Bottleneck Identification and Calibration for Corridor Management Planning

1. Bottleneck Identification and Calibration for Corridor Management Planning Xuegang (Jeff) Ban Lianyu Chu Hamed Benouar California Center for Innovative Transportation (CCIT) University of California – Berkeley January 22, 2007

2. Outline • Introduction • Bottleneck Identification • Bottleneck Calibration • A Real World Example • Concluding Remarks

3. Introduction • Corridor Management • Corridor Management Planning • Integrated Corridor Management • Micro-simulation in Corridor Management • Performance Evaluation • Improvement Scenario Evaluations • Bottleneck Analysis • Definition: Locations that capacity less or demand greater than other locations. • Identification: Queue length and duration • Calibration in Micro-simulation

4. Bottleneck Identification • Current Practice • HICOMP, PeMS • Proposed Method • Binary Speed Contour Map (BSCM) via Percentile Speeds • Assumption: bottleneck area if v<=vth • Why are Percentile Speeds? • Probability of a location being a bottleneck • Flexibility of identifying bottlenecks • Reliability compared with single “typical” day or average speeds p-th percentile speed

5. Bottleneck Identification (Cont.) • Speed Contour Map • Represented as S(i, t) Incident Average 15% No-Incident 50% 85%

6. Bottleneck Identification (Cont.) • Binary Speed Contour Map (BSCM) BS(i, t) = 1, if S(i, t) <= vth, 0, otherwise • Bottleneck(s) can be identified automatically via BSCM Vth = 35mph

7. Bottleneck Calibration • Current Practice • FHWA Micro-Simulation Guideline: Visual Assessment • Proposed Method - A Three Step-Process • 1. Visual Assessment • 2. Area Matching • 3. Actual Speed Matching • Three Levels of Details for Calibrating Bottlenecks

8. Step 1. Visual Assessment • Purpose • Make sure the number of bottlenecks, their locations and areas roughly match • Qualitative and no quantitative measures can be defined Observed Data Simulation Data

9. Step 2: Bottleneck Area Matching • Purpose • Match bottleneck locations and areas using BSCMs • Quantitative Measure C1 • Area Matching Criteria: C1 = 90.5% Union Area Overlapping Area

10. Step 3: Actual Speeds Matching • Purpose • Match Detailed Bottleneck Speeds using both SCMs and BSCMs • Quantitative Measure C2 • Actual Speed Matching Criteria: Observed Data Simulation Data Union Area C2 = 64.2%

11. A Real World Example • I-880 in the San Francisco Bay Area • One of the series of studies for Corridor Management Planning • On-going project and the results presented here are interim • The Example • I-880 NB, AM Peak hours (6:30 AM – 9:30 AM) • Observed data: 20 typical weekdays (Tuesday – Thursday) • Double loop detectors with spacing ¼ mile • Simulation Tool • Paramics

12. The Study Area

13. Calibration Results – Flow and Travel Time • Calibration is satisfactory for matching flow and travel times

14. Calibration Results – Bottlenecks • Bottlenecks? Observed Data Simulation Data

15. Calibration Results – Bottlenecks • Bottlenecks? C1= 24.2%, C2 =42.5% Simulation Data Observed Data

16. Concluding Remarks • Conclusions • Percentile speeds was used to conduct bottleneck analysis • Proposed an automatic bottleneck identification method based on binary speed contour maps • Developed a three-step process for bottleneck calibration: visual assessment, area matching, and actual speed matching • Defined quantitative measures for bottleneck calibration • Enhancement to current micro-simulation calibration practice • Future Study • Using data from single loops (occupancy) • Procedure for calibration