Transpo 2012
Yan Xiao, Mohammed Hadi, Maria Lucia Rojas
Lehman Center for Transportation ResearchDepartment of Civil and Environmental Engineering
Florida International UniversityMiami, FL
October 30, 2012
Estimation of Diversion Rate during Incidents Based on Mainline Detector
Data
• Introduction• Literature Review• Problem Statement• Methodology• Methodology Validation and Application• Conclusions and Future Work
2
Outline
• 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
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
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.
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MethodologyIncidents Extraction
Traffic Volume Estimation
Diversion Rate Estimation
Detector Data Preprocessing
• K-means clustering method
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Traffic Volume Estimation
i
ikijkj tctvcvdist 2))()((),((
kjtvn
tcj
ijk
ik ,)(1)(
• 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
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Example of Clustering Results
Pattern 1 Pattern 2
Pattern 3 Pattern 4 Pattern 5
Pattern 6 Pattern 7
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Diversion Rate Estimation
100
tt
tiijN
tt
tiijI
tt
tiijN
V
VVD
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
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Computer Program
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Methodology ValidationIndex Detected Date Number of
LanesNumber of
Lanes Blocked
Incident 1 2/28/2012 8:45 AM 4 2
Incident 2 3/26/2012 4:28 PM 4 2
Incident 3 7/20/2012 3:06 PM 4 1
0%
5%
10%
15%
20%
25%
Incident 1 Incident 2 Incident 3
Div
ersi
on R
ate
Incidents
Actual ValueEstimated Value
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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, 2011Number of Lanes Number of Lanes
BlockedAverage Diversion
Rate (%) Sample Size
3 1 14.81 28
3 2 10.68 3
3 3 30.27 3
4 1 11.07 70
4 2 16.88 27
4 3 24.61 7
4 4 34.83 3
5 1 8.60 25
5 2 9.87 18
5 3 17.3 4
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Average Diversion Rate• Average Diversion Rate vs Lane Blockage Ratio
RD 949.335%
10%
15%
20%
25%
30%
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Ave
rage
Div
ersi
on R
ate
Lane Blockage Ratio
R-Square=0.8
Estimated ValueFitted Value
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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.
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Thank You