Embed Size (px)
Citation preview
DETERMINANTS OF VULNERABLE ROAD USERS’ CRASH SEVERITY IN STATE ROADS
Álvaro CaviedesMiguel Figliozzi
January 19th
TABLEOF CONTENTS 1. BACKGROUND
2. PROBLEM STATEMENT3. DATA AND METHODS4. RESULTS5. CONCLUSIONS
BACKGROUND
1. Department of Transportation’s (DOT) interest in increasing non-motorized transportation
BACKGROUND
1. Focus on driver safety2. Crash frequency vs crash severity
BACKGROUND
1. Urban environments2. Risk factors:• Location,• Environmental, • Crash, • Road,
• Demographic, and • Traffic characteristics.
PROBLEM STATEMENTExplore risk factors of crash severity for pedestrians and bicyclists in the Oregon Highway Network System
DATA AND METHODS
1. Oregon statewide crash database (2007-2014)2. ODOT’s TransGIS database3. Neighborhood concepts (Currans et al. 2015)
INJURY SEVERITYLEVELS A (INCAPACITATED)- ALMOST KILLED
K (FATAL) - KILLED
B (VISIBLE INJURY) – BRUISE
C – COMPLAIN OF PAIN
0 – ONLY PROPERTY DAMAGE
CRASHES IN THE OREGON HIGHWAY STATE NETWORK
VARIABLE
PEDESTRIANCRASHESIN OREGON
ONLY STATE HIGHWAYS
BICYCLISTCRASHESIN OREGON
ONLY STATE HIGHWAYS
TOTAL CRASHES
6,162 1,840(30%)
7,147 1,584(22%)
CRASHES AT INTERSECTIONS
3,629 1,088 4,702 1,045
CRASHES AT SEGMENTS
1,822 561 864 169
OTHERS 711 191 1481 370
DESCRIPTIVE ANALYSIS
1. Location characteristics2. Environmental conditions3. Crash characteristics4. Traffic characteristics5. Road characteristics
LOCA
TION
CHAR
ACTE
RISTIC
SDE
SCRI
PTIV
E AN
ALYS
ISLOCATION LAND USE
PEDESTRIANBICYCLIST
0% 5%
10% 15% 20% 25%
Intersection Segment
K A
0% 5%
10% 15% 20%
Intersection Segment
K A
0% 5%
10% 15% 20% 25%
Urban Suburban Rural
K A
0% 5%
10% 15% 20%
Urban Suburban Rural
K A
(n=496) (n=1039)
(n=133) (n=867)
(n=602) (n=724) (n=209)
(n=372) (n=487) (n=141)
ENVIR
ONME
NTAL
COND
ITION
SDE
SCRI
PTIV
E AN
ALYS
IS LIGHT CONDITIONS WEATHER
PEDESTRIANBICYCLIST
0%
10%
20%
30%
Daylight Dark + streetlight
Dark Twilight
K A
0%
10%
20%
30%
Daylight Dark + streetlight
Dark Twilight
K A
0%
10%
20%
30%
Clear day Bad conditions
K A
0%
10%
20%
30%
Clear day Bad conditions
K A
(n=775) (n=410) (n=228) (n=122)
(n=786) (n=113) (n=42) (n=59)
(n=856) (n=679)
(n=749) (n=251)
CRAS
H CH
ARAC
TERIS
TICS
DESC
RIPT
IVE
ANAL
YSIS ALCOHOL INTOXICATION USER LOCATION
PEDESTRIANBICYCLIST
0%
10%
20%
30%
No alcohol Yes alcohol
K A
0%
10%
20%
30%
No alcohol Yes alcohol
K A
0%
10%
20%
30%
Crosswalk Roadway Midblock
K A
0%
10%
20%
30%
Crosswalk Roadway Bike lane
K A
(n=1292) (n=243)
(n=961) (n=39)
(n=1042) (n=452) (n=41)
(n=423) (n=541) (n=36)
CRAS
H CH
ARAC
TERIS
TICS
DESC
RIPT
IVE
ANAL
YSIS VEHICLE TYPE VEHICLE MOVEMENT
PEDESTRIANBICYCLIST
0%
10%
20%
30%
40%
Passenger car Heavy vehicle
K A
0% 10% 20% 30% 40%
Passenger car Heavy vehicle
K A
0% 10% 20% 30% 40%
Straight Turning
K A
0% 10% 20% 30% 40%
Straight Turning
K A
(n=1496) (n=39)
(n=989) (n=11)
(n=893) (n=642)
(n=393) (n=607)
TRAF
FIC CO
NDITI
ONS
DESC
RIPT
IVE
ANAL
YSIS
AADT (ONLY TRUCKS)
PEDESTRIAN BICYCLIST
0% 5%
10% 15% 20% 25% 30%
<1.5k 1.5k-5k 5k-7.5k >7.5k
K A
0% 5%
10% 15% 20% 25% 30%
<1.5k 1.5k-5k 5k-7.5k >7.5k
K A(n=1107) (n=418) (n=9) (n=1) (n=716) (n=271) (n=8) (n=5)
ROAD
CHAR
ACTE
RISTIC
SDE
SCRI
PTIV
E AN
ALYS
ISROAD SURFACE POSTED SPEED LIMIT
PEDESTRIANBICYCLIST
0% 20% 40% 60% 80%
100%
<20 20-35 35-50 50-65 >65
K A
0% 20% 40% 60% 80%
100%
<20 20-35 35-50 50-65 >65
K A
0% 5%
10% 15% 20%
Dry Wet
K A
0% 5%
10% 15% 20%
Dry Wet
K A
(n=55) (n=1032) (n=316) (n=131) (n=1)
(n=26) (n=616) (n=239) (n=114) (n=5)
(n=1075) (n=460)
(n=862) (n=138)
ROAD CHARACTERISTICS –EXPOSURE ANALYSISDESCRIPTIVE ANALYSIS
- Crash risk under various road characteristics- Likelihood of crash involvement - Depends on exposure- Risk ratio ~ concentration of crashes (number of lanes, road width, and road
classification)- Exposure controlled by estimating the proportion of VMT
Example: 𝑅𝑖𝑠𝑘𝑟𝑎𝑡𝑖𝑜 = +,-.,/012,34.-156,57/-31859706:;1/,+,-.,/012,34<=>7/-31859706:;1/,
EXPO
SURE
ANAL
YSIS
-ROA
D CH
ARAC
.DE
SCRI
PTIV
E AN
ALYS
IS
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4
0
1
2
3
4
5
6
7
8
9
10
Local Collector Arterial
0
1
2
3
4
5
6
7
8
9
10
Local Collector Arterial
0
1
2
3
4
5
6
7
8
9
10
10 - 20 20 - 30 30 - 40 40 - 50
0
1
2
3
4
5
6
7
8
9
10
10 - 20 20 - 30 30 - 40 40 - 50
NUMBER OF LANES ROAD WIDTH ROAD CLASSIFICATIONRIS
K RAT
IORIS
K RAT
IO
RISK R
ATIO
RISK R
ATIO
RISK R
ATIO
RISK R
ATIO
K A B C O
1 2 3 4
1 2 3 4
10-20 20-30 30-40 40-50
10-20 20-30 30-40 40-50
Local Collector Arterial
Local Collector Arterial
PEDESTRIANBICYCLIST
10 10 10
101010
00 0
000
RESULTS
1. METHODOLOGY2. INDIVIDUAL MODEL3. POOLED MODELS4. SENSITIVITY ANALYSIS
POOLEDMODELS
POOLEDMODELS
POOLEDMODELS
POOLEDMODELS
Other significant variables:- Land use- Segment vs Inters.- Time of the day
POOLEDMODELSOnly road and traffic characteristics
POOLEDMODELSOnly road and traffic characteristics
POOLEDMODELSOnly road and traffic characteristics
SENSITIVITY ANALYSISPEDESTRIAN MODEL
CRASH SEVERITY ~ Light conditions +Road classification +Posted speed limit +Pedestrian location+AADT(Only truck)
SENSITIVITY ANALYSISPEDESTRIAN MODEL
CRASH SEVERITY ~ BASELINE SCENARIO
Prob. (K+A)=7.4%
Light conditions (DAYLIGHT) +Road classification (LOCAL STREET) +Posted speed limit (<50 MPH) +Pedestrian location (CROSSWALK) +AADT(Only truck) (<700)
SENSITIVITY ANALYSISPEDESTRIAN MODEL
CRASH SEVERITY ~ Light conditions (DARKNESS) +Road classification (LOCAL STREET) +Posted speed limit (<50 MPH) +Pedestrian location (CROSSWALK) +AADT(Only truck) (<700)
Prob. (K+A)=7.4%Prob. (K+A)=16.1% Change = +8.6%
BAD LIGHTING SCENARIO VS BASELINE SCENARIO
% CH
ANGE
PEDESTRIAN MODELSENSITIVITY ANALYSIS
59%
9% 7% 7% 4% 3% 0%
20% 40% 60% 80%
100%
K+A
SENSITIVITY ANALYSISBICYCLIST MODEL
CRASH SEVERITY ~ Bicyclist location+AADT
% CH
ANGE
13%
8%
3%
0%
5%
10%
15%
20%
Worst case scenario Location: Segment AADT: >10.000
K+A
CONCLUSIONS
1. TAKEAWAYS- Age- Alcohol intoxication- Vehicle size- Vehicle movement
- Posted speed limit- Light conditions- Road classification- Road surface- Location of the user- Land use and AADT
CONCLUSIONS
2. MITIGATION- Educational campaigns- Training courses- More strict enforcement- Protection vulnerable users
CONCLUSIONS
3. LIMITATIONS and FUTURE RESEARCH- Underreporting- Speed- Bike facilities- Special pedestrian signals
QUESTIONS?ACKNOWLEDMENTS
DISSERTATION COMMITTEE:- Miguel Figliozzi- Chris Monsere- Avinash Unnikrishnan
TTP LABTransportation engineering and planningMaster students
![Case Information · Affects a location with a high crash history [Severity Index ≥ 8.4 or a fatal crash within the past three years] Takes place at a highly congested](https://img.pdfslide.net/doc/110x75/5f0a05d37e708231d429a3a1/case-information-affects-a-location-with-a-high-crash-history-severity-index-a.jpg)
