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

2. Sponsor 2

3. Outline 3 Introduction Scope of study, goals and tasks Training Data Collection Methodology Simulation and progress 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. 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) 7

8. 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 –Isolated –Coordinated 8

9. Meters Across the US Seattle: 232 Portland: 110 LA: 1478 Phoenix: 122 Salt Lake City: 23 Denver: 46 Arlington: 5 Minn-St. Paul: 444 Milwaukee: 122 Chicago: 117 New York: 75 N. Virginia: 26 Implemented - Responsive In Progress - Responsive 9 In Progress - Fixed Ohio: 34

10. Scope 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 10

11. Goals Investigate –Effectiveness of ramp implementation –One or two lane ramp metering Successfully run simulations in VISSIM Present and complete deliverables 11

12. Tasks Generate VISSIM network model using processed data Analyze results Assemble research findings 12

13. Training GPS and traffic counting VISSIM Software –Simulation set up –Data input and analysis –Calibration –Validation 13

14. 14 Data Collection I-275 Mosteller Road Reed Hartman Highway Study Site Legend East-Bound Sections West-Bound Sections

15. Data Collection (cont.) 15

16. Data Collection (cont.) 16 Traffic Video

17. Data Collection (cont.) 17 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 List of Video Completed DateVideo NameLocation Student CollectedCount of videosTotal CarsTrucks

18. Data Collection (cont.) 18 QTravel

19. Methodology 19 VISSIM Training Simulation Setup Run Simulation Results One Lane Ramp Two Lane Ramp Validation Calibration

20. Simulation –Desired speeds –Routing decisions –Driving behavior Validation –Speed (+ 10%) –Travel Time (+ 15%) –Volume (GEH Statistic) 20

21. Progress Post-Processing data collected –Analyzing the data collected with the GPS and traffic counting device VISSIM –Running simulations –Calibration and validation 21

22. Progress (cont.) 22 Network Model

23. Progress (cont.) 23

24. 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 Completed 24LegendComplete Incomplete

25. 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. 25

26. 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 26

27. Questions 27