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

Team Members: Faculty Mentor:

Isaac Quaye Dr. Heng Wei

Junior GRA:

Emma Hand Kartheek K. Allam Sophomore

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

• Simulation and progress

• 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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Why Ramp Meters?Why Ramp Meters?

• Reduce congestion

• Improve throughput (up to 62%)– Decrease in time spent staring at break lights

• Reduce travel time (20-61%)

• Improve travel time reliability

• Ensuring 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

– Isolated

– Coordinated

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Meters Across the USMeters Across the US

Seattle: 232

Portland: 110

LA: 1478

Phoenix: 122

Salt LakeCity: 23

Denver: 46

Arlington: 5

Minn-St. Paul: 444

Milwaukee: 122

Chicago: 117 New York: 75

N. Virginia: 26

Implemented - Responsive

In Progress - Responsive

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In Progress - Fixed

Ohio: 34

Scope of StudyScope of Study

• Conducting research on the study site (I-275) by gathering data using traffic counter and GPS device

• 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

• Analyzing traffic during the peak hours• Investigating and observing both a single and two lane ramp

implementation in VISSIM

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GoalsGoals

• Investigate

– Effectiveness of ramp implementation

– One or two lane ramp metering

• Successfully run simulations in VISSIM

• Present and complete deliverables

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TasksTasks

• Generate VISSIM network model using processed data

• Analyze results

• Assemble research findings

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TrainingTraining

• GPS and traffic counting

• VISSIM Software

– Simulation set up

– Data input and analysis

– Calibration

– Validation

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Data CollectionData Collection

I-275

Mosteller RoadReed Hartman

Highway

Study Site

LegendLegendEast-Bound East-Bound

SectionsSectionsWest-Bound West-Bound

SectionsSections

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

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

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Traffic VideoTraffic Video

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

List of Video Completed

Date Video Name LocationStudent Collected Count of videos Total

        Cars Trucks  

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

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QTravel QTravel

MethodologyMethodology19

VISSIM TrainingVISSIM Training

Simulation Setup

Simulation Setup

Run Simulation

Run Simulation ResultsResults

One Lane Ramp

One Lane Ramp

Two Lane Ramp

Two Lane Ramp

ValidationValidationCalibrationCalibration

SimulationSimulation

• Calibration– Desired speeds

– Routing decisions

– Driving behavior

• Validation– Speed (+ 10%)

– Travel Time (+ 15%)

– Volume (GEH Statistic)

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ProgressProgress

• Post-Processing data collected

– Analyzing the data collected with the GPS and traffic counting device

• VISSIM

– Running simulations

– Calibration and validation

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Progress (cont.)Progress (cont.)

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Network ModelNetwork Model

Progress (cont.)Progress (cont.)

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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        

Completed    

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LegendLegend

CompleteComplete

IncompleteIncomplete

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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