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The effect of adverse weather conditions on the safety of vehicles moving on different types of roads and measuring its margin of safety have always been a major research issue of highways. Determining the exact value of friction coefficient between the wheels of the vehicle and the surface of the pavement (usually Asphalt Concrete) in different weather conditions is assumed as a major factor in design process. An appropriate method is analyzing the dynamic motion of the vehicle and its interactions with geometrical elements of road using dynamic simulation of vehicles. In this paper the effect of changes of friction coefficient caused by the weather conditions on the dynamic responses of three types of vehicles: including Sedan, Bus, and Truck based on the results of Adams/car Simulator are investigated. The studies conducted on this issue for different weather conditions suggest values ranging from 0.04 to 1.25. The results obtained from simulation based on Adams/car represent that the friction coefficient in values of 0.9, 0.8, 0.7, 0.6 do not effect on braking distance significantly and it is possible to attribute them all to dry weather condition. However, as it was anticipated the values of 0.5, 0.4, 0.28 and 0.18 have significant differences in braking distance. Hence, the values of 0.5, 0.4, 0.28 and 0.18 can be attributed to wet, rainy, snowy and icy conditions respectively.
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Civil Engineering Journal
Vol. 4, No. 1, January, 2018
46
Effect of Adverse Weather Conditions on Vehicle Braking
Distance of Highways
Ali Abdi Kordani a, Omid Rahmani b
*
, Amir Saman Abdollahzadeh Nasiri c, Sid
Mohammad Boroomandrad c
a Assistant Professor, Department of Civil Engineering, Imam Khomeini International University, Qazvin, Iran .
b Lecturer, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Tehran ,Iran.
c Master of science Student, Department of Civil Engineering, Highway and Transportation Engineering, South Tehran Branch, Islamic Azad
University, Tehran, Iran.
Received 23 November 2017; Accepted 20 January 2018
Abstract
The effect of adverse weather conditions on the safety of vehicles moving on different types of roads and measuring its
margin of safety have always been a major research issue of highways. Determining the exact value of friction
coefficient between the wheels of the vehicle and the surface of the pavement (usually Asphalt Concrete) in different
weather conditions is assumed as a major factor in design process. An appropriate method is analyzing the dynamic
motion of the vehicle and its interactions with geometrical elements of road using dynamic simulation of vehicles. In this
paper the effect of changes of friction coefficient caused by the weather conditions on the dynamic responses of three
types of vehicles: including Sedan, Bus, and Truck based on the results of Adams/car Simulator are investigated. The
studies conducted on this issue for different weather conditions suggest values ranging from 0.04 to 1.25. The results
obtained from simulation based on Adams/car represent that the friction coefficient in values of 0.9, 0.8, 0.7, 0.6 do not
effect on braking distance significantly and it is possible to attribute them all to dry weather condition. However, as it
was anticipated the values of 0.5, 0.4, 0.28 and 0.18 have significant differences in braking distance. Hence, the values of
0.5, 0.4, 0.28 and 0.18 can be attributed to wet, rainy, snowy and icy conditions respectively.
Keywords: Road Conditions; Friction Coefficient; Dynamic Responses of the Vehicle; Braking Distance; Simulation.
1. Introduction
The effect of undesirable road conditions on the safety of current vehicles on different types of roads is constantly
considered as a major subject in Transportation engineering in all over the world. The statistics of fatality represent
that winter, as the most adverse weather condition, not only can it have significant effect on road surface, but also is
considered as a major factor particularly where transportation and weather condition are interconnected, this means,
Geometric Design and Road Safety, Figures 1 and 2 show the number of fatality of accidents in the US between 2009
and 2010 clearly [1].
The role of Geometric Design, by defining the exact value of the friction coefficient between the surface of the road
(usually Asphalt Concrete) and the tire of vehicle which occurs in different weather conditions as major factor, on the
other hand the dynamic response of the vehicle as a second factor, have to be investigated. When the parameters of
Geometric design and vehicles are investigated interactively, it could be said that designing is close to reality.
*
Corresponding author: its4kar@yahoo.com
http://dx.doi.org/10.28991/cej-030967
This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/).
© Authors retain all copyrights.
Civil Engineering Journal Vol. 4, No. 1, January, 2018
47
In this paper, according to neglect the effect of weather conditions on the road surface and consequently the change
in the interaction between the vehicle and the road and effect of the type of vehicle (weight, dynamic conditions), the
codes valid geometric design, to simulate and present a model for estimating the effect of the mentioned factors on
braking distance. In this paper, in order to investigate the interaction between road and vehicle on each other precisely,
Adams/car is used.
Figure 1. Number killed of roads in various states of America in freezing conditions during the years 2009-2010 [1]
Figure 2. Zones with the possibility of creating frosts on the road (in winter), The United States of America [1]
1.1. Weather Effects on Safety
There are over 5,760,000 vehicle crashes annually. Roughly 22% of which are related to weather, almost 1,259,000
(1,258,978 crashes). These crashes are recognized as those taking place in adverse weather (i.e., blowing
snow/sand/debris or rain, sleet, snow, fog, severe crosswinds) or even on slick pavement (i.e., , snowy/slushy
pavement, icy pavement, or wet pavement). On average, approximately 6,000 (5,879 fatalities) are killed and over
445,000 (445,303 people injured) are injured in weather-related crashes annually (Figure 3). (Source: Ten-year
averages from 2004 to 2014 analyzed by Booz Allen Hamilton, based on NHTSA data). The majority of most
weather-related crashes take place on wet pavement while rainfall: 73% on wet pavement and 46% during rainfall. A
very smaller percentage of weather-related crashes take place in winter conditions: 17% while snowing or sleet, 13%
take place in icy pavement and 14% of them occur on snowy or slushy pavement. Not more than 3% occur in foggy
weather. (Source: Ten-year averages from 2004 to 2014 analyzed by Booz Allen Hamilton, based on NHTSA data)[2].
Civil Engineering Journal Vol. 4, No. 1, January, 2018
48
Note: "Weather-Related" crashes are those that occur in the presence of adverse weather and/or slick pavement conditions
Figure 3. Weather-Related Crash Statistics (10-year Percentages) NHTSA data
It can be seen in Figure 4, segregated, accident statistics (damage, injury and death) in a variety of road weather
conditions:
Figure 4. Weather-Related Crash Statistics (Annual Averages) 10-year Average (2005-2014) NHTSA data [2]
AASHTO the Green Book regulations, focuses more on the road surface which attributes the wet condition as the
worst friction coefficient (Current AASHTO: Green Book 2011, Previous AASHTO: book 1994) (See Table 1) this is
while many vehicles and their drivers experience the icy and snowy road conditions. Obviously, the weather condition
such as rainfall can change the road surface condition. For instance, in snowy condition surface frustration, compacted
snow, soft snow, and slush can be observed which makes the investigation essential.
Table 1. Investigating on suggested values for friction coefficient in different editions of AASHTO [5]
Description
Suggested values for the coefficient of friction
Code and year
The whole maximum and minimum
suggested values are based on speed
parameter that the highest value is for
speed of 112 km/h, and the lowest
value is for speed of 48 km/h
(Wet Pavement and locked wheel)
(previous AASHTO)
Max=0.5
Min=0.4
AASHTO1940
Max=0.36
Min=0.29
AASHTO 1954
Max=0.36
Min=0.27
AASHTO 1965[4]
Max=0.35
Min=0.27
AASHTO 1971
Slightly higher at higher speeds than 1970 Values
AASHTO 1984 & 1990
Current AASHTO
Suggested values in these editions of AASHTO are
based on speed and deceleration rather than friction
coefficient
AASHTO 2001
AASHTO 2004
AASHTO 2011[3]
Since in Geometric Design of roads Stopping Sight Distance (SSD), Passing Sight Distance (PSD) and Decision
Sight Distance (DSD) are directly related to the value of friction coefficient, defining the exact value particularly in
22%
78%
Vehicle Crashes
Weather-Related * Others
19%
81%
Crash Injeries
Weather-Related * Others
16%
84%
Crash fatalities
Weather-Related * Others
907,831
352,211
4,488
573,784
228,196
2,732
210,341
55,942 739
151,94438,770 559
174,446
41,597 538 28,533 10,448 495
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
crashes
persons injured
persons killed
crashes
persons injured
persons killed
crashes
persons injured
persons killed
crashes
persons injured
persons killed
crashes
persons injured
persons killed
crashes
persons injured
persons killed
Wet Pavement Rain Snow/Sleet Icy Pavement Snow/Slushy
Pavement Fog
Weather-Related Crash Statistics10-
year Average (2004-2013)
Road Weather Conditions
Civil Engineering Journal Vol. 4, No. 1, January, 2018
49
modes that most drivers brake while accelerating, and this study becomes more important. In this paper, the effect of
motion features of heavy vehicles such as bus and truck, driver behavior while Braking which usually occurs steering
free (wheels with lateral movement) or locked steering (wheels with very little lateral movement) are investigated in
addition to what mentioned previously.
2. Literature Review
Studies run on friction are divided into three different categories:
1. The changes of surface texture and pavement materials, Geometric features and the fabric of tire on the friction
coefficient.
2. Relation between Geometric Design parameters (such as radius of horizontal curves and vertical alignments,
stopping sight distances the speed of vehicle, slope and Super elevation).
3. The effect of weather conditions on road surface and on friction coefficient
In studies that are divided into category 1, the values of friction are acceptable unless the road surface loses its
primary state, as an example, in case of snow drop and compacted snow or ice which causes the road surface to
become unusual, the same friction coefficient cannot be used. This is because porous surface is covered with
compacted snow and ice and low surface roughness has low impact on the friction coefficient (for example [6]).
In studies in category 2, usually the friction coefficient is obtained by harvesting field of acceleration, speed and
stopping distance of vehicles and also slope and geometric features (for example [7]).
Studies of category 3, the effect of weather conditions directly on road conditions and the value of friction
coefficient is measured which is the subject of this paper as well.
In AASHTO Green Book 2011[3], the effect of weather conditions derived from designing parameters such as
weight, size, the effect of center of mass and so are not considered numerically and precisely in sudden stops. The
studies show that most drivers to stop suddenly while facing an unexpected object brake with the rate of deceleration
greater than 4.5 meter per square seconds, and also about 90% of drivers under normal conditions brake with the
deceleration rate of 3.4 meter per square seconds [5]. In order to compare and include the value of friction coefficient
in different weather conditions and relate its effect on Road friction coefficient while braking, it is necessary to use a
relationship for manual calculations that the value of friction coefficient is applicable. Therefore, because current
AASHTO (2011) is according to the changes of suggested values of speed and acceleration, instead of using current
AASHTO (2001, 2004, 2011), AASHTO (1990 and before) is used in which friction coefficient is directly based on
vehicle type and physical relationships. (Gained relationship is in [4]).
As it is shown in Figure 5, there is no force toward forward and vehicle movement is gained through force
interaction between friction and engine:
Figure 5. Forces Diagram
The force that moves the vehicle forward = 0
     
(1)
Newton's Second Law:
       
(2)
Independent of time:
     



  
(3)
Where:
Civil Engineering Journal Vol. 4, No. 1, January, 2018
50
v = Design Speed (km/h)
d = Braking Distance (m)
Fk = Longitudinal Friction Force
N= Normal Reaction Force
V = Design Speed (km/h)
a = Deceleration Rate (m/s2)
m= Mass of Vehicle (Kg)
µ= Coefficient of Friction
g = Gravitational acceleration (m/s2)
Equation 3 can be chosen according to friction coefficient value choices by designer in different road surface
conditions caused by different weather conditions. Table 2 shows the research that directly gives friction coefficient
based on road surface conditions. In some studies, [8] it also suggested different equations for calculating braking
distance (Table 3) based on road surface conditions (without considering direct effect of friction coefficient).
Table 2. Studies on the coefficient of friction
Description
Icy
Snowy
Rainy
Wet
Dry
Year and Studied
References
The study are presented maximum and
minimum values
Max=1.2326
Min=0.049991
Max=1.224
Min=0.18735
Max=1.093
Min=0.82985
Max=1.093
Min=0.8645
Max= 1.0799
Min=0.96122
2013 [9]
0.15
0.19
0.20
0.24
-
0.3
0.44
0.45
1
2010 [10]
0.25>
-
-
>0.5
>0.5
2004 [11]
Jones and Childers reported
-
-
-
0.4
0.7
2013 [12]
0.15-0.3
)Black ice)
0.7-0.8
0.8-1
2001[13]
The study is carried out by the
coefficient of friction for icy to dry
conditions values of 0.2 to 0.8 to be
considered.
0.2
-
-
-
0.8
2012 [14]
The friction coefficient values are
based on observed speeds of 93 to 99
(Km / h)
0.16
0.27
-
0.8
0.93
1997 [15]
This study using simulation software
Adams / Car with five default values
of coefficient of friction are
considered.
0.18
0.28
0.4
0.5
0.6
2015 [16]
Longitudinal friction coefficient is
suggested between 0.15 and 0.5
0.15
-
-
-
0.5
2003 [17]
This study using simulation software
Adams / Car with four
values of coefficient of friction (for
Dry Concrete Pavement 1.1973 has
been considered.
0.05
0.1946
-
-
1.2801
2017 [18]
Simulations were carried out with
MATLAB/SIMULINK for different
initial velocities under various road
conditions and alignments
0.2
0.35
-
0.5
0.7
2015 [19]
Table 3. Stopping Distance in Jones & Childers study [8]

Compacted Snow
Soft, Loose Snow
Slush
Wet Surface
Dry Surface
Deferent Road
Condition
S.D= 4D
S.D= 3D
S.D= 2D
S.D=1.7 D
S.D= D
Formula
Civil Engineering Journal Vol. 4, No. 1, January, 2018
51
Conducted studies in Table 3, show that the values of friction coefficient range from 0.049 to 1.232. As it is
determined in Table 3, different studies on a road surface condition suggest different friction coefficient values that
many of them are based on field studies. In this study, by having different friction coefficient values for different types
of vehicles and various types of simulations, according to previous studies based on Adams/car simulation different,
suggested values were investigated and simulated. The results of this research can be used in the relationships of
AASHTO Green Book which is related to the friction coefficient, parts of the road which has not got any longitudinal
and terrain slopes, without any considerable roughness in the asphalt concrete road surface. For instance, stopping
sight distance (SSD) in direct routes (without horizontal and vertical curves) (Equation 1) is one of them [3]. Figure 6
is a simplified Diagram of acting forces on the wheels of vehicle while moving. According to Figure 6, the value of
friction coefficient () is obtained by dividing horizontal forces to vertical forces that is mentioned as a simplified
numerator and based on acting acceleration in AASHTO[6].
Figure 6. Simplified diagram of forces acting on a rotating wheel [6]
  
 

   


(4)
By applying default conditions of the simulation (G=0%) on Equation 4, Equation 5 will be linear (AASHTO
2011):
     
   
(5)
Where:
SSD= Stopping Sight Distance (m)
dB = braking distance(m)
dBR = brake reaction distance(m)
V = design speed (km/h)
a = deceleration rate (m/s2)
Due to years of using both Equations 3 and 4 or 5 to calculate braking distance, Many major geometric design and
road safety manuals have approved both equations (both braking distance equations are based on deceleration (a) and
longitudinal coefficient of friction(f))[17].
3. Methodology
In order to analysis of vehicle-highway design interaction, using vehicle dynamic simulation modeling is inevitable,
unless we should prepare a test track with all the vehicles, highway, and weather variables to make a lot of scenarios
and measure all the responses of vehicles in various situations that is too much expensive and almost impossible. In
this paper, the effect of friction coefficient changes due to the type of weather condition on the dynamic responses of
three types of vehicle (Sedan, Bus, Truck) based on outputs of Adams/car simulation environment is investigated.
Since generally the type of vehicle and its features such as size (length, width and height), weight can be effective
on friction coefficient, this research is done not only on Sedan, but on Bus and Truck for five different modes of
friction coefficient and dynamic response of the vehicle. Simulation includes Braking which is known as a common
driving behavior.
Civil Engineering Journal Vol. 4, No. 1, January, 2018
52
3.1. Simulation Process
Adams/car simulation software is based on artificial intelligence which can perform tests on the dynamic response
of vehicles in different highway designs. Moreover, is a module of the MSC Adams software package which can be
used as the importance of the multi-body vehicle models is concerned. Although the number of errors in this simulator
is high in delivering outputs, which increases the time of design and construction of the Roads, high accuracy and
reliable results of its output values have still led to more use of it in the research.
This paper presented crucial details of simulation process with Adams/car, consequently Simulation menu of
Adams/car Straight Line Events are used. Simulating to obtain dynamic responses of vehicle and full vehicle analysis
in Four-Step is run as it following: (Figure 7).
Figure 7. Four-Step Simulation in Adams/Car
3.2. Selected Models of Vehicles
Three types of vehicles which are selected are Sedan, Bus Rigid with Two Axle, Truck with three Axle in order to
simulate (See Figure 8).
Figure 8. 
The Sedan which is investigated [16] is lighter than Bus and Truck. Since friction coefficient is directly related to
the weight force (mg) and the mass of the vehicle, investigation on Bus and Truck is done based on weight. The
vehicle includes bus which is rigid body and the Truck with three axles, single-axle front and rear tandem, also rear
axle has four springs, and front has an Airbag-spring. Truck has six tensors (or moment of inertia at rest). Dynamic
features of the mentioned vehicles are shown in Table 4.
Table 4. Dynamic features of the mentioned vehicles
Vehicle Type
Center of Mass Height
(cm)
Mass
(kg)
Ixx
(kg/mm2)
Iyy
(kg/mm2)
Length
(cm)
Width
(cm)
Sedan
45
1527.68
2.0E+008
5.0E+008
400
200
Rigid Bus
97.4
11697.1
1.42E+010
6.16E+011
1050
260
Truck Unit
116.3
10844.3
4.27E+010
3.79E+011
850
250
Entering the vehicle model on Adams/car software (Open Assembly)
Entering friction coefficients between road surface and the wheels of the
vehicle, entering the type of road and its features (Set Parameters)
Activating and running the simulation (Perform Analysis)
Obtaining the output graphs from Braking Mode (Animate and Plot)
Civil Engineering Journal Vol. 4, No. 1, January, 2018
53
3.3. Simulation Features
Conducted tests are considered for Braking for 40 seconds and with the speed of 80 (km per h). Start of Braking is
the fifth second in each test with a deceleration of 0.34 g’s (according to [3]). Also for braking test in Adams/car,
Close loop mode is used (See Figure 9).
Figure 9. Braking test settings
3.4. Reasons of Driving Behavior
Two distinct modes of operation can be identified: free-rolling and full skidding. In free rolling mode (without any
braking), there is not any relative speed between the pavement and the tire circumference. At full skidding, the
circumferential tire speed is zero and the slip speed is equal to the speed of the vehicle. In typical braking conditions,
the slip speed varies between these two extremes [6]. Figure 10 shows a portion of the simulation performed in the
free steering mode of the sedan vehicle with 8 different friction coefficients.
Figure10. Sedan Vehicle in Simulation test
As it can be observed in Adams/car:
In sliding movement of truck, there is a significant difference between two steering behavior (free steering and
locked steering) is done by driver which is clearly shown in Figure 11.
Civil Engineering Journal Vol. 4, No. 1, January, 2018
54
Figure 11. Difference Value between Locked and Free Steering (Speed = 80 Km/h)
4. Results and Discussion
4.1. The Analysis of Outputs and Investigating the Relationship between Friction Coefficient and the Length of
Stopping Distance of Different Types of Vehicles in Free and Locked Steering
There are 48 tests altogether in order to obtain the values of Braking Distance, different values of 0.18, 0.28, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9 are entered in the simulation environment, and to define each of them to a specific weather
condition and consequently a specific road condition, according to previous studies (Table 3), are named respectively
for Icy, Snowy, Rainy, Wet, Dry, Dry-2, Dry-3. The outputs of Adams/car simulation environment for three types of
vehicles of Sedan, Bus and truck in two different driving behavior modes of free and locked steering are obtained
(Table 5).
Table 5. Braking Distance Based on Road Conditions for types of Vehicle (Speed = 80 Km/h)
Dry3
Dry2
Dry1
Dry
Wet
Rainy
Snowy
Icy
Road Surface Conditions
0.9
0.8
0.7
0.6
0.5
0.4
0.28
0.18
(Coefficient Friction)
85.6010
85.6016
85.6025
85.6039
85.6064
85.6759
187.9897
307.6144
Braking Distance
(Truck-Free) (m)
85.4785
85.4795
85.4806
85.4822
85.485
85.5267
188.081
307.5074
Braking Distance
(Truck-Locked) (m)
114.6687
115.3
115.3656
115.4796
115.6078
116.537
169.5349
311.4074
Braking Distance
(Bus-Free) (m)
113.007
113.0146
113.0279
113.0428
113.05
113.516
162.1888
311.5601
Braking Distance
(Bus- Locked) (m)
104.11
104.319
104.319
104.591
107.02
112.902
132.886
178.424
Braking Distance
(Sedan-Free) (m)
104.39
105.616
105.619
105.907
108.328
114.184
133.017
176.886
Braking Distance
(Sedan- Locked) (m)
-
-
-
-
-
-
-
-
Current AASHTO
(2001,2004,2011) (m)
27.733
31.2
35.6571
41.6
49.92
62.4
89.1428
138.666
Previous AASHTO
(1940-1990) (m)(Sedan)
-
-
-
41.6
70.72
83.2
124.8
166.4
Jones & Childers
(2001) (m) (Sedan)
4.1. Obtained Models from Simulation
In order to calculate stopping distance for variety of roads, particularly those with high passing volume of heavy
vehicles, the following models (6, 7, 8, 9, 10, 11) based on selected friction coefficient according to the dominant
weather condition in the area of design, by selecting 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.28, 0.18 and putting them into the
following models, the values which are really close to reality will be obtained.
0
50
100
150
200
250
300
350
400
450
0 2 4 6 8 10 12
lateral displacement (mm)
Time (s)
free steering
locked steering
Civil Engineering Journal Vol. 4, No. 1, January, 2018
55
  

(6)
  
(7)
  
(8)
  
(9)
  
(10)
  
(11)
Figure 12. Compare between ADAMS simulation, AASHTO and Jones & Childers Study (Speed= 80 Km/h)
Because the most important studies on the values of the coefficient of friction in the longitudinal direction and its
effect on braking distances is on the sedan vehicles, Table 6 for comparing the Values obtained from the simulation
sedan vehicle than the values of AASHTO and Jones & Childers (All values in percentage terms & increased).
Table 6. Percentage difference once for Sedan in Free mode and once in locked mode (Speed= 80 Km/h)
According to models 6 to 11, the corresponding equilibrium points are obtained with friction coefficients of 0.42,
0.52, 0.78 (see Figure 13). At the equilibrium point of 0.42, it was close to what was assumed in the simulation input
0
50
100
150
200
250
300
350
CF=0.9
CF=0.8
CF=0.7
CF=0.6
CF=0.5
CF=0.4
CF=0.28
CF=0.18
Braking Distance (Truck-Free) (m)
Braking Distance (Truck-Locked) (m)
Braking Distance (Bus-Free) (m)
Braking Distance (Bus- Locked) (m)
Braking Distance (Sedan-Free) (m)
Braking Distance (Sedan- Locked) (m)
Current AASHTO (2001,2004,2011) (m)
Previous AASHTO (m)(Sedan)
Jones & Childers (2001) (m) (Sedan)
CF = Coefficient of Friction
µ (Friction Coefficient)
Studies
Steering Mode
0.9
0.8
0.7
0.6
0.5
0.4
0.28
0.18
73.36
70.09
65.8
60.22
53.84
44.73
32.9
22.28
AASHTO
Sedan (free)
-
-
-
60.22
33.65
26.3
6.08
6.7
Jones& Childers
73.43
70.46
66.2
60.72
53
45.35
33.01
21.6
AASHTO
Sedan (locked)
-
-
-
60.72
34.45
27.13
6.17
5.92
Jones& Childers
Civil Engineering Journal Vol. 4, No. 1, January, 2018
56
for rainy weather conditions (μ = 0.4), The difference between the braking distance of Sedan and Truck vehicles is
almost zero. At the equilibrium point of 0.52, close to what was assumed in the simulation input for wet conditions (μ
= 0.5), The braking distances for Sedan and Bus are approximately equal. At the equilibrium point of 0.78, among
input values for simulation in dry weather conditions (Dry1 and Dry 2), the braking distance also equals to Sedan and
Bus vehicles.
Figure 13. Obtained Curves from Simulation models
Examined values of the models represent that according to Literature Review )Table 2), the assumed friction
coefficients for weather conditions are significant Values And can be considered as a friction coefficient with a
precision in order to calculate the braking distance in the geometric design of road components in different road
surface conditions.
Another result of this article is that the braking distance values are less than 0.4 (related to the road surface
conditions in Rainy scenarios, see Figure 12) This suggests that with the changing road surface conditions on the rainy
way (μ = 0.4) to icy = 0.18), there is a lot of difference due to the weight and dynamic conditions of the heavy
vehicles (BUS and Truck) than light vehicles (Sedan). For example, in a friction coefficient of 0.4 (Rainy), The
difference in braking distance in a Locked Steering Truck Vehicle and is 25% greater than the Sedan's braking
distance in locked steering. These value increase to 29.27% for the Snowy condition and μ =0.28, and the friction
coefficient of 0.28 (icy condition) reaches 42.47%, which is a big difference.
5. Conclusions
In this paper conducted studies on different coefficient factors (µ) which every one of them is according to previous
studies on different pavement surface conditions, are caused by different weather conditions. Since in Geometric
design of roads coefficient friction is essential in designing elements, studies on stopping distance is done. In order to
compare the result of this research to other related previous ones, the braking distance relationships of current
AASHTO (2001, 2004 and 2011) and previous AASHTO (1994) and other studies are investigated. The results
obtained from simulation based on Adams/car indicate that friction coefficients of 0.9, 0.8, 0.7and 0.6 do not have
intensively significant differences in the values of braking distance and all of them can be attributed to dry weather
condition. Although it was anticipated there were significant differences in braking distances in values of 0.5, 0.4, 0.28
and 0.18. Therefore 0.5, 0.4, 0.28 and 0.18 can be allocated to wet, rainy, snowy and icy weather conditions
0
50
100
150
200
250
300
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Braking Distance (m)
Coefficient of Friction (µ)
braking distance truck free steering braking distance truck locked steering
braking distance bus free steering braking distance truck locked steering
braking distance sedan free steering braking distance sedan locked steering
Poly. (braking distance truck free steering) Poly. (braking distance truck locked steering)
Poly. (braking distance bus free steering) Poly. (braking distance truck locked steering)
Poly. (braking distance sedan free steering) Poly. (braking distance sedan locked steering)
0.42
0.52
0.78
Civil Engineering Journal Vol. 4, No. 1, January, 2018
57
respectively. For further clarification, the percentage differences are indicated in Table 6, this means that the
percentage difference once for Sedan in Free mode and once in locked mode and also their comparisons to Jones &
Childers and AASHTO are stated. The mentioned values in previous AASHTO are considered in inputs of simulation
due to considering coefficient friction, consequently braking distance are obtained. Also braking distances from
simulation with Adams/car for Sedan, Bus and Truck in both modes of free and locked steering owing to the
significant difference in lateral displacement and to investigate more precisely are obtained. The results obtained from
the comparison of relationships of AASHTO, Jones & Childers 2001 and simulation represent that the lowest vales of
friction coefficient for geometric design belong to AASHTO, while Jones & Childers suggest higher values, also the
values of simulation suggest the highest values which there is no difference in free steering or locked mode.
6. References
[1] By US state .Icy road fatalities. (2008.2009.2010). “Icy Road Fatality Statistics” http://icyroadsafety.com/fatalitystats.shtml
(June.26.2016) .
[2] US Dept. of Transportation (2005-2014). “Weather-Related Crash Statistics”
<http://www.ops.fhwa.dot.gov/weather/q1_roadimpact.html> . (23 June, 2016).
[3] AASHTO (American Association of State Highway and Transportation officials. “A policy on geometric design of highways
and streets, Washington”, D.C. USA 20001). (2011).
[4] AASHTO (Association of State Highway and Transportation officials). “A policy on geometric design of highways and streets”,
Washington, D.C. USA. (1965).
[5] NCHRP400 (National Cooperative Highway Research Program Report). “Determination of Stopping Sight Distances”.
Transportation Research Board, Washington DC. (1997).
[6] NCHRP 108 (National Cooperative Highway Research Program Report). “Guide for Pavement Friction”. Transportation
Research Board, (February 2009).
[7] Kordani, A.A., Molan, M.A. “The Effect of Combined Horizontal Curve and Longitudinal Grade on Side Friction Factors”,
KSCE Jour., 19(1), (2015): 303-310. DOI: 10.1007/s12205-013-0453-3.
[8] Univ. South Carolina. Contemporary College Physics. (2001), “The Friction of Automobile Tires”.
<http://boson.physics.sc.edu/~rjones/phys101/tirefriction.html>. (2 june 2002).
[9] Falero, V.J., “Development and evaluation of a virtual test environment for vehicle models with road friction estimator”, thesis,
Univ. Pontificia Comillas. Madrid. 2013, Pages 76-91.
[10] Hippi M, Juga I, Nurmi P. “A statistical forecast model for road surface friction.” InSIRWEC 15th International Road Weather
Conference, Quebec City, Canada (February 2010): pp. 5-7.
[11] Siril Y, Askar K, Dougherty M. “Expert system to calculate the coefficient of friction-an approach to enhance traffic safety.”
InCybernetics and Intelligent Systems, 2004 IEEE Conference on 2004 Dec 1 (Vol. 2, pp. 803-808). IEEE.
DOI:10.1109/ICCIS.2004.1460691.
[12] Mubarak W. Al-Grafi, Mostafa K. Mohamed, Farhan A. salem,“Analysis of Vehicle Friction Coefficient by Simulink/Matlab”.
International Journal of Control, Automation and System. 2013 Jul; 2(2).
[13] Wallman, C.G., Åström, H.. Friction measurement methods and the correlation between road friction and traffic safety: A
literature review, Statens väg-och transportforskningsinstitut (2001).
[14] Patra N, Datta K. Observer Based Road-Tire Friction Estimation for Slip Control of Braking System. Procedia Engineering
[Internet]. Elsevier BV; 2012; 38:156674. DOI:10.1016/j.proeng.2012.06.192.
[15] Wallman, C.G., Wretling, P., Öberg, G. “Effects of Winter Road Maintenance”, State-of-the-Art, Statens väg-och
transportforskningsinstitut (1997).
[16] Tong C, Li T. “Car Driving Safety Analysis in Rainy and Snowy Weather Based on ADAMS/Car.” CICTP 2015, American
Society of Civil Engineers; 2015 Jul 13. DOI:10.1061/9780784479292.266.
[17] PIARC (Permanent International Association of Road Congresses). “Road Safety Manual”,C13, (2003).
[18] You-Qun Zhao, Hai-Qing Li, Fen Lin, Jian Wang, Xue-Wu Ji. “Estimation of Road Friction Coefficient in Different Road
Conditions Based on Vehicle Braking Dynamics. ” Chin. J. Mech. Eng. (May 2017):982–990. DOI 10.1007/s10033-017-0143-z.
[19] Yang JD, Chen YK, Shi Q, Li YM, Wang FC, Zhu L. Variable Speed Limits on Circular Curved Road Sections under Various
Weather Conditions. InCICTP 2015 2015 Jul (pp. 3242-3253), DOI: org/10.1061/9780784479292.302.
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With the use of the ADAMS/Car and vehicle dynamics model, we established a road model and coupled model of the car and road. By changing the road surface friction coefficient of the ADAMS/Car, the vehicle driving conditions were studied on dry pavement, wet pavement, pavement in rainy weather, pavement in snowy weather, and icy pavement. Through the simulation analysis of the three driving conditions, the curves of lateral displacement, curves of lateral force, and braking distance curves were obtained under different road surface friction coefficients, which can be used to analyze the safety of a car in rainy and snowy weather.
Article
The relationship between side friction factor and longitudinal grade is investigated for horizontal curves considered in three dimensions, and new models for estimating side friction factors for different vehicle types are presented. The study consists of three parts: (1) a series of simulation tests using the CarSim and TruckSim multi-body simulation software packages; (2) multiplex regression analysis; and (3) presentation of recommended formulas for the side friction factor. Two different driving behaviors are considered in the simulation process: in one, the driver negotiates the curve at constant speed; in the other, the driver brakes while passing downgrades. Side friction factors are also studied separately for each axle of a vehicle. Greater side friction factors are found on downgrades for all vehicle types when cornering. From the viewpoint of skidding, the most critical situation is found for the rear axle of a sedan. In addition, braking is found to have a significant effect on side friction factors. The results and models from this study can be used in the geometric design phase by highway engineers to design safer roads.
Conference Paper
Friction plays a key role in causing slipperiness as a low coefficient of friction on the road may result in slippery and hazardous conditions. Analyzing the strong relation between friction and accident risk on winter roads is a difficult task. Many weather forecasting organizations use a variety of standard and bespoke methods to predict the coefficient of friction on roads. This article proposes an approach to predict the extent of slipperiness by building and testing an expert system. It estimates the coefficient of friction on winter roads in the province of Dalarna, Sweden using the prevailing weather conditions as a basis. Weather data from the road weather information system, Sweden (RWIS) was used. The focus of the project was to use the expert system as a part of a major project in VITSA, within the domain of intelligent transport systems
Association of State Highway and Transportation officials)A policy on geometric design of highways and streets
AASHTO (Association of State Highway and Transportation officials). "A policy on geometric design of highways and streets", Washington, D.C. USA. (1965).
Determination of Stopping Sight Distances
NCHRP400 (National Cooperative Highway Research Program Report). "Determination of Stopping Sight Distances". Transportation Research Board, Washington DC. (1997).