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Research article
Factors affecting lane change crashes
Mohamed Shawky
Ain Shams University, Cairo, Egypt
abstractarticle info
Article history:
Received 2 J anuary 2019
Received in revised form 10 December 2019
Accepted 16 December 2019
Available online xxxx
Keywords:
Lane change
Drivers' behavior questionnaire
Trafficcrashes
Logistic regression model
Blind spot
This study aims to investigate the contributing factors affecting the occurrence of crashes while lane-changing
maneuvers of drivers. Two different data sets were used from the same drivers' population. The first data set
was collected from the traffic police crash reports and the second data set was collected through a questionnaire
survey that was conducted among 429 drivers. Two different logistic regression models were developed by
employing the two sets of the collected data. The results of the crash occurrence model showed that the drivers'
factors (gender, nationality and years of experience indriving), location and surrounding condition factors(non-
junction locations, light and road surface conditions) and roads feature (road type, number of lanes and speed
limit value) are the significant variables that affected the occurrence of lane-change crashes. About 57.2% of
the survey responders committed that different sources of distractions were the main reason for their sudden
or unsafe lane change including 21.2% was due to mobile usage. The drivers' behavior model results showed
that drivers who did sudden lane change are more likely to be involved in traffic crashes with 2.53 times than
others. The drivers wholook towards the side mirrors and who look out the windows beforelane-change inten-
tion have less probability to be involved in crashes by 4.61 and 3.85 times than others, respectively. Another in-
teresting finding is that drivers who reported that they received enough training about safe lane change
maneuvering during issuing the driving licenses are less likely to be involved in crashes by 2.06 times than
other drivers.
© 2019 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an
open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Human error is considered as the lead cause of N90% of traffic
crashes around the world [1]. Many studies have attempted to investi-
gate the interrelationship between risky driving behavior and the oc-
currence of traffic crashes. Most of these studies addressed the
speeding, tailgating, alcohol usage, hand-hold mobile usage, failure to
follow traffic sign/signal as the most hazardous driving behavior that
lead to serious crashes. However, the unsafe lane changing behavior
of the drivers has not received the same significant attention from
the researchers. In the Emirate of Abu Dhabi (AD), the capital of United
Arab Emirate UAE, unsafe lane change is consideredas the lead cause of
severe crashes (i.e., any crash resulted in at least one injury or fatality).
It is worth to mention that the driver's population in AD has a unique
composition where N200 different nationalities are living there. This
fact puts the road authorities in a big change to deal with different driv-
ing norms and road safety culture backgrounds. The crash investigation
report defines the lane change related crashes when the crash occurs
during the at-fault drivers changing the lane suddenly. In this case,
the crash cause is recorded in the crash report as “sudden lane
changing”.
In general, not much is known about lane-change related crashes es-
pecially in the middle east countries where road safety information is
not widely published. In the crash reports, it is usually listed that the
crash has been occurred due to sudden lane changing behavior of the
at-faultdrivers (in the crash cause item) without looking for thereasons
behind such behavior. Therefore, this study mainly aims to investigate
the factors affecting the occurrence of lane-change crashes and to ex-
plore the drivers' attitude and perception towards lane change maneu-
vering. To achieve these objectives, two types of data sets were
employed: crash reports data and Driver Behavior Questioner (DBQ)
survey data. Each type of these data provides different valuable infor-
mation regarding the occurrence of the lane change crashes and related
drivers' behavior.
Crash data from 2010 to 2017 shows that the sudden lane change
caused about 17.0% of total severe crashes, followed by speeding
(12.8%) and tailgating (11.2%). Regarding the injury-severity, Table 1
shows a comparison between the frequency of the different injury-
severity levels resulted in lane changing and other crash causes. This
table indicates that the severity of lane-change related crashes is rela-
tively high compared to other crash causes. Table 2 shows a comparison
of three different groups of nationalities. The table also shows that Ara-
bian drivers have higher lane change related crashes comparedto other
IATSS Research xxx (2020) xxx
E-mail address: m_shawky@eng.asu.edu.eg.
Peer reviewunder responsibility of International Association of Traffic and SafetySciences.
IATSSR-00238; No of Pages 7
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0386-1112/© 2019 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://
creativecommons.org/licenses/by-nc-nd/4.0/).
Contents lists available at ScienceDirect
IATSS Research
Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
nationalities despite they represent about 40% of the total number of
driving licenses. This result can be explained on the basis of differences
in driving culture background and the extent to which drivers accept to
pay fines for trafficruleviolations.
2. Lane changing crashes background
Prior studies that tackled lane-change related crashes are relatively
few compared to other crash causes. Chovan, et al. [2] and Eberhard,
et al. [3] mentioned that the main factors that may lead to lane change
crashes include recognition failure, lack of awareness of a threatening
vehicle, and apparent failure to attempt an avoidance maneuver before
the crash. Wang and Kniling [4] reported that lane-change crashes in
the United States represented about 4% of all crashes and resulted in
about 0.5% of total crash fatalities. In addition, about 68% of lane-
change crashes were non-junction crashes, 77% occurred during day-
time and on roads with a wide range of speed limits.Tijerina et al. [5]in-
vestigated eye-glance patters before lane-change intention and they
found that specific eye-glance patterns take place before lane-change
initiation. In addition, during lane change from right to left, the proba-
bility of glancing over the shoulder (blind spot) was 0.08, glancing at
the forward's view was 0.41, glancing at the left mirror was 0.22 and
glancing at the rearview mirror was 0.21.
Dingus, et al. [6] utilized data of the 100-Car naturalistic driving
study to investigate the driver behaviors that lead to lane change
crashes and near-crashes. This study examined 241 participants as
they drove their personal vehicles duringtheir daily commutes for a pe-
riod of 12 months. About 135 lane-change events were identified as
crashes and the rest were near-crashes. The analysis showed that 85%
of the drivers used their turn signals during planned left-lane changes,
while 24% used their turn signals when making unplanned left-lane
changes to avoid a forward crash threat. About 46% of the drivers looked
towards their left mirrors, 50% looked towards their left windows, and
17% looked towards their center mirrors during the 3 s prior to
performing planned left-lane changes. Based on the same data set [7],
it was found that drivers making planned left-lane changes took an av-
erage of 1.5 s to cross into the adjacent left lanes. An average of 2.3 s
elapsed before these drivers encountered a lane-change near-crash.
These findings suggest that drivers had little time to avoid an event
once they started to change lanes. Most of the near-crashes were re-
solved by drivers braking and steering away from the crash threat.
Erik, et al. [8] presented results of naturalistic lane change distribu-
tion, frequency, and duration data collected unobtrusively from 16 com-
muters using instrumented vehicles. A total of 8667 lane changes
(including unsuccessful maneuvers) were investigated. The results
showed that N37% of lane changes were due to a slow vehicle ahead.
The mean duration for 7192 single lane changes was 6.28 s with a stan-
dard deviation of 2.0. In another study, about 539,000 two-vehicle lane-
change crashes occurred in the USA in 1999 were investigated[9]. It was
found that about 10% of “typical lane-change case”crashes involved
large trucks changing lane and light vehicles going straight; about 5%
of these crashes involved thereverse combination. The highest involve-
ment of trucks was observed in the “merging”scenario, counting
around 42% of these crashes.
Some studies used driving simulation to investigate lane change and
risk driving situations [10,11]. Lavallière, et al. [12] investigate the
frequency of looking towards the three vehicles' mirrors (right, center
and left mirrors) during lane change maneuvering. The simulation re-
sults showed differences between young and old drivers. Younger
drivers rotate their heads wider than older drivers when inspecting
the blind spot. These differences can be partially attributed to the re-
duced neck mobility in older drivers. Moreover, older drivers inspect
their rear-view mirrors and blind spots less frequently than younger
drivers: 51 versus 83% for the rear-view mirror and 41 versus 86% for
the blind spot. Yun, et al. [13] examined the impacts of in-vehicle navi-
gation information on lane-change behavior. The results showed that
the impact of in-vehicle navigation information on lane-changing be-
havior varies with trafficflow density and timing. The in-vehicle naviga-
tion information had a significant positive impact on lane-changing
safety under medium and high-density conditions. However, the effect
was not significant under light density conditions. In addition, more im-
provement in operational safety could be gained when in-vehicle navi-
gation information is provided earlier within a range of 2 km upstream
of the exit gore.
Another methodology for investigating the relationships between
driver's behavior and crash involvements was introduced by Reason
et al. in 1990 [14]. This methodology used theDriver Behavior Question-
naire DBQ (i.e., self-report questionnaire survey). Winter and Dodou
[15] stated that the DBQ has increased tremendously since 1990 and
at least 174 studies were published that have used the DBQ up
to 2010. Now hundreds of studied used this methodology to measure
the aberrant driving behavior and the interaction with crash
involvements [16].
3. Lane change crashes analysis
3.1. Descriptive analysis of lane-change crashes
The first set of the employed data has been extracted from the
crashes database for eight years (2010–2017). The crash reports include
full information about the characteristics of at fault-drivers, casualties,
roads, environment and involved vehicles. Out of 15,888 reported se-
vere crashes, about 2705 crashes have occurred as a result of the sudden
lane changing behavior. The analysis of this data has been utilized to de-
fine and statistically describe the problem of lane-change crashes.
Table 3 shows the frequency and percentage of lane-change crashes
and other crash causes in terms of at-fault drivers' characteristics. The
table indicates that females, young drivers (18–24 years old) and low
driving experience (less than three years of driving) have high percent-
age involvement in lane-changecrashes comparedto other crash causes.
Table 4 provides a comparison between the frequency and percent-
age distribution of lane-change crashes and other crash causes in terms
of the characteristicsof roads, location, weather conditions, and vehicle
types. This table shows that the percentage of crashes that had been oc-
curreddue to sudden laneschanging behavior is highon the urban roads,
high-speed limit roads, non-junction segments, wet road surface, light
vehicles, and rainy weather conditions compared to other crash causes.
3.2. Lane-change crashes modeling
To gain more understanding of the contributing factors that signifi-
cantly affect the occurrence of lane-change crashes, a binary logistic re-
gression model was developed. The binary logit model is considered as
Table 1
Statistics of injury-severity levels.
Injury-severity level Lane change crashes Other crash types
Frequency Percentage Frequency Percentage
Slight 1355 31.7% 6536 41.6%
Medium 2105 49.2% 6536 41.6%
Severe 429 10.0% 1351 8.6%
Fatal 390 9.1% 1286 8.2%
Table 2
Drivers' nationality and lane change crashes.
Nationality Driving licenses
percentage
Lane change crashes Other crash types
Frequency Percentage Frequency Percentage
Arabian 40% 1641 61.3% 7221 56.2%
Asian 47% 974 36.4% 5274 41.1%
Others 13% 64 2.4% 346 2.7%
2M. Shawky / IATSS Research xxx (2020) xxx
Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
the best choice for investigating binary variables such as the occurrence
of trafficcrashes[17]. The dependent variable inthe developed model is
the lane change crash occurrence (i.e., 1 = lane change crash,0 = other
crash causes). The statistical software SPSS package was used to find the
significant model variables. Table 5 shows the output of the model re-
sults. The results indicate that drivers' factors (gender, nationality and
years of experience in driving), location and surrounding condition fac-
tors (crash location regarding junctions, light and road surface condi-
tions) and roads feature (road type, number of lanes and speed limit
value) are the significant variables that affected the occurrence of
lane-change crashes.
These results reveal that females, local and low number of years in
driving drivers are more likely to be involved in lane-change crashes
than males, other nationalities and drivers whohave high years of expe-
rience in driving. Based on the estimated odds ratio, the probability of
being involved in lane-change crashes on rural roads is approximately
5.38 times higher than on urban roads and at non-junction segments
of roads is 2.89 times higher than at the locations near/at intersections.
Moreover, the likelihood of lane change crash occurrence increases on
the roads that have poor light conditions, wet road surface conditions,
a high number of lanes and high-speed limit values.
4. Lane changing behaviour analysis
4.1. Drivers' behavior data collection
The second set of data was collected through a Drivers' Behavior
Questionnaire (DBQ) survey for more understanding of the main rea-
sons behind such unsafe behavior during lane changing. This question-
naire was conducted based on a random sample of the drivers in AD,
where some of them have been involved in crashes either sue tosudden
lane changing or other crash causes. The questionnaire form included 27
questions and divided into four main sections: I) demographic informa-
tion of the participants, II) crashes and traffic violation history, III) lane
change behavior and perception, IV) background about awareness and
education. N1000 questionnaire forms were randomly handed out
among drivers in different public areas considering the age and gender
distribution of the licensed drivers' population by AD traffic police sup-
port and supervision. About 429 completed questionnaire forms were
found to be reliable in the analysis process. Table 6 shows the percent-
age distribution of the drivers' gender and age for both the selected
sample and the drivers' population. Based on the result of Chi-Square
tests for specified proportions, the sample of the participated drivers
in the survey well represents the licensed drivers' population with re-
spect to gender and age.
4.2. Drivers' responses analysis
Table 7 summarizes the demographic characteristics of the survey's
participants. As shown in this table, approximately 82% of the partici-
pants were males while about 18% were females. Regarding the age,
Table 4
Location and surrounding conditions for lane change and other crashes causes.
Variables Lane-change crashes Other crashes
Frequency Percentage Frequency Percentage
Road and crash location characteristics Road type rural 2270 88.7% 6628 57.6%
urban 290 11.3% 4876 42.4%
Road speed limit 40 315 12.7% 3245 20.6%
60 515 20.7% 4381 27.9%
80 396 15.9% 4872 31.0%
100 490 19.7% 1659 10.6%
120 772 31.0% 1561 9.9%
No. of lanes per direction 2 650 45.7% 2562 35.9%
3 348 24.5% 2690 37.7%
4 399 28.0% 1628 22.8%
5 30 1.9% 299 3.6%
Intersection-related location at/near intersection 140 5.7% 2444 21.0%
roundabout 93 3.8% 939 8.1%
near U-turn 48 2.0% 300 2.6%
non-junction 2158 88.5% 7941 68.3%
Light condition daytime 1686 62.4% 7560 57.4%
night with good light 798 29.5% 4599 34.9%
night without light 219 8.1% 1017 7.7%
Road surface condition dry 2413 92.1% 11,999 93.6%
wet 81 3.1% 216 1.7%
sand-covered 48 1.8% 153 1.2%
not paved 78 3.0% 448 3.5%
Weather condition clear 2576 95.3% 12,662 96.1%
rainy 79 2.9% 188 1.4%
fog 21 0.8% 186 1.4%
stormy 28 1.0% 139 1.1%
Vehicle type light vehicle 2159 90.8% 10,124 88.5%
heavy vehicle 219 9.2% 1313 11.5%
Table 3
At-fault drivers' characteristics for both lane-change and other causes of crashes.
Variables Lane change crashes Other crashes
Frequency Percentage Frequency Percentage
Gender Male 2343 86.9% 11,732 90.6%
Female 352 13.1% 1211 9.4%
Age 18–24 745 27.6% 3267 25.2%
25–30 656 24.3% 3177 24.6%
31–40 712 26.4% 3557 27.5%
41–50 320 11.9% 1763 13.6%
50–60 190 7.1% 835 6.5%
N60 72 2.7% 340 2.6%
years of driving
experience
b3 1281 47.4% 5820 44.1%
3–6 525 19.4% 2726 20.7%
6–9 323 11.9% 1634 12.4%
N12 576 21.3% 3003 22.8%
Education level
a
Low 9474 60.7% 1623 60.2%
Medium 4881 31.3% 834 31.0%
High 1253 8.0% 237 8.8%
a
Low education levelwho can read and write;medium who has secondary highschool
certificate and high level whohas college and post-graduate certificate.
3M. Shawky / IATSS Research xxx (2020) xxx
Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
about 39.6%, 47.9%, 11.8% and 0.7% of the respondents were 18–30,
31–45, 46–60 and over 60 years old, respectively. In addition, about
23.3% of the participants were local drivers, 39.8% from the Arabian
countries, 30.5% Asian and 6.3% other nationalities. The table also
shows that nearly 16.1% of the participants were low educated, 25.1%
medium and 58.5% high education levels. Furthermore, about 7.2% of
the participants were students, 35.7% were government employees,
and 57.1% were working in the private sector.
Table 8 shows the response of the participants to section II of the
questionnaire survey regarding their history of traffic violations and
crash involvements as at-fault drivers. It shows that about 12.8% of
drivers claimed that they have not receive traffic violation tickets during
the last year. About 9.8% of the questionnaire responders get violation
tickets due to sudden lane change behavior. This low percentage of sud-
den lane change violations does not reflect the real frequency of such
behavior due to the difficulties for detecting this type of trafficoffends
by the traffic police. The table also shows that around half of drivers
were involved in traffic crashes during the last three years and about
83.4% of these crashes were property damage only (PDO) crashes and
16.6% were severe crashes. in addition, about 14.3% of participants re-
ported that sudden lane change maneuvering was the main reason for
their trafficcrashes.
Table 9 shows the participants' responses to the questions about
lane changing behavior (section III) and awareness (section IV). This
table shows that about 22.8% of the participants did a risky sudden
lane change before which led to a near-crash situation. However,
about 88.6% reported that they faced a sudden lane change situation
from other drivers. Regarding the reason behind the sudden lane
change, about 57.2% of the responders committed that driver's
distraction was the main reason (36.0% distraction due to talking with
a person in the car or other things and 21.2% distraction due to using
mobile). Regarding the lane change maneuvering process, 21.0% of the
participants committed that they sometimes, rarely or never use the
turn signals when they intend to change the lane. In addition, about
6.7% reported that they sometimes, rarely or never look at the mirrors
before the intention of lane-change maneuver. Furthermore, about
16.3% of participants do not pay attention to look out to check the va-
cancy of the adjacent lane (blindspot inspections) before lane changing
process.
Regarding the awareness and education of drivers about safe lane
change maneuvering, the result showed that about 18.4% of the partic-
ipants do not know the blind spots,20.7% do not know howto overcome
blind spots problem. About 29.6% of the participants feel that they did
not receiveenough training aboutsafe lane change maneuvering during
issuing driving license. Furthermore, 62.7% of the participants do not
know that sudden lane change is the leading cause of crashes in AD. In
general, participants' answers for section IV questions reveals that
there is a lack of information and awareness about safe lane changing
process.
Table 6
Percentage distribution of drivers' gender and age in the sample and drivers' population.
Drivers
characteristics
% of drivers in the
Sample of participants Licensed drivers' population
Gender Male 82.1% 84.5%
Female 17.9% 15.5%
Age 18–30 39.6% 40.7%
31–45 47.9% 48.2%
45–60 11.8% 10.2%
N60 0.7% 0.9%
Table 5
Output of the binary logit regression model.
Variables Variable categories B S.E. Wald Sig. Odds ratio
Constant −4.335 0.249 303.131 0.000⁎0.013
Gender 1 = male
0 = female
−0.553 0.093 35.317 0.000⁎0.575
Age continuous variable 0.005 0.003 2.544 0.111 1.005
Nationality 1 = local driver
0 = other nationalities
0.143 0.066 4.700 0.030⁎1.154
Education level continuous variable 0.041 0.104 0.158 0.691 1.042
Year of Experience continuous variable −0.041 0.010 17.226 0.000⁎0.960
Crash location 1 = at road segment
0 = at/near intersection
1.062 0.111 91.579 0.000⁎2.891
Light condition 1 = daytime/good light
0 = night with poor light
−0.240 0.118 4.168 0.041⁎0.787
Weather condition 1 = clear
0 = others
0.138 0.144 0.928 0.335 1.148
Road Surface 1 = dry
0 = others
−0.302 0.110 7.553 0.006⁎0.740
Road type 1 = rural
0 = urban
1.683 0.084 398.968 0.000⁎5.379
No. of lanes continuous variable 0.282 0.032 78.400 0.000⁎1.326
Speed limit continuous variable 0.009 0.001 57.647 0.000⁎1.009
Vehicle type 1 = light vehicle
0 = heavy vehicle
−0.123 0.087 2.013 0.156 0.884
⁎Significant level of 95%.
Table 7
Participants' demographic information (section I).
Variables (I - Drivers' demographics) Frequency Percentage
Gender Male 352 82.1%
Female 77 17.9%
Age 18–30 170 39.6%
31–45 205 47.9%
46–60 51 11.8%
N60 3 0.7%
Nationalities Local 100 23.3%
Arabian 171 39.8%
Asian 131 30.5%
Others 27 6.3%
Education level Low 69 16.1%
Medium 109 25.4%
High 251 58.5%
Occupation Student 31 7.2%
Government employee 153 35.7%
Private 245 57.1%
Income level Low 135 31.5%
Medium 169 39.4%
High 125 29.1%
4M. Shawky / IATSS Research xxx (2020) xxx
Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
4.3. Modeling questionnaire responses
To provide a better understanding of the factors related to lane
change behavior that significantly affect crash involvement of the
drivers, the logistic regression model was developed based on the re-
sponses of the survey data. The independent variable in the developed
model is the crash involvement of the drivers (1 = driver who was in-
volved in a traffic crash, 0 = others). Table 10 shows the results of the
logistic regression model for the factors affecting the involvement in
at-fault related crashes. From this table, it can be found that the histor-
ical number of violations is a significant variable of crash involvement.
Regarding the driver's demographics, nationality is shown as a signifi-
cant variable where the local drivers seem more likely to be involved
in crashes than other nationalities.
Regarding the lane change behavior variables, the findings showed
that sudden lane change violations mirrors usage and paying attention
by looking around before lane-change intention are the significant var-
iables that affected crash involvements of drivers. Drivers who commit-
ted that they did risky lane change before are more likely to being
involved in crashes (2.525 times) compared to others. Drivers who al-
ways look at the mirrors before lane change have 78.3% (=1–0.217)
are less likely to be involved in crashes compared to drivers who rarely
or never do that. In other words, the likelihood that drivers who rarely
or never use the mirrors to be involved in traffic crashes is about 4.61
times (=1/0.217) higher than drivers who look at mirrors before
lane-change intention. The same conclusion can be figured out for the
drivers who do not pay attention by look over the shoulder (blind
spot inspection)) before lane change is more likely to be involved in
crashes with 3.85 times (=1/0.260) more than others. An interesting
finding shows that drivers who reported that they received enough
training about safe lane change during issuing the driving licenses are
less likely to be involved in crashes by 2.06 times (=1/0.486) than
other drivers.
The results also showed that the drivers characteristics such as gen-
der, education level and income are significantly affected lane-change
related crashes. Male drivers are more likely to be involved in lane-
change crashes by 1.835 times compared to female. In addition, drivers
who use the Flashlight turn indictor during lane change maneuvering
are less likely to be involved in lane-change related crashes compared
to drivers who do not use the turn light indicators.
5. Conclusion
This study provided a deep analysis of the traffic crashes that oc-
curred as a result of the unsafe lane change of at-fault drivers. Two
sources of data were employed: crash reports and drivers' behavior
questionnaires. The analysis of the crash data showed that the percent-
age of lane-change crashes is high on the rural roads, high-speed limit,
non-junctions, wet road surface, rainy weather, light vehicles compared
to other crashes causes. The first model was developed based on crash
data and showed that drivers' factors (gender, nationality and years of
experience in driving), location and surrounding condition factors
(crash location regarding intersections, light and road surface condi-
tions) and roads feature (road type, number of lanes and speed limit
value) are the significant variables that affected the occurrence of
lane-change crashes.
Table 9
Participants' behavior and awareness regarding lane change process.
Question Categories Frequency Percent
Section III - Lane change behavior
have you done a risky sudden lane
change before that led you to a
near-crash situation?
yes 98 22.8%
no 331 77.2%
If yes, what was the reason that
led you to do such sudden lane
changing?
distraction due to
talking with beside
person or other things
37 36.0%
distraction due to using
mobile phone
22 21.2%
trying to avoid sudden
obstruct on the road
9 8.7%
trying to catch a near
road exit
19 18.3%
trying to overtake a
slow front car
11 10.6%
trying to avoid sudden
lane changing from
another near car
4 3.8%
fatigue or sleepiness
while driving
2 1.9%
Do you use a vehicle turn signals
before lane-change intention?
always 207 48.3%
often 132 30.8%
sometime 69 16.1%
rarely 17 4.0%
never 4 0.9%
Do you look at the mirror before
lane-change intention?
always 257 59.9%
often 143 33.3%
sometime 24 5.6%
rarely 4 0.9%
never 1 0.2%
Do you pay attention to lookout
(for visual inspection the blind
spot) before lane-change
intention?
yes 359 83.7%
no 70 16.3%
Have you ever faced a sudden lane
change while driving from
another driver?
yes 380 88.6%
No 49 11.4%
Section IV - Lane change awareness and education
Do you know the blind spot? yes 350 81.6%
no 79 18.4%
Do you know how to avoid the
blind spot problem while lane
change maneuvering?
yes 340 79.3%
no 89 20.7%
Do you know that sudden lane
change is considered as the
main cause of traffic crashes in
AD?
yes 160 37.3%
no 269 62.7%
Did you receive enough training
when issuing a driving license
about safety lane change?
yes 302 70.4%
no 127 29.6%
Have you received information or
awareness concerning how to
do a safe change lane?
yes 167 38.9%
no 262 61.1%
Table 8
Participants' history of traffic violations and crashes (section II).
Question Categories Frequency Percent
What is the number of traffic violation
tickets you got at last year?
0 55 12.8%
1–2 153 35.7%
3–5 158 36.8%
6–10 51 11.9%
N10 12 2.8%
Did you get a sudden lane change
violation ticket before?
Yes 42 9.8%
No 387 90.2%
If yes, how may sudden lane change
violation tickets?
1 28 66.7%
2 12 28.6%
N2 2 4.8%
How many times have you involved in
a traffic crash during the last three
years?
0 212 49.4%
1 178 41.5%
2 31 7.2%
≥3 8 1.9%
If yes, what was the crash type? PDO crash 181 83.4%
severe crash 36 16.6%
What was the crash cause? Sudden lane change 31 14.3%
Speeding 23 10.6%
Tailgating 68 31.3%
Distractions 24 11.1%
Enter road without
checking gap to
merge
24 11.1%
Others 47 21.7%
5M. Shawky / IATSS Research xxx (2020) xxx
Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
Based on the drivers' behavior survey, the analysis of 429 question-
naire forms showed that about 22.8% of the responders did a risky sud-
den lane change before. About 57.2% of the participants committed that
the main reason behind their sudden lane change was driver's distrac-
tion (36.0% distraction due to talking with a person in the car or other
things and 21.2% distraction due to mobile users). In addition, about
21.0% of the participants reported that they sometimes, rarely or
never use the turn signals and about 6.7% sometimes, rarely or never
look at the mirrors before lane change intention. Furthermore, about
16.3% of participants do not pay attention to look out to check the
blind spots. The analysis also showed a lack of information and aware-
ness about safe lane change maneuvers where about 18.4% of the partic-
ipants do not know the blind spots and 20.7% do not know how to
overcome blind spots problem. The second model was developed by
using questionnaire data. The findings of this model confirmed the re-
sults of the first model regarding the driver's characteristics variables
(i.e., gender and nationalities). The driving experience has also been
confirmed as a significant variable in lane-change crash involvements
in terms of thedriver's usage of vehicle mirrors and turn light indicators
during lane-change maneuver. The model results showed that the
drivers who look towards the mirrors and who look out the windows
(for blind spot inspection) have less probability to be involved in
crashes by 4.61 and 3.85 times than others, respectively. In addition,
the drivers who did sudden lane change before are more likely to be in-
volved in crashes with 2.525 times than others. Another interesting
finding showed that drivers who committed that they received enough
training about safe lane change during issuing the driving licenses are
less likely to be involved in crashes by 2.06 times than other drivers.
The findings of this research proved the importance of the aware-
ness and education of drivers in improving the unsafe lane change be-
havior and its positive impact on road safety. These findings can be
utilized not only in the case study city but could be extended to other
countries. The awareness campaigns and training efforts should focus
on the proper usage of side and mid-mirrors, turn signal indicators,
avoiding districted devices during driving and how to check blind
spots before changing lanes. The findings also reveal that advanced sys-
tems that help drivers in perceiving adjacent vehicles, prevent drivers'
distraction and recognizing crash threats while concurrently monitor-
ing the forward [1–17] roadway may mitigate these human factors
dilemmas.
Acknowledgments
The author would like to thank the Department of TrafficEngineer-
ing and Road Safety, Traffic and Patrols Directorate in the Emirate of
Abu Dhabi for providing the required data and for their help for
conducting the questionnaire survey.
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Table 10
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Variables Category B S.E. Wald Sig. Odds ratio
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Nationality 1 = local
0 = other
0.733 0.323 5.131 0.024⁎2.081
Gender 1 = male
0 = female
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No. of Lane change Violations Continuous −0.161 0.538 0.090 0.764 0.851
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Receiving awareness 1 = received awareness before
0 = do not
−0.096 0.243 0.157 0.692 0.908
⁎Significant at a significant level of 95%.
⁎⁎ Significant at a significant level of 90%.
6M. Shawky / IATSS Research xxx (2020) xxx
Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
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Please cite this article as: M. Shawky, Factors affecting lane change crashes, IATSS Research, https://doi.org/10.1016/j.iatssr.2019.12.002
