Evaluation of NDOR’s Active Advance Warning System

Laurence R. Rilett Ph.D., P.E.University of Nebraska-Lincoln

Presentation Outline

• Background• Analyses

– Safety– Operation– Simulation– Sensitivity

• Conclusions• Recommendations

Background• Dilemma Zone

– At the legal speed limit, the driver can neither clear the intersection before the end of the intergreen period nor stop without entering the intersection.

Background• Dilemma Zone: NDOR 2002 Report

– “Length of roadway in advance of the intersection wherein drivers may be indecisive or respond differently to the onset of the yellow indication.”

– Also known as “option zone” or “zone of indecision”

Background

• If an intersection is designed correctly (e.g. NDOR) a dilemma zone will not exist– Assuming deterministic system

• Vehicles same characteristics (accelerate, decelerate, weather, etc.)– Trucks/braking

• Drivers make the correct decisions– Stop, proceed

• Assuming: legal maneuvers (not running red light)

Potential Problems

• A major safety concern at high speed signalized intersections

Common Treatments

• Advance Warning (AW) Flashers– Flashing signal heads and warning signs

• Activated at predetermined time before end of green

• “Mixed” results regarding effectiveness

Common Treatments• Advance Detection (AD)

– Series of detectors in advance of intersection• Extend green on detection

– Effective in reducing crashes and conflicts– Increases likelihood of extending green to maximum

(max-out)• Dilemma zone protection is lost

NDOR’s Actuated Advance Warning (AAW) System • Combines advance detection and advance warning

– Single detector– Shorter maximum allowable headway– Lower frequency of max-out

Issues

• Results positive but mostly anecdotal• Guidelines for installation

– When do they need to be removed (if ever)?• Motivation for study

Part 1

Crash Data Analyses

Safety Effectiveness

• Test Sites– 26 treated intersections– 29 reference intersections

• “Similar” characteristics as treated intersections

• Provided by NDOR– 13 year of crash counts and AADT

• 1996-2008

Treated Intersections: Table 2.2

Simple example ignores regression to mean, changes in AADT…Need to compare to untreated intersections…

Safety Effectiveness

• Method– Full Bayes– Accounts for uncertainty in data– Generates a distribution of likely expected number of

crashes– Combines this distribution with site-specific crash

data to obtain expected crash frequency– Approach is complex but requires less data

Safety Effectiveness

• Crash Reduction Rate

Safety Effectiveness

• Model

Safety Effectiveness Results

Safety Effectiveness Results

Safety Effectiveness Results

Safety Effectiveness Results

Safety Effectiveness Results

Safety Effectiveness Results

Safety Effectiveness Results

Safety Effectiveness Results

Part 2

Operational Analyses

Operational Analyses

• Main Characteristics– Approach speeds– Acceleration/deceleration characteristics

• Following onset of yellow• During lead flash

– Frequency of max-outs– Rate of dilemma zone “entrapment”– Waiting time on conflicting phases

Study Site: Lincoln

• Highway 77 and Saltillo Road

Study Site: Omaha

• Highway 370 and N 132nd Street

Operational Analyses

• Data

Operational Analyses

• Max-out probabilities

Operational Analyses

• Waiting time on minor road

Operational Analyses

• Waiting time on minor road

Lincoln (Figure 3.13)

• Acceleration/deceleration- lead flash

Omaha

• Acceleration/deceleration- lead flash

Operational Analyses

• (Average) speed profile- lead flash

Operational Analyses

• Acceleration/deceleration- yellow

Operational Analyses • Acceleration/deceleration- yellow

Operational Analyses

• (Average) speed profile- yellow

Operational Analyses

• Vehicles in dilemma zone- yellow

Part 3

Microsimulation Analyses

Microsimulation Model

• VISSIM– Inputs: geometry, traffic counts, timing, speeds, etc.

• Calibration– Adjust model parameters such that field data

“matches” simulated data– Measures of performance

• Average waiting time• Speed profile

Microsimulation Model • GA Calibration Procedure

Microsimulation Model

• Calibration Results

Microsimulation Model

• Measures of performance– Average waiting time

Microsimulation Model

• Measures of performance– Speed profile

Sensitivity Analysis • Experimental Design

Sensitivity Analysis

• Simulation runs– 480 total factor combinations– 1-hour simulation run for each– 10 replications each

• Output– Waiting times– Number of conflicts

Sensitivity Analysis

• Effect of turn percentage– On average waiting times

Conclusions

• Safety effects– Greater than 90% probability that installation of

system is beneficial• Operational effects

– Lower than expected number of vehicles in dilemma zone

– Low max-out probabilities– System seems to work well

Conclusions

• Simulation model– Developed framework for modeling system– Successfully applied to two sites

• Sensitivity analysis – Site specific– Can be used to perform sensitivity analyses

Recommendations

• System worth considering at other high-speed signalized intersections– From a safety perspective

• Guidelines regarding installation– McCoy and Pesti (2002)

• Guidelines regarding removal– Simulation study

• Max out, delay, etc.

Slide design © 2009, Mid-America Transportation Center. All rights reserved.

Dr. Laurence Rilett, Ph.D., P.E. University of Nebraska-Lincoln

CREDITS