november 15, 2005 dr. robert bertini dr. sue ahn using archived data to measure operational benefits...
TRANSCRIPT
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November 15, 2005
Dr. Robert BertiniDr. Sue Ahn
Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System
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Presentation Outline
1. Research Objectives
2. Introduction
3. Overview of SWARM
4. Previous Studies
5. Measures of Potential Benefits
(based on previous studies)
6. Data Sources
(ITS infrastructure and Portal)
7. Study Scope (Work Plan)
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1. Research Objectives
• Demonstrate the use and display of archived data from multiple sources as a tool for evaluation and monitoring of freeway operations.
• Evaluate the effectiveness of the new SWARM program in Portland, Oregon
• Develop tools to facilitate efficient deployment of ramp metering programs in Portland and other places
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2. Introduction
Goals of Ramp Metering
Efficient management of traffic congestion
• Maximize the capacity of the freeway by limiting the
amount of traffic entering a freeway
• Break up the platoons of vehicles discharged from a
traffic signal upstream.
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2. Introduction (con’t)
Advantages of Ramp Metering
• Improved mainline traffic flow and efficient merging
• Improved safety
• Improved air quality
• Balance and efficiency in network routings
Disadvantages of Ramp Metering
• Delays for on-ramp traffic
• Potential negative effect on alternate routes due to
rerouting
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2. Introduction (con’t)
Types of Ramp Metering Control
• Pre-timed
- based on historical information
• Traffic-responsive
- based on real-time information
- Local: based on local conditions around ramps
(e.g. Demand-capacity, ALINEA, etc.)
- Coordinated: based on conditions around a series of
on-ramps (e.g. Bottleneck, SWARM, MEATLINE,
Fuzzy Logic, etc.)
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3. Overview of SWARM
How does it work?
SWARM 1: Global control• Forecasts density at a bottleneck and determines the
required volume reduction from upstream ramps• Determines the individual metering rates based on the
overall volume reduction required
SWARM 2: Local control• Determines the metering rate independently for each on-
ramp based on the local condition (density)
Actual rates: more restrictive of the two rates
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3. Overview of SWARM (con’t)
Capabilities• Potential to detect congestion in advance with high
accuracy• Robustness: Built-in failure management (data clean-
up)
Potential Problems• Potential ill-performance when forecasts are not
accurate Good prediction models are key
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3. Overview of SWARM (con’t)
Implementation Schedule
Already implemented on I-84 and OR 217 SB
OR 217 NB: November 16, 2005I-205 SB: December, 2005I-205 NB: January 2006US 26 EB: February 1, 2006US 26 WB: February 15, 2006I-5 (lower section): March 1, 2006I-5 (upper section): March 20, 2006I-405: April, 2006
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4. Previous Studies
Evaluation via field testing
Portland, OR (Bertini et al., 2004)
• Study location: I-5N from Broadway Bridge to Interstate Bridge
• Data: loop detector data, probe vehicles with AVL• Analysis:
– Bottleneck characteristics: location, capacity, etc.– Manual traffic simulation: system-wide delay savings
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4. Previous Studies (con’t)
Evaluation via field testing (con’t)
Portland, OR (Bertini et al., 2004)
• Weekend ramp meter shutdown (US Hwy 26)– Meters were off on one weekend and turned back on
the following weekend– Ramp metering led to more travel at better quality of
service
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4. Previous Studies (con’t)
Evaluation via field testing (con’t)
Minneapolis, Minnesota Evaluation (2000)
• The meters were shut down Minneapolis, Minnesota for eight weeks and a before and after analysis was performed.
• During the peak periods, freeway mainline throughput declined by an average of 14% with the ramp meters off
• Travel time increased by more than 25,000 (annualized) hours.
• Crash frequency increased by 26% while the meters were off.
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4. Previous Studies (con’t)
Evaluation via simulation
Orange County, CA (Zhang et al., 2001)
• Paramics to compare four algorithms: ALINEA, Bottleneck, Zone, and SWARM
• Results:– All algorithm improved mainline traffic flow– Little difference in performance– Performance of SWARM was sensitive to the
accuracy of the predictions
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4. Previous Studies (con’t)
Performance measure Change
Freeway mainline speed Increases
Accident rate/frequency Decreases
Overall travel time/delay time
Decreases
Freeway mainline volume/flow/stability of flow
Increases and Stabilizes
Fuel Savings Increases
Benefit/Cost Ratio 4:1 to 62:1
Ramp delays Increases
Arterial vehicle volume Increases, but insignificant
Summary of benefits measured
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5. Measures of Potential Benefits
Potential Benefits
Measures/parameters
Savings in delay Mainline: Delay, Travel Time Speed, Flow,
VHT, VMT
On-ramps: Queue length, Waiting time
Improved safety Number of Incidents
Improved air quality
Engine emissionsFuel consumption
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6. Data Sources
• 98 CCTV cameras
• 19 variable message signs
• 135 ramp meters
• 485 inductive loop detectors
• Digital archives of incident logs
• AVL Archives of COMET movements
• Extensive fiber optics network
• Weather data station
Transportation System ManagementIn the Portland metro area ODOT currently operates an extensive
advanced traffic management system from the TMOC including:
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6. Data Sources (con’t)
Inductive Loop Detectors Closed-Circuit Television Cameras
Data Archived in Portal
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6. Data Sources (con’t)
Potential Benefits Measures / Parameters Data Sources
Savingsin delay
Mainline: Delay, Travel Time,
Speed, Flow, VHT, VMT
On-ramps: Queue length,
Waiting time
Mainline: Loop Detectors (Portal)
Probe vehicles (travel time)
On-ramps: CCTV (queue length)
Probe vehicles (waiting time)
Improved safety Number of Incidents Incident LogsState wide crash database
Improved air quality
Engine emissionsFuel consumption
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6. Data Sources (PORTAL)
Portland Transportation Archive Listing (PORTAL)PSU Designated as Regional Archive Center
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6. Data Sources (PORTAL)
PORTAL: Speed Contour Plot
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6. Data Sources (PORTAL)
PORTAL: Vehicle Miles Traveled
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6. Data Sources (PORTAL)
PORTAL: Weather
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7. Study Scope
Task Task Description Timeline /
Duration
1. Literature Review
- Past field testing results
- Past evaluation methods
2. Field Reconnaissance and Data Collection
- Compile relevant data- Field reviews of ramp metering corridors
3. Select Study Corridors
- Pilot study corridor- Regional study corridors for complete analysis
4. Experimental Design
- Data collection plan for pilot study
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7. Study Scope (con’t)
Task Task Description Timeline /
Duration
5. Pilot Study - Analysis of “before” and “after” data
- Design modification for regional study
6. Regional Corridor Study
- Perform larger corridor analyses
7. Evaluation and Recommendations
- Evaluation of benefits of new ramp metering
- Recommendations for improved strategy
8. Final Report