1. Transpo 2012 Yan Xiao, Mohammed Hadi, Maria Lucia Rojas Lehman Center for Transportation Research Department of Civil and Environmental Engineering Florida International University Miami, FL October 30, 2012 Estimation of Diversion Rate during Incidents Based on Mainline Detector Data

2. Introduction Literature Review Problem Statement Methodology Methodology Validation and Application Conclusions and Future Work 2 Outline

3. Diversion Rate One of the most important parameters for assessing the impacts and benefits of traveler information system. Diversion rate is needed to assess the impacts on alternative routes, allowing agencies to select better signal control and other traffic management strategies on these routes during incident conditions 3 Introduction

4. 4 Previous Studies Stated Preference Surveys Revealed Preference Surveys Assume Certain Values Diversion Rate Up to 60% - 70% based on SP surveys 27% -50% based on field measurements or RP surveys

5. 5 Problem Statement Rich Intelligent Transportation System (ITS) Data Traffic detector data Incident and construction data Challenge for Estimating Diversion Rate Off-ramp detectors are not installed due to the additional cost involved, preventing the direct measurement of volumes exiting the freeways during incidents. Research Goal Develop and evaluate a method to estimate the diversion rates based on freeway mainline detectors, without requiring measurements from on-ramp and off-ramp detectors.

6. 6 Methodology Incidents Extraction Traffic Volume Estimation Diversion Rate Estimation Detector Data Preprocessing

7. K-means clustering method 7 Traffic Volume Estimation v j (t i ): time series measurement j at time interval i from detector data c k (t i ): centroid of cluster k at time interval i N k : total number of time series in cluster k

8. 8 Example of Clustering Results Pattern 1Pattern 2 Pattern 3Pattern 4Pattern 5 Pattern 6Pattern 7

9. 9 Diversion Rate Estimation

10. Incident Recovery Time Estimation Speeds of neighboring detectors around the incident vs their normal day values Traffic Direction Normal Day Incident Day 10 One-lane blockage Incident Location: I95SB Detected at 9/6/2011 2:18 pm Estimated ending time: 3:05 pm

11. 11 Computer Program

12. 12 Methodology Validation IndexDetected Date Number of Lanes Number of Lanes Blocked Incident 12/28/2012 8:45 AM42 Incident 23/26/2012 4:28 PM42 Incident 37/20/2012 3:06 PM41

13. 13 Methodology Application I95 corridor between the Golden Glades Interchange and SR-836 Study time period: 6:00am-7:00pm on weekdays from Jan. 1, 2011 to June 30, 2011 Number of Lanes Number of Lanes Blocked Average Diversion Rate (%) Sample Size 3114.8128 3210.683 3330.273 4111.0770 4216.8827 4324.617 4434.833 518.6025 529.8718 5317.34

14. 14 Average Diversion Rate Average Diversion Rate vs Lane Blockage Ratio

15. 15 Conclusions and Future Work A new methodology was developed to estimate the diversion rate during the incidents based on mainline traffic detector data. The validity of developed methodology was verified by comparing the estimated values with real-world data. Case study results indicate that the average diversion rate is about 10%-35% for 3-lane and 4-lane roadways depending on number of lanes blocked. A linear relationship between average diversion rate and lane blockage ratio was also developed The impacts of incident attributes, such as incident duration and time of occurrence, and traffic parameters, such as congestion levels on the corridor and alternative routes, will be further investigated.

16. 16 Thank You