Utilization of ITS Data to Calibrate Simulation Models

1. Transpo 2012 Utilization of ITS Data to Calibrate Simulation Models Mohammed Hadi, Yan Xiao, Ali Daroodi Lehman Center for Transportation Research Department of Civil and Environmental Engineering Florida International University Miami, FL October 30, 2012

2. Introduction • Microscocopic traffic simulation models allow detailed analysis of traffic operations and alternative capacity improvements and traffic operation analysis • The complexity of simulation modeling increases with the increase in congestion level and advanced strategy modeling • Data from ITS combined with data from other sources allow: • More cost-effective simulation model development • Better calibrated and validated models

3. STRIDE Project • “Investigation of ATDM Strategies to Reduce the Probability of Breakdown” • Joint FIU/UF Project: M. Hadi, L. Elefteriadou, Y. Xiao, C. Letter, A. Darroudi • Investigate the implementation of the breakdown probability to an existing real-world deployment of ramp metering. • Investigate the use of speed harmonization by itself or in combination with the ramp metering implementation • Examine how connected vehicle technologies can be used to support the strategies investigated • Provide guidelines to agencies on how to use simulation models to assess and fine-tune ATDM strategies of the types investigated in the project

4. Objectives • Utilize detailed data to identify the variability of congestion between days • How similar are different days • Identify day(s)/congestion levels for use in the analysis • Examine variation in breakdown attributes between days and associate these attributes with congestion levels • Examine the use of new attributes (based on breakdown and queuing) for calibrating simulation models for use in modeling advanced strategies

5. Simulation Model Calibration • Guidelines have been produced for calibration and use of simulation models • FHWA Traffic Analysis Toolbox (TAT) Volumes 3 and 4 are examples • Calibration data has consisted of measures of capacity; traffic counts; and measures of system performance such as travel times; speeds, delays, and queues • TAT specifies that system performance data (travel times, delays, queues, speeds) must be gathered simultaneously with the traffic counts

6. Multi-Scenario Modeling • Multi-scenario analysis for “normal”, incidents, special events, weather has been proposed in recent years. • Range of “normal” conditions considered – low, median, and heavy based on VMT

7. Speed Contour Plot (16 days) Based on Similarity CORSIM Results Mean 11/11/2010 10/6/2010 Median 5/12/2010 11/18/2010 Less Congestion More Congestion 4/26/2011

8. Similarity Based on Euclidean Distance: 16 Days Distance Results Based on Speed

9. Speed Contour Plot (7 days) Based on Similarity of Speed CORSIM Results Mean 5/12/2010 11/30/2010 Median 6/17/2010 Less Congestion 2/11/2011 More Congestion 4/26/2011

10. Similarity Based on Euclidean Distance: 7 Days Distance Results Based on Speed Only

11. Speed Contour Plot (7 days) Based on Similarity of Speed and Volume CORSIM Results Mean 5/12/2010 2/11/2011 Median 6/17/2010 3/15/2011 Less Congestion More Congestion 4/26/2011

12. Similarity Based on Euclidean Distance: 7 Days Distance Results Based on Speed and Volume after Normalization

13. Speed Contour Plot (7 days) Based on Congestion Index 4/26/2011 CORSIM Results 5/12/2010 11/30/2010 Median 6/17/2010 Less Congestion 2/11/2011 More Congestion Mean

14. Congestion Index (7 days):

15. Capacity, Breakdown Flow, and Queue Discharge

16. Congestion/Breakdown Attributes • Congestion Index • Speed before breakdown (mph) • Average Speed of breakdown (mph) • Speed Reduction due to beakdown (mph) • Starting time • Duration (hr:min) • Maximum pre-Breakdown Flow upstream and downstream of ramp (veh/hr/lane) • Breakdown Flow (veh/hr/lane) • Queue Discharge (veh/hr/lane) • Recovery Flow (veh/hr/lane) • Queue Build-up Rate and Queue Dissipation Rate

17. Breakdown Analysis Results:

18. Thank You ?