Rami Harb

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THE USE OF THE UCF DRIVING SIMULATOR TO TEST THE
CONTRIBUTION OF LARGER SIZE VEHICLES TO REAR-END
COLLISIONS AND RED LIGHT RUNNING ON INTERSECTIONS
By Rami Harb
Masters Thesis SUMMER TERM 2005
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KEYWORDS
LTV Light Truck Vehicle e.g. SUV, VAN,
etc. LSV Larger Size Vehicle e.g.
SEMI, SCHOOL BUS,
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CHAPTER 1 INTRODUCTION

• 1.1 Problem statement
• Visibility blockage (vertical or horizontal) at
  intersections is the main concern of our
  research.
• Horizontal visibility blockage occurs at
  unsignalized intersections where the visibility
  for a passenger car driver driving behind an LTV
  is inhibited to his left and right.
• Vertical visibility blockage occurs at signalized
  intersections where the vertical visibility ( of
  the traffic light) for a passenger car driver
  driving behind an LSV is inhibited .

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• 1.2 Research objectives
• To determine whether driving behind an LTV
  increases the probability of rear-end collisions
  due to horizontal view blockage.
• To verify that driving behind an LSV contributes
  to red light running due to vertical view
  blockage at intersections.
• To verify the ability of adding an additional
  traffic light on the side of the road to solve
  vertical visibility problems.
• To evaluate drivers behavior model at
  intersections, including speeds, accelerations,
  and decelerations when driving behind LTV and
  LSV.

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1.3 Literature review

• 1.3.1 Horizontal visibility blockage literature
  review
• According to NCSA, in year 2003 rear-end
  collisions accounted for about 1/3 of the 6
  million crashes reported nationwide.
• Abdel-Aty et al. (2003) stated that the LTVs
  block the visibility of passenger cars drivers
  if they drive behind LTVs.
• Polk (2001)stated that LTV are becoming more
  common on U.S. highways. For year 2000, Motor
  vehicle registrations show 77.8 million light
  trucks in the U.S., a 63.8 increase from 1990.
  During the same period, there was 1 decease in
  the number of passenger cars (PCs). LTVs now
  present 40 of all registered vehicles
• Sayer et al. (2000) asserts that passenger cars
  followed LTVs at shorter distance which could
  lead to higher probability of rear-end
  collisions.
• Acierno (2004) related the mismatch between
  weight, stiffness, and height between LTVs and
  passenger cars to the increase in fatalities
  among passenger car occupants.

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• 1.3.2 Vertical visibility blockage literature
  review
• Retting et al. (2002) reported that red light
  running causes 260,000 crashes every year from
  which 750 are fatal.
• According to the Federal Highway Administration
  (FHWA), in year 2001 there were 200,000 crashes,
  150,000 injuries and about 1,100 deaths
  attributed to red light running.
• According to FHWA survey study, 44 of the red
  light running were attributed to traffic light
  view blockage.
• Countermeasures have been studied and cameras
  cannot help reduce red light running due to
  visibility blockage
• Smith (2001) confirmed that advanced warning
  signs to forewarn drivers approaching a
  signalized intersections reduces significantly
  red light running.

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CHAPTER 2 DRIVING SIMULATORS

• 2.1 UCF Driving Simulator
• The driving simulator is an STS Mark-III system
• Simulator Cab 5 channels of image generation.
• Simview Graphical display software.
• Mdyn Physical feeling of the driving.
• Motion base Provides motion.

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2.1 UCF Driving Simulator (continued)

• Scenario Editor enables us to design scenarios.
• APIs for reading real-time data Currently, APIs
  can read real-time data from Simview. The data
  include steering wheel, accelerator, brake, every
  cars speed and coordinates, with 30 HZ
  frequency.
• Control Room with an emergency STOP button.
• Real-time map showing the location of the
  simulator and the ambient traffic.

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CHAPTER 3 EXPERIMENT METHODOLOGY
3.1 Horizontal visibility blockage scenario
SUB-SENARIO 2, BASE SUB-SCENARIO
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3.1 Horizontal visibility blockage scenario
(Continued)
SUB-SENARIO 1, TEST SUB-SCENARIO
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3.2 Vertical visibility blockage scenario
SUB-SENARIO 2, BASE SUB-SCENARIO
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3.2 Vertical visibility blockage scenario
(continued)
SUB-SENARIO 1, TEST SUB-SCENARIO
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3.3 Horizontal visibility blockage simulation
scenario design

• Two design criteria are applied
• Dropping velocity from 45 mph to 35 mph.
• Dropping lanes from 2 in each direction to 1 in
  each direction.

STAGE 3
STAGE 2
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3.4 Vertical visibility blockage simulation
scenario design

• Two design criteria are applied
• School bus makes a right turn slowly.
• 1 lane per direction and high traffic volume in
  the opposite direction.

STAGE 3
STAGE 2
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CHAPTER 4 THEORETICAL CALCULATIONS
4.1 Vertical visibility blockage
to be lt H3
H2 LSV height 8.5 ft is used in the
experiment. H3 signal head height 21 ft is
used in the experiment (AASHTO). H1 eye
elevation equal to 3.75 ft (AASHTO) w width
of the intersection 40 ft (AASHTO) L length
of the vehicle taken 30 ft (AASHTO) D Gap
bet/ stop bar and intersection 10 ft t
standard reaction time 1.0 s (AASHTO) a
acceleration 10 ft/s2 (AASHTO) X1 See
figure X2 .See figure V 35 mph or 51.33
feet per second
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4.2 Horizontal visibility blockage
CONCLUSION if AFlt295.60 ft gt Visibility
blockage
Condition
w intersection width 24 ft ( each lane is
assumed to be 12 ft) L vehicle length 30 ft
(AASHTO) D Gap bet/ stop bar intersection
10 ft t reaction time 1.0 s (AASHTO) a
acceleration 10 ft/s2 (AASHTO) AG Shown in the
fig GF Shown in the fig. v velocity 35
mph or 51.33 ft/sec
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CHAPTER 5 Data collection method
5.1 Pilot Study
Preliminary data collection method
5 (SIM-LTV/SIM-PC)
N 10
5 (SIM-PC/SIM-LTV)
3 Tests
P-value0.138

• Testing rear-end collisions between the SIM-PC
  and SIM-LTV (10 Vs.10) .
• Testing the rear-end collisions between SIM-PC
  and SIM-LTV (5 Vs. 5).
• Testing rear-end collisions SIM-LTV and SIM-LTV
  (5 Vs.5)

P-value0.157
P-value0.157
CONCLUSIONS

• bias in the data collection.
• marginal statistical difference between starting
  with PC and starting with LTV accident ratio with
  P-value 0.157.
• A subject cannot drive more than 1 sub-scenario
  in each scenario

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5.2 Sample size
95 C.I.
99 C.I.
N40 for each scenario
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CHAPTER 6 EXPEIMENTS DATA OUTPUT
6.1 Raw data

• X-Y coordinates of vehicles
• Simulator car speed
• Acceleration input
• Brake input
• Steering input

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6.2 Derived data
6.2.1 Horizontal view blockage
1. Deceleration rate
2. Reaction delay time
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3. Cruising velocity
4. Gap (ft) (sec)
5. Angular Velocity
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6.2.2 Vertical view blockage
2. Cruising velocity
1. Reaction delay time
3. Deceleration rate
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7. HORIZONTAL VIEW BLOCKAGE DATA ANALYSIS
7.1 Rear-end collision rate
CHI-SQUARE TEST
P-value 0.013
Statically significant difference between the
accident rates
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7.2 Deceleration Rate
Vi Cruising Velocity Vf 5 mph
2 sample t-test
17.77 ft/sec2
22.23 ft/sec2
P-value 0.002lt0.05
7.3 Gap (ft)
2 sample t-test
75.6 ft
114.6 ft
P-value 0.01lt0.05
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7.4 Reaction delay time (sec)

• Assumption For both Sub-scenarios

2 sample t-test
P-value 0.551gt0.05
7.5 Cruising velocity
2 sample t-test
34.30 mph
32.54mph
P-value 0.013lt0.05
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7.6 Impact velocity (mph)
Impact velocity LTV gt Impact velocity PC
Conclusion

• More Rear- end collisions w/ LTV.
• Accidents are more severe

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7.7 Logistic regression
P-values
P-values gt0.05 Start eliminating factors
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7.7 Logistic regression (continued)

• High Correlation Between PC LTV
• Final Model does not include PC

• When Critical ratio gt 1.375
• Accident probability gt0.5

1.375
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7.8 Survey Analysis
Driving close to PC or LTV?
Seen or unseen Vehicle turning left?
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7.10 Conclusions

• Velocities following LTV gt PC
• Gap following LTV lt PC
• Deceleration rate following LTV gt PC
• Impact Velocity higher
• When Ratio gt1.375
• Survey Analysis shows

• Frustration to pass LTV due to visibility blockage

• Higher accident probability
• Severe accidents following LTV

Potential rear-end collision with LTV

• 50 had visibility problems

• 35 Said they drive close to LTV in real life

All indications show that visibility blockage due
to LTV is serious
Possible future solution Adding a concave mirror
on the right side the road
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8. VERTICAL VIEW BLOCKAGE DATA ANALYSIS
8.1.1 Red light running rate
CHI-SQUARE TEST
P-value 0.006 lt 0.05
Statistical significant difference between red
light running rate
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8.1.2 Deceleration rates
N 10 subjects from LSV Vs. N 18 subjects
from PC
2 Sample t-test P-value 0.97 gt 0.05
No statistically significant difference
8.1.3 Reaction delay time

• N18 Vs. N 10

2 Sample t-test P-value 0.073 gt 0.05
No statistically significant difference
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8.1.4 Velocity means
N 20 subjects from LSV Vs. N 20 subjects
from PC
2 Sample t-test P-value 0.43 gt 0.05
No statistically significant difference
8.1.5 Gaps

• N18 Vs. N 10

2 Sample t-test P-value 0.398 gt 0.05
No statistically significant difference
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8.1.6 Survey analysis
too late to stop following a school bus and
following a PC?
Driving close behind a school bus and a PC?
Same Visibility problem in daily life?
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8.2 Suggested solution
8.2.1 Red light running rate
N 20 New Subjects
CHI-SQUARE TEST
Statistical significant difference Potential
Solution
P-value 0.047 lt 0.05
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8.2.2 Deceleration rates
P-value 0.408 gt 0.05
8.2.3 Reaction delay time
P-value 0.649 gt 0.05
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8.2.3 Cruising Velocities
P-value 0.019 lt 0.05
8.2.4 Gaps
P-value 0.273 gt 0.05
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8.2.5 Survey Analysis
Additional traffic signal pole evaluation for
real life.
traffic signal poles visibility
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8.3 Conclusions

• LSVs contribute to red light running.
• Cruising velocities, gaps, deceleration rates,
  and reaction delay time were not statistically
  different
• Fear of passing oversized vehicles
• The solution is profitable
• Survey
• Profitability of additional signal pole
• Real life visibility problem

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9. CONCLUSIONS

• LTVs contribute to rear-end collisions.
• LTVs block the visibility for following PC.
• Drivers behind LTV are frustrated
  smaller gaps higher velocity
• Accidents are more severe.
• Survey analysis - 50 suffered a visibility
  problem
• - 35 drive
  close to LTVs in real life
• LSVs contribute to red light running.
• Velocities, gaps, deceleration rates, reaction
  delay time are the same

Fear of passing LSVs
3. From the survey (1) Real life problem, (2)
Visibility problem 4. Additional signal pole
decreases red light running 5. Additional
traffic signal pole profitable according to survey