November 15, 2005

Dr. Robert BertiniDr. Sue Ahn

Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System

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)

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

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.

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

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.)

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

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

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

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

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

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.

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

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

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

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:

6. Data Sources (con’t)

Inductive Loop Detectors Closed-Circuit Television Cameras

Data Archived in Portal

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

6. Data Sources (PORTAL)

Portland Transportation Archive Listing (PORTAL)PSU Designated as Regional Archive Center

6. Data Sources (PORTAL)

PORTAL: Speed Contour Plot

6. Data Sources (PORTAL)

PORTAL: Vehicle Miles Traveled

6. Data Sources (PORTAL)

PORTAL: Weather

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

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