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<=v th  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 No-Incident 15% 50%85%

6 Bottleneck Identification (Cont.)  Binary Speed Contour Map (BSCM) BS(i, t) = 1, if S(i, t) <= v th, 0, otherwise  Bottleneck(s) can be identified automatically via BSCM V th = 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 C 1  Area Matching Criteria: Overlapping Area Union Area C1 = 90.5%

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

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% Observed Data Simulation 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