project 5: ramp metering control in freeway system

Project 5: Ramp Metering Control in Project 5: Ramp Metering Control in Freeway SystemFreeway System

Team Members: Faculty Mentor:

Emma Hand Dr. Heng Wei

Sophmore GRA:

Isaac Quaye Kartheek K. Allam Junior

Jared SagagaJunior

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SponsorSponsor

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OutlineOutline

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• Introduction

• Scope of study, goals and tasks

• Training

• Data Collection

• Methodology

• Results

• Timeline

National StatisticsNational Statistics

• Average time spent on highway (NHTSA 2009)– Student: 1.3 hours/day

– Working: 1.5 hours/day

– 36 hours/year in traffic

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Source: NHTSA

National Statistics (cont.)National Statistics (cont.)

• 32,885 people died in motor vehicle traffic crashes in 2010 (NHTSA)– 5,419,000 total crashes on highway, 29% caused injury or were fatal

• 33% crashes occur on freeway stretch with bridges or interchanges (2011)

• $871 BILLION in economic loss and societal harm

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Ramp Ramp

MetersMeters

What can fix this?What can fix this?

Source: Reference 10

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What are Ramp Meters?What are Ramp Meters?

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• Traffic controls that regulate traffic flow entering a highway

Source: Reference 6

Why Ramp Meters?Why Ramp Meters?

• Reduces congestion

• Improves throughput (up to 62%)– Decreases time spent staring at brake lights

• Reduces travel time (20-61%)

• Improves travel time reliability

• Ensures the safety of vehicles (5-43% decrease in accidents)

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Types of Ramp MeteringTypes of Ramp Metering

• Fixed time– Pre-timed meter cycle based off of past data

• Responsive– Meter cycles vary depending on changes in traffic conditions

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Metering Signal

Arterial

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Signal Controller

Ramp Metering SystemRamp Metering System

Meters Across the USMeters Across the US

Seattle: 232

Oregon: 150

California: 3471

Phoenix: 233

Salt LakeCity: 23

Denver: 54

Texas: 115

Minn-St. Paul: 444

Wisconsin: 80

Chicago: 117 New York: 75

N. Virginia: 26

Implemented - Responsive

In Progress - Responsive

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In Progress - Fixed

Ohio: 34

Atlanta: 170

Wisconsin: 38

Washington D.C.: 24

Florida: 22

St. Louis: 1

Scope of StudyScope of Study

• Conducted research on the study site (East-Bound I-275) by gathering data using traffic counter and GPS travel data logger

• Criteria – Elevated locations nearby for placing the camcorder to capture the

traffic– Location should be busier in the peak hours than the normal flow of

freeway

• Investigated both a one and two lane ramp implementation in VISSIM

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GoalsGoals

• Gain background knowledge and training for research project

• Collect and process data from GPS data logger and traffic counter

• Investigate– Effectiveness of one and two lane ramp implementation

• Successfully run simulations in VISSIM

• Present completed deliverables

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TasksTasks

• Equipment and software training

• Utilized GPS software (QTravel)

• Generated VISSIM network model using processed data

• Analysis of simulation results

• Assembled research findings

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TrainingTraining

• GPS and traffic counting

• QTravel– Extracted data collected from field trips

• VISSIM Software– Simulation set up

– Data input and analysis

– Calibration and validation

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Data CollectionData Collection

I-275

Mosteller RoadReed Hartman

Highway

Study Site

LegendLegend

East-Bound East-Bound SectionsSections

Data Collection (cont.)Data Collection (cont.)

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Data Collection (cont.)Data Collection (cont.)

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Sample DataSample Data

9/16/2013 EB09160653 On-ramp Emma 691 55 746EB09160753 On-ramp Jared 636 83 719EB20130916155957 Freeway Isaac 10960 395 11355EB20130916065028 Freeway Jared 10139 797 10936

9/17/2013 EB201309171622 Freeway Isaac 5337 179 5516EB20130917072223 Freeway Emma 7877 497 8374WB20130917070632 Freeway Jared 9175 659 9834

9/18/2013 EB20130918154910 Freeway Isaac 12514 468 12982  WB09181600 On-ramp Emma 621 23 644

EB20130918065700 Freeway Isaac 11860 630 12490

Date Video Name LocationStudent Collected Cars  Trucks Total

List of Videos Completed

Data Collection (cont.)Data Collection (cont.)

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QTravel QTravel

MethodologyMethodology20

VISSIM TrainingVISSIM Training

Simulation Setup

Simulation Setup

Run Simulation

Run Simulation

ResultsResults

One Lane Ramp

One Lane Ramp

Two Lane Ramp

Two Lane Ramp

ValidationValidation

CalibrationCalibration

Study SiteStudy Site Data Collection

Data Collection

SimulationSimulation

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Network ModelNetwork Model

Calibration and ValidationCalibration and Validation

• Calibration– Desired speeds

– Routing decisions

– Driving behavior

• Validation– Speed (+ 10%)

– Travel Time (+ 15%)

– Volume

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Calibration and Validation (cont.)Calibration and Validation (cont.)

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Travel Time

Simulation Travel Time (sec) Actual Accepted Accepted Result

Time (sec) Cars Trucks Time (sec) Percentage Range (sec)

3600 58.3 67.8 58 + 15% 49.3-66.7

Mean Speed Actual Accepted Accepted Result

Cars TrucksSpeed (mph) Percentage Range

61.5 52.9 65  + 10% 58.5-71.5

PASS

PASS

Speed

ResultsResults

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Simulation Travel Time (sec) Number of Vehicles

Time (sec) Cars Trucks Cars Trucks

3600 59.1 68.6 5489 687

Simulation Travel Time (sec) Number of Vehicles

Time (sec) Cars Trucks Cars Trucks

3600 58.3 67.8 5488 687

One Lane On-Ramp Without Ramp Meter

One Lane On-Ramp With Ramp Meter

Results (cont.)Results (cont.)

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Simulation Travel Time (sec) Number of Vehicles

Time (sec) Cars Trucks Cars Trucks

3600 60 68.8 5485 687

Simulation Travel Time (sec) Number of Vehicles

Time (sec) Cars Trucks Cars Trucks

3600 60.4 69.1 5481 686

Two Lane On-Ramp Without Ramp Meter

Two Lane On-Ramp With Ramp Meter

Results (cont.)Results (cont.)

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Results (cont.)Results (cont.)

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• Decrease in standard deviation• MZ = Merge Zone• NMZ = Non-Merge Zone

Results (cont.)Results (cont.)

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• Increase in Speed• MZ = Merge Zone• NMZ = Non-Merge Zone

Results (cont.)Results (cont.)

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ConclusionConclusion

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• No significant change in overall speed and travel time

• Significant change in sectional average speed and speed variation

• Ramp meters are more effective on two-lane on-ramps in increasing safety

TimelineTimeline

Task Week

1-2 3 4 5 6 7-8

Methods of evaluation and research

Equipment and software training

Data collection and analysis

Use data to develop deliverables

Create and run simulation models

Complete deliverables        

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LegendLegend

CompletCompletee

ReferencesReferences

• Zongzhong, T., Nadeem, A. C., Messer, C. J., Chu, C. (2004). “Ramp Metering Algorithms and Approaches for Texas,” Transportation Technical Report No. FHWA/TX-05/0-4629-1, Texas Transportation Institute, The Texas A&M University System, College Station, Texas.

• Yu, G., Recker, W., Chu, L. (2009). “Integrated Ramp Metering Design and Evaluation Platform with Paramics,” California PATH Research Report No. UCB-ITS-PRR-2009-10, Institution of Transportation Studies, University of California, Berkley, California.

• Kang, S., Gillen, D. (1999). “Assessing the Benefits and Costs of Intelligent Transportation Systems: Ramp Meters,” California PATH Research Report No. UCB-ITS-PRR-99-19, Institution of Transportation Studies, University of California, Berkley, California.

• Arizona Department of Transportation. (2003). Ramp Meter Design, Operations, and Maintenance Guidelines.

• Papamichail I., and Papageorgiou, M. (2008). “Traffic-Responsive Linked Ramp-Metering Control,” IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 1, n.p.

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References (cont.)References (cont.)

• Federal Highway Administration, USDOT (2013). “FHWA Localized Bottleneck Program.” <http://ops.fhwa.dot.gov/bn/resources/case_studies/madison_wi.htm> (Accessed 6/9/2014)

• Maps, Google (2014). <https://www.google.com/maps/search/homewood+suites+near+Hilton+Cincinnati,+OH/@39.2885017,-84.399993,83m/data=!3m1!1e3?hl=en> (Accessed 6/30/2014).

• Maps, Google (2012). <https://www.google.com/maps/@39.288408,-84.399636,3a,75y,243.6h,66.31t/data=!3m4!1e1!3m2!1si7sOFQJVai_eF3v7k8u_LQ!2e0> (Accessed 6/30/2014).

• https://www.fhwa.dot.gov/policy/ohim/hs06/htm/nt5.htm

• http://www-nrd.nhtsa.dot.gov/Pubs/811741.pdf

• http://content.time.com/time/nation/article/0,8599,1909417,00.html

• http://www.academia.edu/2899596/Crashes_and_Effective_Safety_Factors_within_Interchanges_and_Ramps_on_Urban_Freeways_and_Highways

• http://www.fairfield.ca.gov/latest_news/displaynews.asp?NewsID=447

• http://www-nrd.nhtsa.dot.gov/Pubs/811552.pdf

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