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
1
SponsorSponsor
2
OutlineOutline
3
• 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
4
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
5
Ramp Ramp
MetersMeters
What can fix this?What can fix this?
Source: Reference 10
6
What are Ramp Meters?What are Ramp Meters?
7
• 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)
8
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
9
Metering Signal
Arterial
10
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
11
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
12
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
13
TasksTasks
• Equipment and software training
• Utilized GPS software (QTravel)
• Generated VISSIM network model using processed data
• Analysis of simulation results
• Assembled research findings
14
TrainingTraining
• GPS and traffic counting
• QTravel– Extracted data collected from field trips
• VISSIM Software– Simulation set up
– Data input and analysis
– Calibration and validation
15
16
Data CollectionData Collection
I-275
Mosteller RoadReed Hartman
Highway
Study Site
LegendLegend
East-Bound East-Bound SectionsSections
Data Collection (cont.)Data Collection (cont.)
17
Data Collection (cont.)Data Collection (cont.)
18
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.)
19
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
21
Network ModelNetwork Model
Calibration and ValidationCalibration and Validation
• Calibration– Desired speeds
– Routing decisions
– Driving behavior
• Validation– Speed (+ 10%)
– Travel Time (+ 15%)
– Volume
22
Calibration and Validation (cont.)Calibration and Validation (cont.)
23
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
24
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.)
25
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.)
26
Results (cont.)Results (cont.)
27
• Decrease in standard deviation• MZ = Merge Zone• NMZ = Non-Merge Zone
Results (cont.)Results (cont.)
28
• Increase in Speed• MZ = Merge Zone• NMZ = Non-Merge Zone
Results (cont.)Results (cont.)
29
ConclusionConclusion
30
• 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
31
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.
32
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
33
QuestionsQuestions34