IDENTIFYING HIGH-RISK ROADWAYS THROUGH JERK-CLUSTER ANALYSIS Seyedeh-Maryam Mousavi, Louisiana State...

IDENTIFYING HIGH-RISK ROADWAYS THROUGH JERK-CLUSTER ANALYSIS

Seyedeh-Maryam Mousavi, Louisiana State University

Scott Parr, California State University - Fullerton

Anurag Pande, California Polytechic State University

Brian Wolshon, Louisiana State University

41st Annual International Forum on Traffic Records and Highway Information SystemsCosta Mesa, CA - November 2015

1

BACKGROUND

Research was initiated as part of a National Science Foundation study to examine stop-and-go, speed, and travel time parameters associated with the Two Fluid Model of traffic flow

GPS data collected to analyze those movements also showed evidence of “abrupt” and “abnormal” driving maneuvers

Based on these observations, it was suggested that there could be a correlation between the areas of “abrupt, atypical” movement and locations of crashes over time

In effect, the idea of this research was is that if we see a fatality in one of every 300 crashes and 1 crash occurs for every 10,000 abrupt maneuvers, we may be able to identify dangerous locations by looking first at locations with very high abrupt maneuvers These locations would be much easier to identify because abrupt maneuvers occur much more

frequently

This could suggest problem areas that could be corrected before crashes, injuries, and fatalities ever occur.

Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement

2

LITERATURE REVIEW

State of practice for identifying high-risk road segments: using long-term historic traffic crash data

However, crashes

Would it be more effective to develop and apply surrogate measures of safety to identify high-risk roadway segments?


1. Are a reactive measure: They can only be used after damage, injury, and loss of life have occurred

2. Might occur due to several reasons

3. May not be recorded uniformly

3

WHAT IS A SURROGATE MEASURE OF SAFETY?

An observable, non-crash event that has a relationship with crashes

Can be observed long before crashes

have occurred

Occur far more frequently than actual crashes; in some cases, 10 and 15 times more

frequently

enabling more powerful statistical analyses to be

applied

Advantages:


4

RESEARCH HYPOTHESIS AND OBJECTIVE

Hypothesis

Locating high concentrations of abnormal negative jerk values

(jerk-clusters: rate of change of acceleration_ ft./s³) would

enable high-risk locations to be identified in advance and

potentially with greater accuracy.

Objective:

Develop measures to proactively identify high-risk roadway

segments.Introduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement

5

METHODOLOGY


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1. Data Collection

GPS data collection and processing, road selection, ADT, curve location

2. Data Processing

Data errors, linear referencing, jerk analysis, crash analysis

3. Sensitivity Analysis

“Appropriate” jerk threshold and segment length

4. Crash Frequency Modeling and Log Likelihood ratio test

Crash intensity v. jerk-clusters v. horizontal curvature, model fitness

1. DATA COLLECTION :GPS DATA LOGGERS

GPS Data Loggers:•Rechargeable battery;

•Recorded data in a comma-separated format;

•Collected: latitude, longitude, altitude, heading, speed, number of satellites utilized, universal time code (UTC) and date, and etc.

•Placed in the center console or glove box of the tested vehicles.

Readings were recorded at a rate of three

hertz .


7

1. DATA COLLECTION :PARTICIPANTS

1) To ensure the consistency of travel over the same routes for developing jerk-clusters: a questionnaire based on personal characteristics and driving patterns was developed;

2) Participants: 31 staff members and their household at Louisiana State University;

3) Participants were asked to refrain from allowing anyone

else to use their vehicle;

4) To maintain confidentiality: a unique random identifier

was assigned to each participant;

5) Data collection period: spanned from July 2012 to January

2013- With each driver contributing around 10 days.


8

20-29 30-45 46-65 650.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

Male FemaleAge

Per

cent

age

Interrupted state highways in Baton Rouge, LA were selected in this

study, based on the data frequency:

1) A 7.5 mile long stretch of LA 42,

a four-lane divided highway with

posted speeds varying from 45 to 55 mph;

2) A 5.15 mile long stretch of LA 1248,

a four-lane divided highway with

posted speeds varying from 30 to 45

mph.

1. DATA COLLECTION :ROADWAY SELECTION


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1) Average Daily Traffic (ADT) count data: accessed through

LADOTD’s estimated annual average daily traffic counts

2) Roadway geometric features: drawn from Google maps.

1. DATA COLLECTION :ADT AND ROADWAY CURVATURE


10

2. DATA PROCESSING:GPS DATA ERRORS

GPS “noise” at intersections GPS position wandering GPS reading gaps


11

2. DATA PROCESSING:GIS LINEAR REFERENCING

Linear referencing geographic locations (x, y) to a measured linear feature (LA 42 and LA 1248) within a radius of 300 ft. [14 and 16].

This was applied to the collected GPS data and crash data.


12

2. DATA PROCESSING:JERK ANALYSIS

Each participants’ acceleration and jerk between consecutive data points calculated from the

driving data by:

Where:

: Acceleration (ft/s²)

: Change in velocity between successive observations (ft/s)

: Jerk (ft/s³)

: Change in acceleration between successive observations (ft/s²)


13

2. DATA PROCESSING:SEGMENTING THE ROADWAYS


The roads were divided into three different segment lengths in ArcGIS:

1. Short: 1/8 mile segments

2. Medium: ¼ mile segments

3. Long: ½ mile segments

14

2. DATA PROCESSING:CRASH RATE ANALYSIS


Crash rate was calculated for each segment based on [18]:

Where:

: Road segment crash rate expressed for 100 million vehicle-miles traveled

: Total number of crash on the roadway segment

: Traffic volume (ADT)

: Number of years of crash data

: Length of the road segment

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3. SENSITIVITY ANALYSIS

1. 21 jerk thresholds were evaluated, started at -0.5 ft. /sec3 and

decreasing by 0.5 ft. /sec3 until reaching -10.5 ft. /sec3

2. Calculating “crash rate” and “jerk ratio” per each segment of 1/8, ¼,

and ½ mile.

3. Implementing a sensitivity analysis for LA42 and LA1248 separately.


16

3. SENSITIVITY ANALYSIS:LA42

Jerk Threshold: -2.5 ft./s3 Segment length: ¼ mile

Jerk

-0.5

Jerk

-1.5

Jerk

-2.5

Jerk

-3.5

Jerk

-4.5

Jerk

-5.5

Jerk

-6.5

Jerk

-7.5

Jerk

-8.5

Jerk

-9.5

Jerk

-10.

50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Eighth_mile Quarte_mile Half_mile

Pea

rson C

orr

elati

on C

oeff

icie

nt


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4. CRASH FREQUENCY MODELING & LOG LIKELIHOOD RATIO TEST

Crash frequency

modelIndependent variable: jerk

ratio

Log likelihood ratio test

Log likelihood ratio test

Crash frequency model

Independent variables: jerk

ratio & presence of horizontal

curvature

Dependent variable: Crash Ratio= Number of crashes per segment/ ADT


18

19

DATA PREPARATION BASED ON QUARTER MILE SEGMENTS

4. CRASH FREQUENCY MODELINGINDEPENDENT VARIABLE: JERK RATIO

Parameter

DF Estimate

S.E. 95% Confidence Limits

Chi-Square

Pr > ChiSq

Intercept 1 -1.3465 0.4644

-2.2567

-0.4363 8.41 0.0037

Jerk Ratio 1 0.4060 0.0857

0.2380 0.5739 22.45 <.0001

Dispersion

1 0.2856 0.1930

0.0760 1.0736

Parameter DF Estimate

S.E. 95% Confidence

Limits

Chi-Square

Pr > ChiSq

Intercept 1 -0.5448 0.5348 -1.5930

0.5034 1.04 0.3084

Jerk Ratio 1 0.1744 0.0672 0.0428 0.3061 6.75 0.0094Dispersio

n1 0.1523 0.1829 0.0145 1.6025

Table 1- Crash Frequency Model LA42



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Log likelihood ratio test performed to [60]:

1.compare nested models

2.express how many times more likely the data in the first two model are under the general model and any overlap

=

≠

Model:

4. LIKELIHOOD RATIO TEST


21

4. LIKELIHOOD RATIO TEST: INDEPENDENT VARIABLE: JERK RATIO

Road Full Log LikelihoodLA42 -48.4620

LA1248 -35.8554LA42 and LA1248

-88.5092

The two models are significantly different.


22

Parameter

DF Estimate

S.E. 95% Confidence

Limits

Chi-Square

Pr > ChiSq

Intercept 1 -0.6615 0.5959 -1.8294 0.5065 1.23 0.2670Jerk Ratio 1 0.1789 0.0674 0.0468 0.3110 7.04 0.0080

Curve 1 0.1097 0.2431 -0.3668 0.5862 0.20 0.6518Dispersio

n1 0.1442 0.1816 0.0122 1.7020

Parameter

DF Estimate

S.E. 95% Confidence

Limits

Chi-Square

Pr > ChiSq

Intercept 1 -1.4279 0.4372 -2.2847 -0.5711 10.67 0.0011Jerk Ratio 1 0.4619 0.0822 0.3008 0.6229 31.60 <.0001

Curve 1 -0.6095 0.2958 -1.1893 -0.0298 4.25 0.0393Dispersio

n1 0.1374 0.1763 0.0111 1.6969

4. CRASH FREQUENCY MODELINGINDEPENDENT VARIABLE: JERK RATIO & PRESENCE OF HORIZONTAL CURVATURE




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4. LIKELIHOOD RATIO TEST: INDEPENDENT VARIABLE: JERK RATIO & PRESENCE OF HORIZONTAL CURVATURE

The two models are significantly different.

Road Full Log Likelihood

LA42 -46.6056LA1248 -35.7548LA42 and LA1248

-88.0960


24

COMPARING THE LOG LIKELIHOOD RATIO TESTS

Comparing the full log likelihood values of 2 tables:

Models including the presence of curvature performed better

Generally: Inclusion of more variables improve models and offer better results.

Road Full Log LikelihoodLA42 -48.4620

LA1248 -35.8554LA42 and LA1248

-88.5092

Road Full Log Likelihood

LA42 -46.6056LA1248 -35.7548LA42 and LA1248

-88.0960

Independent Variable: Jerk Ratio Independent Variable: Jerk Ratio & Presence of Curvature


25

CONCLUSIONS

Strong correlation between the location of jerk-clusters and

vehicle crash intensity on interrupted traffic flow routes LA42

and LA1248

This finding may permit identification of crash prone locations before

crash data accumulates

Ideal segment length yields the highest correlation

Segments that were too short (less than or equal to 1/8 mile) or too large

(greater than or equal to 1/2 mile) reduced the ability to correlate jerks

and crashesIntroduction and Objective Background Information Methodology Crash Modeling and LL Test Conclusion Acknowledgement

26

ACKNOWLEDGEMENTS

The researchers gratefully acknowledge the financial support

of the National Science Foundation through Grants

#0927123 and #0927358, as well as the Gulf Coast Center

for Evacuation and Transportation Resiliency, a United States

Department of Transportation sponsored University

Transportation Center and part of the Southwest

Transportation University Transportation Center (SWUTC).


27

QUESTIONS?

Brian Wolshon

Gulf Coast Research Center for Evacuation and Transportation Resiliency

Louisiana State UniversityBaton Rouge, LA 70803

[email protected]

IDENTIFYING HIGH-RISK ROADWAYS THROUGH JERK-CLUSTER ANALYSIS Seyedeh-Maryam Mousavi, Louisiana State...

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