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Primary & Secondary Incident Management Dr. Asad Khattak Old Dominion University akhattak@odu.edu Acknowledge: H. Zhang, X. Wang, L. Zheng, K. Yang ITSVA Conference Results are preliminary. Secondary Incidents. HR ~45,000 incidents responded to yearly Secondary incidents-3% to 15%

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Secondary incidents

Primary & Secondary Incident ManagementDr. Asad KhattakOld Dominion Universityakhattak@odu.eduAcknowledge:H. Zhang, X. Wang, L. Zheng, K. YangITSVA ConferenceResults are preliminary


Secondary incidents
Secondary Incidents

  • HR ~45,000 incidents responded to yearly

  • Secondary incidents-3% to 15%

  • 45,000*0.03 = 1350 to 6750

  • Analyzed primary incident durations

  • Analyzed dependence of secondary incident occurrence on primary incident duration


Incident data
Incident Data

  • Provided by Hampton Roads Smart Traffic Center (STC)

  • Vehicle-based record includes 43 variables

  • Archived as Mircrosoft Excel table

  • Jan. 2004 to June 2007

  • Exact position of incidents not available within section


Incidents in hr
Incidents in HR

  • Durations

    • Conversion from a “vehicle file” to “incident file”

    • Duration of incidents is ~14 minutes, on average

  • Incident response times

    • If SSP is detection source (~90%), then zero

    • If non-SSP detection, then ~8 minutes


Secondary incident definition
Secondary Incident Definition

http://www.youtube.com/watch?v=KMwXBWML9E4

S

P


Secondary identification
Secondary Identification

Secondary incident with….

  • Static time/space based method

    • 15 min

    • 1 hour/1 mile, 2 hours/2 miles

  • Queue-based dynamic method

    • Secondary only occurs within incident duration and within queue

    • We used a version of this method

  • Same direction only or both


Some incidents may be missed

C1

C2

Section1

Section 2

Section3

Some incidents may be missed


Issue 1 pair c1 vs c2 c3 c4 or 2 pairs c1 c2 and c3 c4

C3

C4

C2

C1

Section 1

Section 2

Section3

Issue: 1 Pair (C1 vs. C2, C3, C4) or 2 Pairs (C1, C2) and (C3, C4)


Single incident queuing calculation
Single-incident queuing calculation

Maximum Queue length (Qmax ) caused by incident is indicated as green dash line

V = Demand (vph)

C = Capacity (vph)

Ci = Remaining capacity due to incident (vph)

T = Incident duration (hours)

Total vehicle delay (TD) caused by incident is the colored triangular area between the arrival and departure curve


Demand and remaining capacity
Demand and Remaining Capacity

  • AADT% distribution in urban areas is shown

  • Demand V= (AADT%)*AADT

  • Remaining Capacity derived from HCM (2000) Exhibit 22-6 (shown) according to incident severity




Identified secondary incidents
Identified secondary incidents

Two bounds: Rubbernecking effect


Descriptive statistics incident data in hr 2006
Descriptive statistics Incident data in HR (2006)



Descriptive statistic for duration 2006
Descriptive statistic for Duration (2006)

Total incidents = 38614

Normal:37304

Pri:662

Sec:687


Secondary incidents

I- 64

I- 264

564

I- 664

I- 464

Individual and Secondary Incidents

Frequency Distribution (2006)


Distribution density 2006
Distribution Density (2006)

Individual Incident Distribution Density

I- 64

564

I- 264

Secondary Incident Distribution Density

I- 664

I- 64

I- 464

564

I- 264

I- 664

I- 464


Relationship between variables
Relationship between variables

  • Type

  • Detection Source

  • Weather condition

  • Lanes closed?

  • Number of Vehicles involved

  • EMS response?

  • Right shoulder affected?

  • Left shoulder affected?

  • Ramp affected?

  • AADT

  • TOD

Simultaneity

Duration

Sec.

Incident


Regression models
Regression Models

  • Duration Model

    Use linear model

    Duration = b0 + b1 (Detection) + b2 (weather) + b3 (Type) + b4 (Laneclose) + b5 (# of vehicles) + b6(EMS) + b7(AADT) + b8 (Rightshoulder) + b9 (leftshoulder) + b10(ramp)+b11(Resptime) + b12(Primary) + e

  • Secondary Incident Model

    Use Binary Logistic model

    Logit(P(SEC)) = g0 + g1 (Detection) + g2 (weather) + g3 (Type) + g4 (Laneclose) + g5(# of vehicles) + g6(EMS) + g7 (AADT) + g8 (Rightshoulder) + g9 (Leftshoulder) + g10 (ramp) + b11(Resptime) + b12(Duration) + b13(TOD)+ u




Factors associated with duration
Factors associated with Duration:

  • Secondary incident (if occurs, then longer)

  • Response time (the higher, the longer)

  • Detection Source (CCTV, VSP, radio and phone call have longer duration compared with the SSP)

  • Incident Type (Accident longer duration)

  • Severe injuries (EMS responded implies longer)

  • Freeway facility damage-if lane closed, then longer

  • Vehicles number (more, the longer)

  • AADT (more, the longer)


Secondary incident occurrence model
Secondary incident occurrence model

Used all normal incidents and primary incidents.

Not including the secondary incidents since they are highly associated with the primary incidents.

The Pseudo R2 for Log transformed model is higher


Factors associated with secondary incident occurrence
Factors associated with secondaryincident occurrence

  • Primary incident duration (the longer the higher)

  • Happened in peak hours (higher)

  • Freeway facility damage-lane closure

  • Vehicles involved (the more the higher)

  • Higher AADT level (the more the higher)

  • Weather (?)


Secondary incidents

Simultaneity

  • Durations of primary incidents are expected to be longer if secondary incidents occur

  • The secondary incidents are more likely to occur if the primary incident lasts long

  • Calculate the residual variable

  • w = SEC – P(SEC)

  • w is added to the original regression to test for simultaneity

  • The resulting equation is:

  • Duration = b0 + b1 (Detection) + b2 (weather) + b3 (Type) + b4 (Laneclose) + b5 (# of vehicles) + b6(EMS) + b7(AADT) + b8 (Rightshoulder) + b9 (leftshoulder) + b10(ramp)+b11(Resptime) + b12(Primary) + b13(w)


Test simultaneity
Test Simultaneity

Use all “normal” incidents and “primary” incidents. Not including the secondary incidents since they are highly associated with the primary incidents.

Residual is significant


Delay distribution
Delay Distribution

Delay of 163 primary incidents on I-64 WB in 2006

Nearly 80% primary incidents have no delay

Total: 305,195 Vehicle-Hours

Caveat: Method applies in certain conditions


Closure
Closure

  • Work in progress

  • Need to look at “rubbernecking”

  • Clean up the models

  • Use the results for better planning and operations

  • Future work

    • Prediction models

    • Better geo-coded data

  • Contact: akhattak@odu.edu

  • ODUTRI: http://eng.odu.edu/transportation/