Estimation of Diversion Rate during Incidents Based on Mainline Detector Data

1. Transpo 2012 Estimation of Diversion Rate during Incidents Based on Mainline Detector Data 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

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

3. Introduction • 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 Diversion

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

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

7. Traffic Volume Estimation • K-means clustering method • vj(ti): time series measurement j at time interval i from detector data • ck(ti): centroid of cluster k at time interval i • Nk: total number of time series in cluster k

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

9. Diversion Rate Estimation

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

11. Computer Program

12. Methodology Validation

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

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

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. Thank You ?