Analysis of Driver Behavioral Adaptation to the

Lateral Drift Warning SystemAdam Greenstein, M.S.C.E. Candidate

Graduate Research Assistant, Larson Transportation Institute

Department of Civil and Environmental Engineering

Pennsylvania State University

Transportation Engineering and Safety Conference

December 9, 2009

Co-conspirators:Dr. Paul JovanisDr. Venky ShankarKun-Feng Wu, Ph.D. Candidate

Definitions and Preliminary Discussion

• Lateral Drift Warning (LDW)– Exceed threshold of distance b/w vehicle and lane

centerlines

• Adaptation– Changes in alert frequency over time

• Important Findings– Drivers adapt to LDW alerts by decreasing alert

frequency over time– Males have more substantial decreases than females– Drivers who need more excitement while driving have

more overall alerts and cannot maintain a decrease in frequency over time

Outline

• Introduction

• Data Description

• Hypotheses

• Analyses

• Major Findings, Conclusions

• General ITS and Safety Implications

• Data Limitations, Future Research

Introduction

• Technology – make tasks easier

– Adapt behavior

• Implement ITS technology in vehicles – improve driving experience

– Often for safety

• Limited research with in-vehicle ITS devices

• Continued need to understand adaptation to technology

• New device → must be tested

Data Description

• UMTRI RDCW-FOT (2004)

• 87 drivers

• 4 weeks each

– 1st week – system disabled (pseudo-alerts)

– Weeks 2-4 – LDW alerts provided

• Visual, auditory, haptic

• Looking for changes in alert freq. between weeks

• DAS – roadway and environment information, vehicle kinematics and status (10Hz)

Driver Information andTest-Related Questionnaires

• Gender

• Smoking Habits

• Questions related to crash predisposition

– Sensation-seeking desires

– Risk perception

Modeling Approach - Hypotheses

• 3 possible responses

– 1 - Rely on system – more risky behavior

– 2 – Learn from dangerous situations – drive more carefully

– 3 - Annoyed by alerts – try to reduce alert likelihood

– (2) and (3) should have same outcome – reduce freq.

• Hypotheses

– Alert frequency increases with distance traveled

– Alert frequency decreases by week in study

– Driver descriptors influence adaptation

Count Models

(Count Model)

(Negative Binomial Distribution)

(Washington et al., 2003)

• Aggregate count models– Counts of alerts per week as a function of distance

traveled per week, week in the study, driver attributes

• Segmented count models– By attributes and/or predispositions

– Examine trend differences between driver groups

Count of Alerts Based on Distance Traveled (by week) – All Drivers

Aggregate Model – All Drivers

Variable Coef. SE z P>z

Weekly distance in miles 0.0026 0.0002 14.6 <0.001

Week 2 -0.2703 0.0833 -3.24 0.001

Week 3 -0.3561 0.0872 -4.08 <0.001

Week 4 -0.3869 0.1014 -3.82 <0.001

Constant 0.6963 0.1419 4.91 <0.001

Number of drivers = 71

Count of Alerts Based on Distance Traveled (by week) - Gender

Segmented Model Pair – Gender

Males Females

Variable Coef. SE z P>z Coef. SE z P>z

Weekly dist. in mi. 0.0023 0.0003 8.92 <0.001 0.0030 0.0003 11.39 <0.001

Week 2 -0.3569 0.1239 -2.88 0.004 -0.1087 0.1129 -0.96 0.336

Week 3 -0.4551 0.1323 -3.44 0.001 -0.2143 0.1130 -1.9 0.058

Week 4 -0.5655 0.1605 -3.52 <0.001 -0.1955 0.1274 -1.53 0.125

Constant 0.6392 0.1944 3.29 0.001 0.7736 0.2120 3.65 <0.001

Number of males = 34 Number of females = 37

Count of Alerts Based on Distance Traveled (by week) - Sensation-Seeking Desires

Segmented Model Pair –Sensation-Seeking Desires

Low Sensation-Seeking Desires High Sensation-Seeking Desires

Variable Coef. SE z P>z Coef. SE z P>z

Weekly dist.

in mi. 0.0026 0.0002 11.77 <0.001 0.0025 0.0003 8.72 <0.001

Week 2 -0.2036 0.1167 -1.74 0.081 -0.3563 0.1183 -3.01 0.003

Week 3 -0.2857 0.1240 -2.3 0.021 -0.4403 0.1204 -3.66 <0.001

Week 4 -0.3485 0.1429 -2.44 0.015 -0.4317 0.1429 -3.02 0.003

Constant 0.5022 0.1878 2.67 0.007 0.9701 0.2204 4.4 <0.001

Drivers in “low” group = 40 Divers in “high” group = 31

Major Findings and Conclusions

• General result: decrease alert freq. over time

when controlling for distance traveled

– Supports 1st hypothesis

• Trends vary between driver groups (attributes

and predispositions)

– Supports 2nd hypothesis

• System does improve safety related to ROR crash

risk

ITS and Safety Implications

• In-vehicle vs. infrastructure-based ITS technologies

– Both can improve safety but in different ways

• Implement based on driver differences

– Distributions within population

• Issues with Data

– Accounting for technological skill

– Sample size issues

– Random sampling of drivers

Acknowledgements

• Strategic Highway Research Program

– SHRP2 S-01: Development of Analysis Methods Using Recent Data

• UMTRI staff supplied data

www.trb.org/SHRP2 www.umtri.umich.edu

QUESTIONS?

www.mtc.ca.gov