2024 RS_1 - Basic Concepts_Updated F.pdf

Ce document permet de comprendre les notion de sécurité routière et entretient routier

1. Module 1: Basic Concepts of
Road Safety
www.ctl.uniroma1.it
[email protected]
Road Safety
A.A. 2023-2024
Module 1
Dr. Stephen Kome

2. Mod. 1: Basic Concepts of Road Safety
•1.1 –Crashes and indicators
•1.2 –The Magnitude of the Problem
•1.3 –Main factors affecting probability of
accidents and injuries
•1.4 –The Pillars of road safety
14/12/2024Mod. 1 Slide 2

3. 1.1 –CRASHES AND
INDICATORS
Basic Concept of Road Safety
14/12/2024Mod. 1 Slide 3

4. What is a road accident?
14/12/2024Mod. 1 Slide 4

5. What is a road Crash?
14/12/2024Mod. 1 Slide 5
➢Road crashes, also known as traffic collisions or
motor vehicle accidents, are incidents that occur
on public roadways involving at least one
vehicle in motion.
➢These crashes can result in property damage,
injuries, or fatalities.
➢Road crashes can involve various types of
vehicles such as cars, trucks, motorcycles,
bicycles, and pedestrian

6. Random Events
2-6
14/12/2024Mod. 1 Slide 6

7. Rare Events
Relative Proportion of Accident Events
HSM, 2010
14/12/2024Mod. 1 Slide 7

8. What is Safety?
Subjective safety
•Perception
•Values vary among
observers
Objective safety
•Quantifiable
•Independent of the
observer
14/12/2024Mod. 1 Pagina 8

9. Changes in Objective and Subjective
Safety
HSM, 2010
14/12/2024Mod. 1 Slide 9

10. What is an indicator?
14/12/2024 Slide 10
“An indicator is a variable, or a combination of variables,
selected to represent a certain wider issue or
characteristic of interest” (Gudmundsson et al.,2016)

11. Road accidents indicators
•Absolute indicators are measures of:
–Number of Crashes during the observation period
–Number of fatalities during the observation period
–Number of injuries during the observation period
•Relative indicators are measures of:
–Crash risk
–Crash severity
14/12/2024Mod. 1 Slide 11

12. Absolute indicators

13. Example: road accidents, injuries and
fatalities in Italy in 2021
•In Italy, in 2021, 151.875injury crashes
occurred, causing 2.875fatalities and
204.728injuries
•8 fatalities every day -1 fatality every 3 hours
•The estimated social cost is over 24
billions Euro, 1.6% of GDP(2013)
Let’s update these figures together!

14. Example: evolution in Italy, 2001-2016
In Italy from 2001 there has been a strong decrease of
the number of road fatalities (-53%) and injuries (-33%)
7096
3283
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Nr Injuries
Nr Fatalities
Road Fatalities Road Injuries

15. Example: comparing different time
periods in the day
14/12/2024Mod. 1 Slide 15
Number of fatalities by hour of day in the EU, 2015
(CARE - 2017)

16. Relative indicators
How we compare the safety level e.g.
in different areas?

17. Accident Risk indicators
•Are used to make comparisons between
different elements of the network or between
different areas/regions
•Are obtained by dividing the frequency of
accidents (A), fatalities (F)and injuries (I)by
a measure of exposure to risk (E)
14/12/2024Mod. 1 Slide 17

18. Measures of exposure to risk
•For networks (or areas):
–Population
–Vehicles
–Passengers * km
•For elements (i.e. road sections and
intersections) of the road network:
–Traffic flows
14/12/2024Mod. 1 Slide 18

19. Types of Accident Risk Indicators
•Accidents Rate (AR = A / E)
•Injury Rate (IR = I / E)
•Fatality Rate (FR = F / E)
14/12/2024Mod. 1 Slide 19

20. Accident Risk Indicators for
areas/regions
Relates the number of accidents A and the
number of Inhabitants, Vehicles and
Passengers * kms
•AR = A / Population
•AR = A / Vehicles
•AR = A /Passengers * kms
14/12/2024Mod. 1 Slide 20

21. Accident Risk Indicators for Road
Sections
Relates the number of accidents A the length
L of the road section and the annual average
daily traffic flow, AADT:
AR = A / (L x AADT)
14/12/2024Mod. 1 Slide 21

22. Accident Risk Indicators for Road
Intersections
Relates the number of road accidents A
divided by the total entering annual average
daily traffic flow AADT:
AR = A / AADT
14/12/2024Mod. 1 Slide 22

23. Accident Severity indicators
•The Injury Index (I.I.) expresses the average
number of injuries (I) in a given period of time
every 100 accidents (A)
I.I. = (I / A) x100
•The Mortality Index (M.I.) expresses the
average number of fatalities (F) in a given
period of time every 100 accidents (A)
M.I. = (F / A) x100
14/12/2024Mod. 1 Slide 23

24. 1.2 –MAGNITUDE OF THE
PROBLEM
Basic Concept of Road Safety
14/12/2024Mod. 1 Slide 24

25. World level

26. Summary
•World level
•European level
•National level
Mod. 1 Pagina 26

27. World level

28. Key messages
(WHO, 2023)

29. How serious is the problem?
1.19 millions road traffic deaths per year
99,167 road traffic deaths per month
3,306 road traffic deaths per day
138 road traffic deaths per hour
One road traffic death every 25 seconds

30. Leading causes of death in 2016 vs 2019
Mod. 1 Slide 30
(WHO, 2018)
(WHO, 2023)

31. 10 Leading Causes of Death by Age Group,
United States─ 2020
Mod. 1 Slide 31
Data Source: National Vital Statistics System, National Center for Health Statistics, CDC., 2020

32. Evolution at world level
1
Mod. 1 Slide 32
WHO estimated number of road traffic fatalities, 2000–2021
(WHO, 2023)

33. Evolution at world level
2
Mod. 1 Slide 33
Number of motor vehicles and rate of road traffic death per 100,000 vehicles: 2000-2016
(WHO, 2018)
Number and rate of road traffic death per 100,000 population: 2000-2016
(WHO, 2018)

34. WHO: Fatalities per 100.000 inhabitants in 2016
Mod. 1 Slide 34
(WHO, 2018)

35. Evolution in WHO Regions
1
Number of road traffic fatalities by WHO region 2016
(WHO, 2018)

36. Evolution in WHO Regions
2
Number of road traffic fatalities by WHO region and country-income level, 2021
(WHO, 2023)
Mod. 1 Slide 36

37. Evolution in WHO Regions
3
Road traffic fatality rate per 100 000 population by WHO region and country income level, 2021
(WHO, 2023)
Mod. 1 Slide 37

38. Evolution in WHO Regions
4
Percentage change in estimated road fatalities, by WHO region, 2010–2021
(WHO, 2023)
Mod. 1 Slide 38

39. WHO: Population, fatalities, vehicle fleet, and
paved roads by country income status (World)
(WHO, 2023)

40. WHO: Trends in road fatalities in low, middle
and high income countries
(WHO, 2018)
Number of countries increasing or decreasing fatalities in 2013-2016

41. Proportion of road traffic deaths by age
range and country income status
(WHO - 2015)
Mod. 1 Slide 41

42. Fatalities by type of user
(WHO, 2023)

43. Africa level

44. Key Facts in Africa
14/12/2024Mod. 1 Slide 44

45. Estimated road traffic fatalities per 100
pop, 2021
Mod. 1 Slide 45

46. Change in fatality rate by WHO region
(2010 t0 2021)
24/02/2021Mod. 1 Slide 46

47. Change in road user death type by WHO
region (2010 t0 2021)
Mod. 1 Slide 47
(ETSC -2023)

48. African Countries with laws that meet best
practice
Mod. 1 Slide 48

49. National level

50. 14/12/2024Mod. 1 Slide 50
Magnitude (See WHO App)
Android
iOS

51. 1.2 -MAIN FACTORS
AFFECTING PROBABILITY OF
CRASHES AND INJURIES
Basic Concept of Road Safety
14/12/2024Mod. 1 Slide 51

52. Summary
•Some definitions
•Factors affecting exposure
•Factors affecting accident rate
•Factors affecting injury severity
14/12/2024Mod. 1 Slide 52

53. Some definitions
•Exposure
–The volume of activity generating risk; the amount
of traffic and travel
•Probability of accident
–Accident rate (as an approximation) =
•Consequence/injury severity
–The extent of damage; the severity of personal
injuries; the number of fatalities
•Injury risk
–The probability of being injured in an accidentNumber of accidents
Exposure

54. Understanding risk factors
•What is a risk factor?
–a variable or feature of the road transport system
that is associated with a higher chance to get
involved in a crash or a higher chance to get
injured in a road crash (SafetyCube, 2017)
•A measure of the strenght of the association
between exposure to a risk factor and an
adverse outcome is Relative Risk

55. Relative Risk
•Relative risk compares the probability of an
adverse outcome in an exposure group to its
probability in an unexposed group
•It can be expressed as the ratio of the
probability of the adverse outcome in the
exposure group to its probability in the
unexposed group
•It helps us in understanding if the exposure to
a risk factor increases, decreases or does not
affect the probability of e.g. dyeing

56. Relative Risk - Example
•We collect data and find that:
–40% of drivers involved in an accident and not
fastening their seatbelt die after the accident
–5% of drivers involved in an accident but fastening
their seatbelt die
•Relative risk = 0.40 / 0.05 = 8
•Drivers not using the seatbelt are 8 times
more likely to die in case of accident than
drivers using their seatbelt

57. Relative Risk - Exercise
Fatally
injured
Not
fatally
injuredTotal
Pedestrians hit by cars
traveling at 40 miles per hour40 10 50
Pedestrians hit by cars
traveling at 20 miles per hour 5 45 50
Total 45 55100
•A study found that the relative risk of a
pedestrian being killed by a car increases
dramatically with speed.
Exposed
Not Exposed

58. Relative Risk - Exercise
•Risk of Death for Pedestrians Hit by Cars
Traveling at 40 mph = 40 / (40+10) = 0.8
•(Risk of Death for Pedestrians Hit by Cars
Traveling at 20 mph) = 5 / (45+5) = 0.1
•Relative Risk = 0.8 / 0.1 = 8

59. The main factors affecting road safety
Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 59

60. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 60

61. Percentage of variation in accidents counts by county and month in
Norway explained by various variables. Source: Fridstrøm et al 1993, 1995
8.1
5.2
4.8
0.3
1.5
7.2
6.1
66.8
0 10 20 30 40 50 60 70 80
Random variation
Unexplained systematic
variation
Rules for accident reporting
Long term trend
Month
County
Weather and daylight
Traffic volume
Explanatory factor
Percentage of explained variation 14/12/2024Mod. 1 Slide 61

62. The amount of travel
•The unit of measurement is generally the
volume of traffic (number of vehicles using a
road unit time)
•Generally, it takes account of motor vehicles
(for pedestrians and cyclists, there are no
reliable estimates on the movement)
14/12/2024Mod. 1 Slide 62

63. Mathematical function
•The relationship between accidents and traffic
volume is:
• where:
–N is the number of accidents
–Q is the volume of traffic
– is a constant
–b is the elasticity (% change in N, if Q varies 1%)b
QN =
14/12/2024Mod. 1 Slide 63

64. Typical relationships between traffic volume and the expected number of
accidents
0
5
10
15
20
25
30
0 5 10 15 20 25
Traffic volume (arbitrary values)
Expected number of accidents (arbitrary values)
Property damage only =
Traffic
1.1
Injury accidents =
Traffic
0.9
Fatal accidents =
Traffic
0.7 14/12/2024Mod. 1 Slide 64

65. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 65

66. Choosing mode of transport
Relative rate of injury (self = 1)
Relative injury risk (Dk, G, UK, Nl, N, Sw)
Elvik, 2002-2008
14/12/2024Mod. 1 Slide 66

67. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 67

68. The mix of road users
•Very often (especially in urban areas and at
intersections) different categories of road
users use the same area for travel
•The interaction determines danger especially
for vulnerable road users (e.g. pedestrians
and cyclists)
•The accident rates depend on the proportions
between the groups
14/12/2024Mod. 1 Slide 68

69. An example
•For pedestrians and cyclists, we have (Brude
and Larsson, 1993):

•where:
–N
1,2 is the number of accidents involving groups 1
and 2
–Q
i is the volume of traffic of group i
– is a scale constant
–b and c are coefficients to be estimatedcb
QQN
212,1
=
14/12/2024Mod. 1 Slide 69

70. Numerical values
•where:
–MV is the volume of motorvehicles
–PED is the volume of pedestrians
–CYC is the volume of cyclists65,052,0
acc .
72,05,0
acc. ped.
0000180,0
0000734,0
CYCMVN
PEDMVN
cyc =
=
14/12/2024Mod. 1 Slide 70

71. Exercise
•If PED increases from 500 to 1,000 and MV
increases from 5,000 to 10,000 (total traffic is
doubled) → what happen to the number of
pedestrian accidents
•If PED increases from 100 to 1,000, what
happens to the risk for pedestrian (number of
pedestrian accidents per pedestrian exposed)?
•If MV increases from 2,000 to 10,000, what
happens to the risk of a MV hitting a
pedestrian?
14/12/2024Mod. 1 Slide 71

72. Comment
•The relationship between accidents and
exposure is, in this case, strongly non linear
•It shows that:
–each category of users is safer (i.e., the accident
rate decreases) if there are more users from the
same group → «Safety in numbers»
–The total number of accidents increases more
than proportionally with interacting traffic volumes
14/12/2024Mod. 1 Slide 72

73. Examples
•If PED increases from 500 to 1,000 and MV
increases from 5,000 to 10,000 (total traffic is
doubled) → the number of pedestrian
accidents increases by a factor 2.33
•If PED increases from 100 to 1,000, the risk
for pedestrian (number of pedestrian accidents
per pedestrian exposed) drops by 50%
•If MV increases from 2,000 to 10,000, the risk
of a MV hitting a pedestrian is reduced by
more than 50%
14/12/2024Mod. 1 Slide 73

74. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 74

75. The type of infrastructure
Area Road type DKFNUKNNLSUSA
RuralMotorway 1.01.01.01.01.01.01.0
Main 4.02.92.92.31.31.32.7
Collector 4.73.2 -3.53.62.34.6
Access 5.76.15.15.57.21.38.7
UrbanMain 11.07.97.25.2 -2.15.7
Collector 9.16.8 -6.518.34.05.6
Access 10.07.37.112.19.53.18.8
All All 4.63.74.44.0 -2.24.6
Relative risk of injury accidents (Motorway = 1)
Elvik,1991-2008
14/12/2024Mod. 1 Slide 75

76. Design features
•Many design features affect safety level:
–Cross section (e.g. number and width of lanes)
–Vertical and horizontal alignment
–Size and regulation of intersections
–Type of parking
–Road surface
–Pedestrian crossings
–........
14/12/2024Mod. 1 Slide 76

77. Width of the road
•The effect of road width depends on whether
you are in an urban or a non-urban (rural)
area:
–in rural area width plays in favor of safety (the
wider space allows for more safety at high speeds)
–in urban it is the opposite (the greater width makes
it difficult crossings the road)
14/12/2024Mod. 1 Slide 77

78. Road Intersections
•The accident rate increases if ...
–... an intersection has more legs
–... a higher proportion of traffic enters the
intersection from the minor road
•Roundabouts reduce the severity of accidents
14/12/2024Mod. 1 Slide 78

79. Curves
The accident rate depends on the radius and the number of
curves per km
Accident rate, based on Norwegian data
14/12/2024Mod. 1 Slide 79

80. Effect of access point density on injury accident rate
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
0 10 20 30 40 50 60 70
Access points per kilometre
Accident rate Access points density
14/12/2024Mod. 1 Slide 80

81. The environmental conditions
FactorValue Relative
accident rate
Light Daylight 1.0
Dark - vehicles 1.0
Dark - pedestrians 2.1
Dark - cyclists 1.6
SurfaceDry 1.0
Wet 1.3
Wet - snow 1.5
Snow or ice covered road 2.5
Relative risk of injury accidents in different
environmental conditions Elvik,1976-2009
14/12/2024Mod. 1 Slide 81

82. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 82

83. The vehicle
•Many vehicle related factors affect the
accident rate:
–Braking capacity (eg ABS, Electronic Brake
Control)
–Stability (eg, tires, suspension, Electronic Stability
Control and Traction)
–Ergonomics (driver's position, position of
information and control devices)
14/12/2024Mod. 1 Slide 83

84. Other factors
•Type of information (e.g. tire pressure,
warning about outside temperature)
•Advanced Driver Assistance Systems:
enhance the driving skills, intervene to take
control of the vehicle, monitor the physical
and psychological condition of the driver (e.g.
Collision Avoidance, Cruise Control, Vision
enhancement, Driver Monitoring, Lane
Control)
•Daytime running lights
14/12/2024Mod. 1 Slide 84

85. Some considerations
•In the case of the vehicle, it is more difficult to
estimate the weight of each risk factor
•Effect of "risk compensation": It is
demonstrated that road users are prone to
adapt their behaviourto risk factors and road
safety measures to a greater or lesser extent
(e.g., I have the ABS -> I drive faster and / or
brake later)
14/12/2024Mod. 1 Slide 85

86. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 86

87. Relationship between annual driving distance and accident rate in three
studies
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
0.0 5000.0 10000.0 15000.0 20000.0 25000.0 30000.0
Annual driving distance (km)
Accidents per million kilometres
Middle-aged
Older (65+)
Middle-aged
Older (65+)
Middle-aged
Older (65+)
Hakamies-Blomqvist
Langford
Alvarez Distance driven
14/12/2024Mod. 1 Slide 87

88. Some considerations
•Risk for older drivers > middle-aged drivers
•Limitations of studies
–Limited samples and self-reported crash
involvement
•The low mileage bias
–Older drivers drive less distance annually
compared to younger drivers
–Most of their driving occurs on high-risk congested
streets
•Frialtybias
–Higher likelihood to be in crash database
14/12/2024Mod. 1 Slide 88

89. Fatal Crash Involvement by Driver
Age
14/12/2024Mod. 1 Slide 89

90. Age and gender of car drivers
Elvik,1996-2008
14/12/2024Mod. 1 Slide 90

91. Some considerations
•The risk for young drivers and elderly is
higher than other age groups
•Up to the age of 30 years accident rate is
higher for men than women, the opposite
from the age of 30:
–women drive less than men
–women drive smaller cars (increases the risk of
injury)
–women drive more in towns and cities
–.... other?
14/12/2024Mod. 1 Slide 91

92. Health conditionsEffects of medical conditions on accident rate
3.71
2.06
2.01
1.96
1.84
1.09
1.19 1.17
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
Sleep apnoeaAlzheimer's
disease
Severe mental
illness
Any drug
presumably
abused
Epilepsy Any visual
impairment
Any hearing
impairment
Any locomotor
disability
Relative accident rate (unimpaired drivers = 1.0)
14/12/2024Mod. 1 Slide 92

93. Alcohol Elvik,1985-2005
14/12/2024Mod. 1 Slide 93

94. Some considerations
•In some studies seems to emerge a lower risk
to low concentrations of alcohol (not
confirmed)
•The ordinate scale is logarithmic: at
concentrations of 2 g/l the risk of accident
increases by a factor of 100!
•The effect is more severe on fatalities and
injuries
14/12/2024Mod. 1 Slide 94

95. Alcohol & drugEffects of acute impairments on accident rate
1 1.4
29
1.4
6.7
12
60
153
0
20
40
60
80
100
120
140
160
180
No drug Any single
drug
Combinations
of drugs
BAC 0.2-0.5
g/l
BAC 0.5-0.8
g/l
BAC 0.8-1.3
g/l
BAC > 1.3 g/lBAC > 0.8 g/l
and drugs
Relative accident rate
14/12/2024Mod. 1 Slide 95

96. Speed
Severe damage
All accidents
Fatal
Elvik et al, 2004
Effect of the average speed (V
0 = 80 km / h)
Change accidents
Change average speed km / h
14/12/2024Mod. 1 Slide 96

97. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 97

98. Infrastructure role
•The infrastructure does not affect always the
the consequences of a road accident
•The most important element is crash barrier
•Peculiar features of crash barriers are: shape,
size, material, anchor
14/12/2024Mod. 1 Slide 98

99. Exposure
Type/Mode MixtureAmount
Accident rate
Injury severity
Vehicles Road UsersInfrastructure
Vehicles Road UsersInfrastructure
14/12/2024Mod. 1 Slide 99

100. Relationship between typical mass of vehicle and probability of driver
(pedestrian, cyclist) injury in injury accidents
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
100,0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
Typical mass (kilograms)
Probability of getting injured Truck
Bus
Passenger car
Taxi
Station wagon
Van
Small motorbike
Moped
Large motorbike
Bicycle
Pedestrian The type of vehicle
Effect of the type and mass of the vehicle
Norvegia
14/12/2024Mod. 1 Slide 100

101. Risk of own injury, risk of injuring others and total risk of injury in two car
crashes as a function of mass of car
1,00 1,00 1,00
0,55
0,60
0,65
0,70
0,75
0,80
0,85
1,75
1,65
1,60
1,55
1,50
1,40
1,25
0,93
1,00
0,950,97
1,00
1,05
1,00
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
2,00
2,20
<850 900 1000 1100 1200 1300 1400 >1500
Mass in kilograms
Relative injury risk to drivers. Smallest cars =
1.00
Own
Others
Total The mass
Effect of vehicle mass on themselves and on others
14/12/2024Mod. 1 Slide 101

102. 1.02 1.02 1.03
0.92
1.01 0.98
0.84
0.78
1.06
0.85
0.70
0.33
0.00
0.20
0.40
0.60
0.80
1.00
1.20
0 1 2 3 4 5 6
Relative injury risk (set to 1.00 for cars without EuroNCAP score)
Number of stars (5 = maximum)
Relationship between EuroNCAP stars and injury risk (Kullgren et al 2010)
All injuries
Fatal and serious injuries
Fatal injuries Vehicle crashworthiness
14/12/2024Mod. 1 Slide 102

103. The pedestrian
Effect of impact speed on the probability of death
of pedestrian
Pasanen, 1991
14/12/2024Mod. 1 Slide 103

104. The pedestrian
Effect of impact speed on the probability of
death of pedestrian
Rosen et al, 2011
14/12/2024Mod. 1 Slide 104

105. Other protective measures
•The helmet reduces the risk of injury by 25%
•The protective suit 30% (helmet + suit = 50%)
•Seat belts reduce the risk of injury by 20-
30%, risk of death by 40-50%
•The air bag reduces the risk of death by
12/14% (with / without belt)
14/12/2024Mod. 1 Slide 105