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.

6

MethodologyIncidents Extraction

Traffic Volume Estimation

Diversion Rate Estimation

Detector Data Preprocessing

• K-means clustering method

7

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

8

Example of Clustering Results

Pattern 1 Pattern 2

Pattern 3 Pattern 4 Pattern 5

Pattern 6 Pattern 7

9

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

11

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.

16

Thank You