April 18, 2006 Dr. Robert Bertini Dr. Sue Ahn Dr. Chris Monsere Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System

Presentation Outline 1. Literature Review 2. Corridor Selection for Pilot Study 3. Data Collection Plan

1. Literature Review

System-wide Adaptive Ramp Metering Competitive, Traffic-Responsive Algorithm SWARM 1: Global control Forecasts density at a bottleneck and determines the required volume reduction for 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  Actual rates: more restrictive of the two rates

System-wide Adaptive Ramp Metering 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

SWARM Field Testing Orange County, CA (MacCarley et al., 2000) The field-testing could not be conducted because the SWARM algorithm did not operate correctly when tested for six weeks. Caltrans did not receive proper training nor documentation to fully understand the SWARM system.  Testing via Paramics (Zhang et al., 2001)  SWARM was sensitive to the accuracy of the predictions

SWARM Field Testing LA and Ventura Counties, CA (Pham et al., 2002) 1,200 ramp-meters in the region Existing ramp-metering: pre-timed, local traffic-responsive Tested on 20 controlled on-ramps on a freeway corridor (Route 210 W) during morning peaks (between September 2001 and January 2002) A couple of days for each of the following strategies SWARM 1 only: testing the global control only SWARM 2b only: testing the local control only SWARM 1/2b combined: testing the global and local controls

SWARM Field Testing LA and Ventura Counties, CA (Pham et al., 2002) Results: SWARM 1/2b generated most benefits Mainline Freeway: Speed increased by 11% Travel time decreased by 14% Occupancy decreased by 13% Delay decreased by 17% Volume increased by 1%

SWARM Field Testing LA and Ventura Counties, CA (Pham et al., 2002) On-ramps (at the nine busiest locations): Volume decreased by 9% Queue lengths increased by 41% Limitations: Small sample size (only a couple of days of testing) No analysis on spatial equity No analysis on traffic diversion to alternative routes No analysis on change in the distribution of inflows

SWARM Field Testing Some Future Plans LA and Ventura Counties –Ramp meter development plan in 2005 –More testing of SWARM Fresno County (District 6) –Proposal for a pilot study Orange County (District 12) –No updated status yet

Other Traffic-Responsive Algorithm

Field Testing: ZONE Algorithm Shut-off Experiment in Minneapolis, MN (Fall of 2000)  The meters were shut off for eight weeks. Conditions on mainline freeway Traffic diversion to alternative routes Change in travel behavior On-ramp queue lengths Cambridge Systematics, 2001 Kwon et al., 2001 Hourdakis and Michaelopoulos, 2002 Levinson and Zhang, 2002

Field Testing: ZONE Algorithm Shut-off Experiment in Minneapolis, MN (Fall of 2000)  The meters were shut off for eight weeks. Results: (Cambridge Systematics 2001, etc.) –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. –Ramp-metering resulted in more delay on on-ramps but generated system-wide delay.

Field Testing: ZONE Algorithm Shut-off Experiment in Minneapolis, MN (Fall of 2000)  The meters were shut off for eight weeks. Levinson and Zhang (2002) Evaluated the system with seven measures: (mobility, accessibility, equity, consumers’ surplus, travel time variation, productivity, and travel demand responses) Favorable results for ramp-metering Equity analysis: worse off with ramp-metering  travel time for short trips increased while the travel time for longer trips decreased.

Other Field Testing Helper Algorithm: Denver, CO Bottleneck Algorithm: Seattle, WA Fuzzy Logic Algorithm: Seattle, WA / Zoetermeer, Netherlands  Similar Results (Favorable to ramp-metering)

2. Corridor Selection

Freeway Network in Portland source:

SWARM Implementation Schedule CORRIDORSCHEDULE I-205 NBDecember 2005 I-205 SBDecember 2005 I-405 NBApril 2006 I-405 SBApril 2006 I-5 Lower (NB and SB)February 2006 I-5 Upper (NB and SB)January 2006 I-84EBMay 2005 I-84WBMay 2005 OR 217 NBLate November 2005 OR 217 SBEarly November 2005 US26 EBMarch 2006 US26 WBMarch 2006

Corridor Selection Criteria 1) Level of congestion Duration and spatial extent of congestion should be reasonably large (i.e., no localized queue). Assessment of the SWARM performance while SWARM 1 mode (global control) interacts with SWARM 2 modes (local control) at multiple on-ramp locations. 2)Spatial extent of queues: Isolated queues within a corridor The head and tail of a queue should reside within a corridor. This ensures a system-wide evaluation of the SWARM system within corridor without having to evaluate other intersecting freeways simultaneously.

Corridor Selection Criteria 3)Coverage of Loop detectors Good coverage over the entire corridor Reasonable spacing between loop detectors (≈1 mile) 4)Data quality Corridors for the pilot study will be selected based on their history of data quality Rare communication failure Large proportion of “good” readings = (No activity + OK + Suspects) / all readings

Corridor Selection Criteria 5)Feasibility of analyzing traffic diversion This requires identifying possible alternative routes and obtaining necessary data to measure traffic diversion. Feasibility of collecting data on all alternative routes should be taken into consideration in selecting a study corridor. 6)Stability of the SWARM system implemented All ramp meters should be working properly No bug in the implemented algorithm: actual metering rates = theoretical rates

Corridor Selection Criteria 7)Construction schedule Exclude corridors that are scheduled for construction. Excluded corridors will be re-considered for the regional-level evaluations. 8)Presence of HOV lane or transit service Changes in demand for a HOV lane or transit service on freeways or on alternative routes.  It is highly unlikely that people change their travel behavior in the short run.

Corridors NOT recommended US-26 (East and Westbound) Under major construction to expand a travel lane. May be considered for the regional-level evaluation. I-84 (East and Westbound) EB: Only five loop detector stations, spanning less than 4 miles. WB: Four loop detector stations, spanning 3 miles, and the 5 th one is located 10 miles upstream. Queues are not isolated within the section. Only two or three on-ramps are metered on this corridor.

Speed Contour: I-84 EB Morrison Bridge 60 th St.

Corridors NOT recommended I-405 (Northbound) Relatively short (≈ 3.5 miles). Only two loop detector stations, covering less than 1 mile. Queues are not isolated. I-405 (Southbound) Relatively short (≈ 3.5 miles). Lightly congested during the peaks with a small queue (≈ 1.5 mile). The head of a queue is located at the most downstream end.

Speed Contour: I-405 SB Count Station Front Ave.

Corridors NOT recommended I-205 Southbound This corridor is lightly congested during the peaks. (speed > 40 mph throughout the whole corridor) OR 217 Northbound Lightly congested during the peaks (speed > 45 mph). Queue are not isolated: A queue forms near the junction with 99W and spills-over to I-5.

Speed Contour: I-205 SB Stafford Airport Way

Speed Contour: OR 217 NB Walker 72 nd 99W

Corridors NOT recommended I-5 Upper Northbound A major bottleneck at the South end of the Interstate Bridge?  This could not be verified since no loop detector data are received downstream of the bridge (Vancouver, WA). History of communication failure HOV lane

Speed Contour: I-5 Upper NB Willsonville Jantzen Beach upper

Corridors NOT recommended I-5 Upper Southbound Queues are not isolated: a queue forms near Columbia Blvd. and spills-over to Vancouver, WA. I-5 Lower Southbound AM and PM peaks: usually a small queue from near the Wheeler Ave. on-ramp (≈2 – 3 miles) AM peak: often overrides the upstream bottleneck near Columbia Blvd. and the resulting queue propagates to WA Large spacing between Wheeler and Hood Ave. (> 2 miles)

Speed Contour: I-5 SB Nyberg Jantzen Beach upper lower Columbia Wheeler Hood

SWARM Implementation Schedule CORRIDOR I-205 NB I-205 SB I-405 NB I-405 SB I-5 Lower (NB and SB) I-5 Upper (NB and SB) I-84EB I-84WB OR 217 NB OR 217 SB US26 EB US26 WB

Candidate Corridors: OR 217 SB source:

Candidate Corridors: OR 217 SB Barnes 72 nd Greenburg Denney

Candidate Corridors: OR 217 SB OR 217 Southbound Good amount of congestion in the morning and evening Few alternative routes Good history of loop detector data quality (> 95%) Good detector spacing (good coverage) (< 1 mile) No transit service or HOV lane on the freeway Questions/Issues: Operation hours: 6 AM – 10 AM? Isolated queue  ? (spill-over from I-5S?) Transit service on alternative routes?  Any construction scheduled?

Candidate Corridors: I-5 Lower NB source:

Speed Contour: I-5 Lower NB Willsonville Jantzen Beach lower Morrison Macadam

Candidate Corridors: I-5 Lower NB I-5 Lower Northbound Good amount of congestion in the evening Isolated queues No HOV lane on the freeway Questions Operating hours: 6 – 10 AM, 1 – 7 PM? Any construction scheduled? Transit service on freeway: ridership data? How about on alternative routes? 

Candidate Corridors I-5 Lower Northbound Issues: Several alternative routes One segment with detector spacing > 2 miles (between Macadam Ave. and Bertha Ave.): any device? Data quality (% good readings < 95%) –April 2006: Terwilliger Blvd NB (42%), Morrison BR (~90%), Broadway (74%), Alberta St. (68%) –March 2006: Pacific Hwy (23%), Broadway (82%), Alberta St. (53%) –February 2006: Pacific Hwy (0%), Broadway (88%), Alberta St. (75%)

Candidate Corridors: I-205 NB source:

Speed Contour: I-5 NB Stafford Division Powell OR 43

Candidate Corridors I-205 Northbound Good amount of traffic in the morning and evening Good data quality Two recurrent bottlenecks (one near Division St. and the other near the junction with OR 43) No HOV lane

Candidate Corridors I-205 Northbound Questions/Issues: Construction? Transit service on freeway? Isolated queue? (bottleneck at Powell or Division?) An upstream queue is not usually isolated within this corridor (spill-over to I-5S)

3. Data Collection Plan

Summary of Data Collection Plan EvaluationMeasuresData Sources Freeway Conditions Flow Speed Travel Time Delay Vehicle-Miles-Traveled Vehicle-Hours-Traveled TMOC Loop Detectors (PORTAL) Detection device where spacing is large? On-ramp Conditions Queue Length Delay Compliance Demand TMOC Ramp Detectors Road Tubes CCTV Cameras Field Observation

Summary of Data Collection Plan EvaluationMeasuresData Sources Urban Arterials (Alternative Routes) Travel Time Speed Flow Demand Change Road Tubes Probe Vehicles (Travel Time) Safety Incidents in Study Area – Freeways – On-ramps – Alternative Routes ATMS Incident logs ODOT/PDOT Crash Data Air Quality Fuel Consumption Engine Emission No additional data collection is required.

4. Experimental Design

Candidate Corridors: OR 217 SB Alternative Routes

Candidate Corridors: OR 217 SB Alternative Routes Cedar Hills – SW Hall Blvd. SW Murray Blvd.

Candidate Corridors: OR 217 SB On-ramp volumes and queue storage length

Candidate Corridors: OR 217 SB

Candidate Corridors: I-5 Lower NB Alternative Routes

Candidate Corridors: I-5 Lower NB Alternative Routes

Candidate Corridors: I-5 Lower NB Alternative Routes SW Boones-Ferry Rd. – SW 72 nd Ave. – 99W Boones-Ferry Rd. – SW Terwilliger Blvd. Macadam Ave. (OR 43)

Candidate Corridors: I-5 Lower NB On-ramp volumes and queue storage length