneck Analysis Definition: Locations that capacity less or demand greater than other

locations. Identification: Queue length and duration Calibration in Micro-simulation

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Bottleneck Identification Current Practice

HICOMP, PeMS

Proposed Method Binary Speed Contour Map (BSCM) via Percentile Speeds Assumption: bottleneck area if v<=vth

Why are Percentile Speeds?

Probability of a location being a bottleneck Flexibility of identifying bottlenecks Reliability compared with single “typical” day or average speeds

TtNiptivtivP p ,,1,,,1,)),(),(( p-th percentile speed

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Bottleneck Identification (Cont.) Speed Contour Map

Represented as S(i, t)

IncidentAverage

No-Incident 15%

50% 85%

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Bottleneck Identification (Cont.) Binary Speed Contour Map (BSCM)

BS(i, t) = 1, if S(i, t) <= vth,

0, otherwise

Bottleneck(s) can be identified automatically via BSCM

Vth = 35mph

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Bottleneck Calibration Current Practice

FHWA Micro-Simulation Guideline: Visual Assessment

Proposed Method - A Three Step-Process 1. Visual Assessment 2. Area Matching 3. Actual Speed Matching

Three Levels of Details for Calibrating Bottlenecks

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Step 1. Visual Assessment Purpose

Make sure the number of bottlenecks, their locations and areas roughly match

Qualitative and no quantitative measures can be defined

Observed Data Simulation Data

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Step 2: Bottleneck Area Matching Purpose

Match bottleneck locations and areas using BSCMs

Quantitative Measure C1

Area Matching Criteria:

Overlapping Area Union Area

N

iii

T

trs

N

iii

T

trs

xxtiBStiBS

xxtiBStiBSC

1 1

1 11

)}())],(),([{(

)}())],(),([{(

11 C

C1 = 90.5%

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Step 3: Actual Speeds Matching Purpose

Match Detailed Bottleneck Speeds using both SCMs and BSCMs

Quantitative Measure C2

Actual Speed Matching Criteria:

N

iii

T

trsrs

N

iii

T

trsrs

xxtiStiStiBStiBS

xxtiStiStiBStiBSC

1 1

1 12

)}())],(),([)],(),([{(

)}()|),(),(|)],(),([{(21

Observed Data

Simulation Data

C2 = 64.2%

22 C

Union Area

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A Real World Example I-880 in the San Francisco Bay Area

One of the series of studies for Corridor Management Planning On-going project and the results presented here are interim

The Example I-880 NB, AM Peak hours (6:30 AM – 9:30 AM) Observed data: 20 typical weekdays (Tuesday – Thursday) Double loop detectors with spacing ¼ mile

Simulation Tool Paramics

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The Study Area

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Calibration Results – Flow and Travel Time

Calibration is satisfactory for matching flow and travel times

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Calibration Results – Bottlenecks Bottlenecks?

Observed Data

Simulation Data

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Calibration Results – Bottlenecks Bottlenecks? C1= 24.2%, C2 =42.5%

Observed Data

Simulation Data

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Concluding Remarks Conclusions

Percentile speeds was used to conduct bottleneck analysis Proposed an automatic bottleneck identification method based on

binary speed contour maps Developed a three-step process for bottleneck calibration: visual

assessment, area matching, and actual speed matching Defined quantitative measures for bottleneck calibration Enhancement to current micro-simulation calibration practice

Future Study Using data from single loops (occupancy) Procedure for calibration