1. Project 5: Ramp Metering Control in Freeway System Team Members: Faculty Mentor: Emma Hand Dr. Heng Wei Sophmore GRA: Isaac Quaye Kartheek K. Allam Junior Jared Sagaga Junior 1

2. Sponsor 2

3. Outline 3 Introduction Scope of study, goals and tasks Training Data Collection Methodology Results Timeline

4. National 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

5. 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

6. RampMeters What can fix this? Source: Reference 10 6

7. What are Ramp Meters? 7 Traffic controls that regulate traffic flow entering a highway Source: Reference 6

8. 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

9. Types 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

10. Metering Signal Arterial 10 Signal Controller Ramp Metering System

11. Meters Across the US Seattle: 232 Oregon: 150 California: 3471 Phoenix: 233 Salt Lake City: 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

12. Scope 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

13. Goals 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

14. Tasks Equipment and software training Utilized GPS software (QTravel) Generated VISSIM network model using processed data Analysis of simulation results Assembled research findings 14

15. Training 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. 16 Data Collection I-275 Mosteller Road Reed Hartman Highway Study Site Legend East-Bound Sections

17. Data Collection (cont.) 17

18. Data Collection (cont.) 18 Sample Data 9/16/2013EB09160653On-rampEmma69155746 EB09160753On-rampJared63683719 EB20130916155957FreewayIsaac1096039511355 EB20130916065028FreewayJared1013979710936 9/17/2013EB201309171622FreewayIsaac53371795516 EB20130917072223FreewayEmma78774978374 WB20130917070632FreewayJared91756599834 9/18/2013EB20130918154910FreewayIsaac 12514468 12982 WB09181600On-rampEmma62123644 EB20130918065700FreewayIsaac 11860630 12490 DateVideo NameLocation Student CollectedCars TrucksTotal List of Videos Completed

19. Data Collection (cont.) 19 QTravel

20. Methodology 20 VISSIM Training Simulation Setup Run Simulation Results One Lane Ramp Two Lane Ramp Validation Calibration Study Site Data Collection

21. Simulation 21 Network Model

22. Calibration and Validation Calibration –Desired speeds –Routing decisions –Driving behavior Validation –Speed (+ 10%) –Travel Time (+ 15%) –Volume 22

23. Calibration and Validation (cont.) 23 Travel Time SimulationTravel Time (sec)ActualAccepted Result Time (sec)CarsTrucksTime (sec)PercentageRange (sec) 360058.367.858+ 15%49.3-66.7 Mean SpeedActualAccepted Result CarsTrucks Speed (mph)PercentageRange 61.552.965 + 10%58.5-71.5 PASS Speed

24. Results 24 SimulationTravel Time (sec)Number of Vehicles Time (sec)CarsTrucksCarsTrucks 360059.168.65489687 SimulationTravel Time (sec)Number of Vehicles Time (sec)CarsTrucksCarsTrucks 360058.367.85488687 One Lane On-Ramp Without Ramp Meter One Lane On-Ramp With Ramp Meter

25. Results (cont.) 25 SimulationTravel Time (sec)Number of Vehicles Time (sec)CarsTrucksCarsTrucks 36006068.85485687 SimulationTravel Time (sec)Number of Vehicles Time (sec)CarsTrucksCarsTrucks 360060.469.15481686 Two Lane On-Ramp Without Ramp Meter Two Lane On-Ramp With Ramp Meter

26. Results (cont.) 26

27. Results (cont.) 27 Decrease in standard deviation MZ = Merge Zone NMZ = Non-Merge Zone

28. Results (cont.) 28 Increase in Speed MZ = Merge Zone NMZ = Non-Merge Zone

29. Results (cont.) 29

30. Conclusion 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

31. Timeline Task Week 1-234567-8 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 31LegendComplete

32. References 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

33. References (cont.) Federal Highway Administration, USDOT (2013). “FHWA Localized Bottleneck Program.” (Accessed 6/9/2014) http://ops.fhwa.dot.gov/bn/resources/case_studies/madison_wi.htm Maps, Google (2014). (Accessed 6/30/2014).https://www.google.com/maps/search/homewood+suites+near+Hilton+Cincinnati,+OH/@39.2885017,- 84.399993,83m/data=!3m1!1e3?hl=en Maps, Google (2012). (Accessed 6/30/2014).https://www.google.com/maps/@39.288408,- 84.399636,3a,75y,243.6h,66.31t/data=!3m4!1e1!3m2!1si7sOFQJVai_eF3v7k8u_LQ!2e0 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_Highwayshttp://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

34. Questions 34