bottleneck identification and calibration for corridor management planning
DESCRIPTION
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. Outline. Introduction Bottleneck Identification - PowerPoint PPT PresentationTRANSCRIPT
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Bottleneck Identification and Calibration for Corridor Management
Planning
Xuegang (Jeff) BanLianyu 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