Project 5: Ramp Metering Control in Freeway System

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

2. 2 Sponsor Grant ID No.: DUE – 0756921

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

4. 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 Source: NHTSA

5. 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

6. 6 What can fix this? Ramp Meters Source: Reference 10

7. 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)

8. 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

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

10. 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

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

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

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

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

15. 15 Data Collection (cont.)

16. 16 Data Collection (cont.) Traffic Video

17. 17 Data Collection (cont.) Sample Data

18. 18 Data Collection (cont.) QTravel

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

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

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

22. 22 Progress (cont.) Network Model

23. 23 Progress (cont.)

24. 24 Timeline Legend Complete Incomplete

25. 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.

26. 26 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

27. 27 Questions