International  Journal  of
Environmental Research
and Public Health
Article
Analysis of Trafﬁc Crashes Caused by Motorcyclists Running
Red Lights in Guangdong Province of China
Guangnan Zhang 1, Ying Tan 2, Qiaoting Zhong 1,*
and Ruwei Hu 3


Citation: Zhang, G.; Tan, Y.;
Zhong, Q.; Hu, R. Analysis of Trafﬁc
Crashes Caused by Motorcyclists
Running Red Lights in Guangdong
Province of China. Int. J. Environ. Res.
Public Health 2021, 18, 553.
http://doi.org/10.3390/
ijerph18020553
Received: 1 December 2020
Accepted: 6 January 2021
Published: 11 January 2021
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1
Center for Studies of Hong Kong, Macao and Pearl River Delta, Institute of Guangdong, Hong Kong and
Macao Development Studies, Sun Yat-Sen University, Guangzhou 510275, China; zhgnan@mail.sysu.edu.cn
2
School of Economics and Trade, Guangdong University of Finance, Guangzhou 510521, China;
yingtan.cn@hotmail.com
3
School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China; huruwei@mail.sysu.edu.cn
*
Correspondence: zhongqt6@mail.sysu.edu.cn
Abstract: Motorcycles are among the primary means of transport in China, and the phenomenon
of motorcyclists running red lights is becoming increasingly prevalent. Based on the trafﬁc crash
data for 2006–2010 in Guangdong Province, China, ﬁxed- and random-parameter logit models are
used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red
light violations and injury severity resulting from motorcyclists’ running red lights in China. Certain
factors that affect the probability of motorcyclists running red lights are identiﬁed. For instance,
while the likelihood of violating red light signals during dark conditions is lower than during light
conditions for both car drivers and pedestrians, motorcyclists have signiﬁcantly increased probability
of a red light violation during dark conditions. For the resulting severe casualties in red-light-running
crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe
injury. Regarding the relationship between red light violations and the severity of injuries in crashes
caused by motorcyclists running red lights, this study indicated that driving direction and time
period have inconsistent effects on the probability of red light violations and the severity of injuries.
On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right
is higher than when going straight, but this turning factor has a nonsigniﬁcant impact on the severity
of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsigniﬁcant
effects on the probability of motorcyclists’ red light violations in contrast to going straight, but have a
very signiﬁcant impact on the severity of injuries. Moreover, the likelihood of red light violations
during the early morning is higher than off-peak hours, but this time factor has a negative impact on
the severity of injuries. Measures including road safety educational programs for targeted groups
and focused enforcement of trafﬁc policy and regulations are suggested to reduce the number of
crashes and the severity of injuries resulting from motorcyclists running red lights.
Keywords: trafﬁc violation; injury severity; road safety; risk factor; motorcyclist
1. Introduction
Running red lights is a major cause of crashes at intersections, posing a higher
risk of injury than other kinds of trafﬁc violations [1,2]. The AAA Foundation for Traf-
ﬁc Safety has revealed that red-light running (RLR) deaths in the United States hit a
10-year high in 2017 and 28% of crash deaths that occurred at signalized intersections
were the result of a driver running through a red light. More than 184 drivers daily
were caught failing to stop at red lights in the United Kingdom in 2015 (more details
can be found at https://www.thisismoney.co.uk/money/cars/article-3589194/The-roads-
drivers-caught-running-red-lights-revealed.html.). Among all police-reported crashes in
2015 in Thailand, 1.96% were caused by drivers violating the red light signal [3]. According
to the statistics revealed by the Ministry of Public Security, 4227 severe-injury crashes and
789 fatalities between January and October 2012 in China were attributed to RLR [4].
Int. J. Environ. Res. Public Health 2021, 18, 553. https://doi.org/10.3390/ijerph18020553
https://www.mdpi.com/journal/ijerph
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Previous studies have reported various rates of running red lights among road users.
For instance, a study by Yan et al. [5] showed that the RLR rate in Changsha, China was
0.14% for motorists, which was far lower than those for motorcyclists (18.64%), bicyclists
(18.74%) and pedestrians (18.54%). Kim et al. [6] compared the odds of running red lights
between drivers and pedestrians in Hawaii, United States and concluded that drivers tend
to commit proportionately more RLR than pedestrians.
It is critical to identify the inﬂuencing factors leading to RLR behavior for different
road users in different contexts [5]. However, research efforts have focused on car drivers,
e-bike riders, cyclists and pedestrians rather than motorcycle riders, who dominate trafﬁc
in developing countries such as China. Not only the riders of motorcycles but also the
use of motorcycles in China are common, making the problem of motorcyclists running
red lights more prominent in China than in other countries. Most of the motorcyclists in
China have not undergone formal training on how to ride a motorcycle, and riding without
number plates is quite common [7]. In addition, motorcycles in China are mainly used
for delivering food and goods, services with a high demand for efﬁciency, resulting in
many motorcyclists choosing to run red lights or violating trafﬁc safety laws to complete
their tasks on time. By assessing the impact of various risk factors on motorcyclists’ RLR
violations and related accident severity in Guangdong Province of China, results arising
from this study will shed lights on the development of similar (adjusted) measures to
reduce the number of crashes and the severity of injuries resulting from motorcyclists
running red lights, and to promote road safety in other regions.
Based on previous limited studies, there are few contributing factors affecting motor-
cyclists’ RLR violations, such as human characteristics, driving conditions, and driving
environment. For human characteristics, both Chen et al. [8] and Jensupakarn and Kanit-
pong [9] revealed that male and young motorcycle riders were more likely to commit RLR
violations. Motorcyclists whose occupation is a business person/trader and students are
more likely to run red lights than other occupations [9].
The approaching speed and the direction of travel of motorcycles, helmet use, the
presence of a pillion passenger, and the distance from the subject to the stop line all
signiﬁcantly affect RLR violations. Evidence shows that motorcycle riders carrying pillion
passengers are less likely to execute RLR violations [8,9], which could be explained by
the ease and stability of riding without a passenger, or a sense of responsibility for the
passenger’s safety [8]. While the approaching speed had a negative effect on RLR rates in
Jensupakarn and Kanitpong’s study [9] on motorcyclists in Thailand, Chen et al. [8] found
that motorcyclists travelling at higher speeds were more likely to commit RLR violations
in Taiwan. Jensupakarn and Kanitpong’s research [9] also suggested that motorcyclists
riding straight through an intersection are less likely to run a red light compared to when
they have to make a turn, and motorcycle riders who do not wear a helmet tend to commit
RLR violations. Finally, motorcycle riders are less likely to run a red light if they are further
from a junction when the light turns red [8].
With regard to the driving environment, evidence shows that the likelihood of mo-
torcyclists running a red light is higher at night time [8,9], in periods with lower trafﬁc
volume [8] and during off-peak hours [5,8], but is lower on a weekend or a holiday [5].
Despite the increasing prevalence of motorcyclists running red lights in China, the
relevant literature is limited. Only a study by Yan et al. [5] examined the relationship
between RLR violation rates and the day (weekend or work day) and time period for
motorcyclists in Changsha, China. Although such observational investigation is extremely
valuable, it is costly to undertake by its very nature, and thus is performed on selected
intersections. Even if the estimates obtained are reliable, they can only reﬂect some speciﬁc
circumstances at a particular time and place; therefore, sampling errors may arise, and
many important variables (such as individual characteristics of motorcycle riders) may be
lost. More importantly, the extent to which the results arising from observation surveys are
broadly generalizable is unclear [10], leading to a lack of comprehensiveness in the analyses
of risk factors in red light violations and the severity of injuries caused by motorcyclists.
Int. J. Environ. Res. Public Health 2021, 18, 553
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With this in mind, the speciﬁc objectives of this study were to identify the risk factors related
to personal characteristics, vehicle characteristics, road conditions and environmental
conditions affecting (1) motorcycle riders’ RLR violations and (2) the severity of injuries
caused by motorcycle RLR crashes, using data from the road trafﬁc crash database of
China’s Public Security Department from 2006 to 2010 in the Guangdong Province of China.
2. Materials and Methods
2.1. Data
The data used in this study, obtained from the Guangdong Provincial Security De-
partment, were extracted from the Trafﬁc Management Sector-Speciﬁc Incident Case Data
Report. The data were recorded and reported by the trafﬁc police who conducted on-
scene assessments and provided feedback within 24 h to the headquarters of the Trafﬁc
Management Department. The information was recorded according to the Code of Trafﬁc
Crash Information issued by the Computer and Information Processing Standardization
Commission under the Security Department of the country. Each sample included detailed
indexes about the characteristics of drivers/riders, injury severity, vehicle features, road
conditions, crash time, as well as environmental conditions, such as the level, form, and
cause of the crash [2].
Reports of multi-vehicle motorcycle-related crashes occurring at intersections between
2006 and 2010 were extracted for the current research study (see Figure 1 for data inclusion
and exclusion). Among 8054 cases relevant to red light violations of motorcycle riders
together with non-trafﬁc violation accidents, 2317 (28.7%) involved no injury (property
damage only, PDO), 3968 (49.3%) involved minor injury, 931 (11.6%) resulted in serious
injury, and 838 (10.4%) resulted in death. A lower proportion of PDO crashes than that
of crashes involved in minor injury in the dataset is inconsistent with the fact that the
number of crashes decreases with the increase in injury severity [11,12]. Given the potential
under-reporting of PDO crashes [13], PDO crashes were excluded from our analysis.
Moreover, cases with an absence of motorcycle rider characteristics (e.g., rider age), cases
involving foreign riders and cases occurring on expressways were also removed. Thus,
5304 motorcycle-related trafﬁc crashes were selected in the ﬁnal sample, among which
409 involved red light violations by motorcyclists. The China Road Trafﬁc Accidents
Statistics Report showed that the phenomenon of running red lights was very common
during the data period. Previous studies have analyzed the red light violations in China
that happened in the same period, but with different road users or in different cities
(e.g., [2,14,15]). These works provide a comparable foundation for this study to explore the
unique factors behind motorcyclists’ RLR behaviours. Therefore, the results arising from
the current research on motorcyclists running red lights in Guangdong province of China
during 2006 to 2010 have an appropriate level of generality.
In addition, Zhang et al. [2] identiﬁed factors inﬂuencing red light violations by car
drivers, cyclists, and pedestrians from Guangdong province of China. Their results provide
a comparative foundation to study the unique factors for motorcyclists’ RLR behaviors
from the same province. Therefore, the same data were also used here to study the factors
for the severity of car drivers’ injuries in RLR crashes so that the risk factors of severe
injuries between motorcyclists and car drivers in RLR crashes could be distinguished.
Int. J. Environ. Res. Public Health 2021, 18, 553
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Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 
4 of 11 
 
 
 
Figure 1. Data flow diagram for analysis of crashes caused by motorcyclists running red lights. 
2.2. Risk Factors 
The risk factors under consideration in the current study were described in a previ-
ous study derived from the same database [2]. Four dimensions, namely personal factors, 
vehicle factors, road factors and environmental factors, were established as follows. 
Personal factors: Rider gender, age and occupation are considered to be potential risk 
factors. Rider age is divided into four categories following the WHO’s age classification 
criteria: ≤24, 25–44, 45–59, and ≥60 years. Occupation and residential registration are used 
to capture the education level, income and social status of riders. Rider occupation is di-
vided into six categories: self-employed, worker, migrant worker, farmer, no occupation 
and other. Residential registration is divided into rural and urban. Additionally, the im-
pact of head injury on the severity of injuries is examined. 
Vehicle factors: These mainly include whether motorcycles have number plates, 
whether motorcycles carry passenger(s), vehicle safety conditions and vehicle driving sta-
tus, where vehicle driving status is divided into four types: going straight, turning left, 
turning right, and other. 
Road factors: These mainly include road types, junction types and whether there are 
physical barriers on the roads. Roads are considered as two types, i.e., general highways 
(including the first-class and second-class or below highways) and urban roads (including 
general urban roads and other urban roads). Junctions are divided into three types, i.e., 
fork, crossroads and other. 
Environmental factors: Environmental factors include street-light conditions, weather 
conditions, visibility, weekends, holidays, time periods and years. Street-light conditions 
include daylight, nighttime with lighting and nighttime without lighting. While bad 
weather conditions include cloudy, snowy, rainy, foggy, and very windy, poor visibility 
is defined as visibility below 50 meters. Following the previous work of red light viola-
tions [2,8,9], the time period is divided into early morning hours (midnight to dawn), 
morning peak hours (7:00–8:59 am), after work peak hours (5:00–7:59 pm) and other time 
periods. 
2.3. Statistical Data Analysis 
Binary logit models are widely used in the related literature on motorcycle riders’ 
RLR behavior at signalized intersections (e.g., [8,9]). To facilitate comparison with the 
literature using the same method, it was appropriate to adopt binary logit models in this 
study to estimate the effect of different risk factors on the likelihood of the occurrence of 
motorcyclists running a red light in China. Specifically, multivariate logistic regression 
was conducted and the adjusted odds ratios (ORs) of significant factors and their 95% 
confidence intervals (CIs) were computed using Stata 14 (StataCorp, College Station, TX, 
USA). 
Data obtained from the Guangdong Provincial Security Department are extracted from the
Traffic Management Sector-Specific Incident Case Data Report, covering 21,132 multi-
vehicle motorcycle-related crashes occurring at intersections with 25,013 motorcycle
riders for the period of 2006–2010.
Together with non-traffic violation accidents, 8054 samples relevant to red light violations
of motorcycle riders were selected.
Of the 8054 samples reported, 3968 (49.3%) involved minor injury, 931 (11.6%) resulted in
serious injury, and 838 (10.4%) resulted in death.
Data available for analysis in the current research (5304 in total)
Red light violations of motorcyclists (n=409)
Given the potential under-reporting of no injury crashes,
2317 (28.7%) property damage only crashes were
excluded from our samples. 
EXCLUDED (Total=433)
Foreign riders (n=9)
Crashes occurred on the expressways (n=311)
Absence of motorcycle rider characteristics
(n=113)
Figure 1. Data ﬂow diagram for analysis of crashes caused by motorcyclists running red lights.
2.2. Risk Factors
The risk factors under consideration in the current study were described in a previous
study derived from the same database [2]. Four dimensions, namely personal factors,
vehicle factors, road factors and environmental factors, were established as follows.
Personal factors: Rider gender, age and occupation are considered to be potential risk
factors. Rider age is divided into four categories following the WHO’s age classiﬁcation
criteria: ≤24, 25–44, 45–59, and ≥60 years. Occupation and residential registration are
used to capture the education level, income and social status of riders. Rider occupation is
divided into six categories: self-employed, worker, migrant worker, farmer, no occupation
and other. Residential registration is divided into rural and urban. Additionally, the impact
of head injury on the severity of injuries is examined.
Vehicle factors: These mainly include whether motorcycles have number plates, whether
motorcycles carry passenger(s), vehicle safety conditions and vehicle driving status, where
vehicle driving status is divided into four types: going straight, turning left, turning right,
and other.
Road factors: These mainly include road types, junction types and whether there are
physical barriers on the roads. Roads are considered as two types, i.e., general highways
(including the ﬁrst-class and second-class or below highways) and urban roads (including
general urban roads and other urban roads). Junctions are divided into three types, i.e.,
fork, crossroads and other.
Environmental factors: Environmental factors include street-light conditions, weather
conditions, visibility, weekends, holidays, time periods and years. Street-light conditions in-
clude daylight, nighttime with lighting and nighttime without lighting. While bad weather
conditions include cloudy, snowy, rainy, foggy, and very windy, poor visibility is deﬁned
as visibility below 50 meters. Following the previous work of red light violations [2,8,9],
the time period is divided into early morning hours (midnight to dawn), morning peak
hours (7:00–8:59 a.m.), after work peak hours (5:00–7:59 p.m.) and other time periods.
2.3. Statistical Data Analysis
Binary logit models are widely used in the related literature on motorcycle riders’
RLR behavior at signalized intersections (e.g., [8,9]). To facilitate comparison with the
literature using the same method, it was appropriate to adopt binary logit models in this
study to estimate the effect of different risk factors on the likelihood of the occurrence of
motorcyclists running a red light in China. Speciﬁcally, multivariate logistic regression was
conducted and the adjusted odds ratios (ORs) of signiﬁcant factors and their 95% conﬁdence
intervals (CIs) were computed using Stata 14 (StataCorp, College Station, TX, USA).
Int. J. Environ. Res. Public Health 2021, 18, 553
5 of 11
Although motorcycle riders’ red light violations are treated as a serious problem
in terms of related injuries and fatalities in developing countries such as China, limited
research has considered the analysis of inﬂuencing factors for the severity of injuries
in motorcycle RLR crashes. Previous injury severity studies have reported a signiﬁcant
correlation among unobserved effects crossing discrete injury outcome categories (e.g., [16]);
to further estimate the effect of different risk factors on the likelihood of the occurrence of
severe casualties for motorcyclists in RLR crashes in comparison with car drivers, random-
parameter logit models were applied in the current research. Random parameters were
assumed to be normally distributed and 200 Halton draws were used in this study, which
have been widely used assumptions in previous research (e.g., [13,17]).
3. Results
3.1. Sample Description
Among 5304 motorcycle-related trafﬁc crashes, even though the motorcycle-related
crashes caused by RLR are less common (approximately 7.7%) when compared to other
causes, they are considered a serious problem. In fact, the ratio of severe injuries among all
injuries caused by red light violations of motorcyclists is as high as 44.0%, whereas this
ratio for other causes is much smaller, i.e., 38.3% for all motorcycle-related trafﬁc crashes
(see Table 1).
Table 1. Descriptive statistics of variables.
Variables
Motorcycle-Related Crashes
(n = 5304)
Crashes Caused by Red Light
Violations of Motorcyclists
(n = 409)
Crashes Caused by Red Light
Violations of Car Drivers
(n = 435)
Frequency
Proportion
(%)
Frequency
Proportion
(%)
Frequency
Proportion
(%)
Signal violation
409
7.7
409
1
435
1
Killed or seriously injured
2031
38.3
180
44.0
126
29.0
(1)
Gender
Male
4776
90.0
374
91.4
409
94.0
(2)
Age
≤24
1133
21.4
81
19.8
47
10.8
25–44
2988
56.3
241
58.9
322
74.0
45–59
982
18.5
76
18.6
65
15.0
≥60
201
3.8
11
2.7
1
0.2
(3)
Residential registration
Rural
1818
34.3
129
31.5
60
13.8
(4)
Occupation
Farmer
1296
24.4
76
18.6
22
5.0
The self-employed
427
8.1
39
9.5
79
18.2
Worker
1053
19.9
85
20.8
74
17.0
Migrant worker
690
13.0
74
18.1
50
11.5
Unemployed
236
4.4
21
5.1
10
2.3
Other occupations
1602
30.2
114
27.9
143
32.9
(5)
Whether motorcycles
carry a passenger
No passenger
3576
67.4
273
66.7
279
64.1
(6)
Whether motorcycles
have number plates
No number plates
1539
29.0
120
29.3
7
1.6
(7)
Vehicle safety condition
Unﬁt
394
7.4
17
4.2
16
3.7
(8)
Vehicle driving status
Straight
4524
85.3
326
79.7
319
73.3
Turning left
308
5.8
47
11.5
74
17.0
Turning right
83
1.6
8
2.0
14
3.2
Others
389
7.3
28
6.8
28
6.4
Int. J. Environ. Res. Public Health 2021, 18, 553
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Table 1. Cont.
Variables
Motorcycle-Related Crashes
(n = 5304)
Crashes Caused by Red Light
Violations of Motorcyclists
(n = 409)
Crashes Caused by Red Light
Violations of Car Drivers
(n = 435)
Frequency
Proportion
(%)
Frequency
Proportion
(%)
Frequency
Proportion
(%)
(9)
Type of road
First-class highways
828
15.6
53
13.0
45
10.3
Second-class or below
highways
2223
41.9
167
40.8
118
27.1
General urban roads
1663
31.4
164
40.1
208
47.8
Other urban roads
590
11.1
25
6.1
64
14.7
(10)
Type of junctions
Fork
441
8.3
31
7.6
27
6.2
Crossroads
464
8.7
88
21.5
81
18.6
Others
4399
83.0
290
70.9
327
75.2
(11)
Whether there are
physical barriers in roads
No physical barriers
3182
60.0
192
46.9
193
44.4
(12)
Visibility
Bad visibility
525
9.9
37
9.0
39
9.0
(13)
Street-light condition
Daylight
2938
55.4
218
53.3
212
48.7
Dark but lighted
1527
28.8
167
40.8
204
46.9
Dark
839
15.8
24
5.9
19
4.4
(14)
Weather condition
Bad weather condition
1089
20.5
71
17.4
98
22.5
(15)
Holiday
Holiday
381
7.2
30
7.3
31
7.1
(16)
Day of the week
Weekends
1401
26.4
109
26.7
135
31.0
(17)
Time of day
Early morning
804
15.2
64
15.6
98
22.5
Morning peak hours
712
13.4
51
12.5
48
11.0
After work peak hours
911
17.2
63
15.4
58
13.3
Others
2877
54.2
231
56.5
231
53.1
(18)
Year
2006
922
17.4
108
26.4
107
24.6
2007
960
18.1
64
15.6
74
17.1
2008
1075
20.3
71
17.4
75
17.2
2009
1127
21.2
85
20.8
78
17.9
2010
1220
23.0
81
19.8
101
23.2
(19)
Injured parts
Head
1390
26.2
101
24.7
15
3.4
3.2. Risk Factors Affecting Motorcyclists Running Red Lights
The ﬁrst model in Table 2 shows the risk factors associated with motorcyclists running
red lights. Concerning personal characteristics, the probability of male riders running red
lights is 1.48 times greater than that of female riders, which is consistent with conclusions in
most of the literature. Compared with motorcyclists over the age of 60, young motorcyclists
under the age of 24 (OR = 1.61) shows a signiﬁcant increase in the probability of running
red lights. Chen et al. [8] argued that the young-rider effect could be explained by the
fact that young riders, in general, tend to demonstrate risk-taking road behaviors. The
risk of running red lights among migrant worker motorcyclists is signiﬁcantly higher
than that among farmers (OR = 1.23), but the impact of residential registration of rider is
insigniﬁcant.
Int. J. Environ. Res. Public Health 2021, 18, 553
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Among vehicle factors, whether motorcycles carry passengers, whether motorcycles
have number plates, vehicle safety conditions and vehicle driving status are signiﬁcant
factors for the probability of motorcyclists running red lights. The probability of running
a red light by a motorcyclist riding alone is 1.18 times the probability with passengers.
Such results with regard to the presence of a pillion passenger are similar to the study
conducted by Chen et al. [8] and Jensupakarn and Kanitpong [9]. The probability of a
motorcyclist running a red light is higher when riding a motorcycle that does not satisfy
safety requirements (OR = 1.25). However, the probability of a motorcyclist running a red
light is lower when riding a motorcycle that does have a number plate (OR = 0.88) As
reported in Jensupakarn and Kanitpong’s study [9], the driving direction of a motorcycle
also affects whether a motorcyclist runs a red light: compared to driving straight ahead,
the probability of a motorcyclist running a red light is signiﬁcantly higher when they are
turning (left: OR = 1.52, right: OR = 1.71, respectively).
Table 2. Factors inﬂuencing motorcyclists running red lights and resulting severe casualties.
Factors
Red Light Violations for
Motorcyclists
Severe Casualties for Motorcyclists
in Red-Light-Running Crashes
Severe Casualties for Car Drivers
in Red-Light-Running Crashes
ORs
(95% CI)
ORs (s.d.)
(95% CI)
ORs (s.d.)
(95% CI)
n
5304
409
435
(1) Personal Factors
Gender of rider (base: female)
Male
1.48 ***
0.17 **
[1.20, 1.82]
[0.04, 0.70]
Age of rider (base: ≥60)
≤24
1.61***
1.38 (35.53 *)
7.11 **
[1.15, 2.26]
[0.16, 12.11]
[1.53, 33.05]
25–44
2.79 *
[0.92, 8.45]
Residential registration of rider (base: urban)
Rural
3.21 *
[0.87, 11.80]
Occupation of rider (base: farmer)
The self-employed
0.19 *
[0.03, 1.36]
Migrant worker
1.23 *
[1, 1.52]
Worker
0.14 **
[0.02, 0.97]
Injured parts (base: others)
Head
NA
11.80 ***
[3.15, 44.15]
(2) Vehicle factors
Carry passenger (base: yes)
No passenger
1.18 **
0.33 ***
[1.04, 1.34]
[0.16, 0.71]
Whether motorcycles have number plates (base: yes)
No number plates
0.88 *
[0.77, 1.01]
Vehicle safety condition (base: ﬁt)
Unﬁt
1.25 **
[1.01, 1.56]
Vehicle driving status (base: straight)
Turning left
1.52 ***
[1.19, 1.93]
Turning right
1.71 **
[1.09, 2.68]
Int. J. Environ. Res. Public Health 2021, 18, 553
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Table 2. Cont.
Factors
Red Light Violations for
Motorcyclists
Severe Casualties for Motorcyclists
in Red-Light-Running Crashes
Severe Casualties for Car Drivers
in Red-Light-Running Crashes
ORs
(95% CI)
ORs (s.d.)
(95% CI)
ORs (s.d.)
(95% CI)
n
5304
409
435
Others
10.24 ***
[2.33, 44.95]
(3) Road factors
Type of road (base: ﬁrst-class highways)
Second-class or
below highways
1.29 ***
1.35 (11.60*)
6.06 **
[1.07, 1.54]
[0.40, 4.55]
[1.41, 26.12]
General urban roads
1.22
[0.33, 4.59]
Other urban roads
1.23 *
1.54
[0.97, 1.56]
[0.33, 7.26]
Type of junction (base: others)
Fork
0.62 ***
[0.50, 0.78]
Crossroads
0.62 ***
[0.50, 0.78]
(4) Environmental factors
Visibility (base: others)
Poor visibility
3.28 *
3.15 **
[0.91, 11.87]
[1.14, 8.66]
Street-light condition (base: dark)
Daylight
0.57 ***
0.22 *
0.85 (73.51 **)
[0.48, 0.69]
[0.04, 1.15]
[0.09, 8.31]
Dark but lighted
0.75 ***
2.45
[0.62, 0.91]
[0.54, 11.15]
Weather condition (base: others)
Bad weather
condition
0.29 **
[0.09, 0.92]
Holiday (base: others)
Holiday
0.13 *
[0.02, 1.04]
Time of day (base: others)
Early morning
1.27 ***
0.35 *
[1.07, 1.51]
[0.11, 1.12]
Morning peak hours
After work peak
hours
0.16 ***
[0.04, 0.62]
log likelihood
−3371.41
−219.13
−214.66
AIC
6814.83
516.27
511.31
For brevity, insigniﬁcant results are omitted, and standard deviations are presented for signiﬁcant random variables only. * p < 0.1,
** p < 0.05, *** p < 0.01.
Compared with the probability of motorcyclists running red lights on ﬁrst-class
highways, the probabilities of RLR violations by motorcyclists occurring on second-class or
below highways, and other urban roads were 1.29 times and 1.23 times higher, respectively.
For motorcyclists, the risk of running red lights at forks or crossroads is signiﬁcantly
lower than at other types of intersections. This result differs from the ﬁndings of previous
studies (e.g., [4]), likely because the surrounding conditions of motorcyclists also inﬂuence
their likelihood of running red lights. Street lighting can signiﬁcantly reduce the risk of
a motorcyclist running a red light: the risk of running a red light is lower in the daytime
(OR = 0.57) and at night with lighting (OR = 0.75) than that at night without lighting. The
risk of red light violations by motorcyclists is higher during early morning (OR = 1.27)
Int. J. Environ. Res. Public Health 2021, 18, 553
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than during off-peak hours. However, the impact of peak hours on motorcyclists’ red light
violations is insigniﬁcant, which is inconsistent with the literature [5,8]. Differing from
Yan et al. [5], neither weekends nor holidays have signiﬁcant effects on motorcyclists’ red
light violations.
3.3. Factors for the Severity of Injuries in Red-Light-Running Crashes
Although motorcyclists are less likely to be involved in RLR trafﬁc accidents than car
drivers (7.7% vs. 9.6%), crashes involving motorcyclists running a red light have higher
odds of resulting in severe casualties than car drivers (44% vs. 29%) in Guangdong, China;
therefore, further comparisons between motorcyclists and car drivers were carried out
to examine risk factors related to the severity of outcome in different types of vehicle
crashes involving a red light violation. Speciﬁcally, random-parameter logit models were
conducted for two sub-samples separately, i.e., motorcyclists and car drivers in RLR crashes.
In cases of motorcyclists in RLR crashes, as shown in the second model in Table 2, the
estimated parameters for young motorcycle riders were insigniﬁcant and random, with a
mean of 1.38 and a standard deviation of 35.53. Motorcycle riders registered as residing
in rural areas were more likely to get severely injured than those with urban residential
registration (OR = 3.21). Furthermore, a motorcyclist’s occupation impacts the level of
injury. The probability of serious casualties sustained by the self-employed (OR = 0.19)
and workers (OR = 0.14) was signiﬁcantly lower than that of farmers, but there were no
signiﬁcant differences between the probabilities of migrant workers, the unemployed and
those employed in other professions. When a head injury occurs for the motorcycle rider in
a RLR crash, the risk of serious casualties increases (OR = 11.80). With regard to the vehicle
driving state, a motorcycle rider was more strongly associated with serious casualties when
reversing, making a U-turn or changing lanes rather than going straight (OR = 10.24). The
estimated parameters for second-class or below highways were insigniﬁcant and random,
with a mean of 1.35 and a standard deviation of 11.60. Poor visibility can signiﬁcantly
increase the risk of serious casualties in motorcyclists (OR = 3.28), but daylight (OR = 0.22)
and bad weather (OR = 0.29) conditions signiﬁcantly decrease the risk of serious casualties.
The risk of serious casualties in motorcyclists is lower during early morning (OR = 0.35)
and after work peak hours (OR = 0.16) than during off-peak hours.
By comparing empirical results of the injury models for motorcyclists and car drivers in
RLR crashes, this study found that the common risk factor for motorcyclists and car drivers
experiencing severe injury in RLR crashes is poor visibility. Moreover, riders/drivers’
residential registration and occupation, vehicle driving status, weather condition, time of a
day and whether the rider/driver suffers a head injury are signiﬁcantly associated with
severe casualties in motorcycle crashes related to RLR but have no signiﬁcant effects on car
drivers in RLR crashes.
4. Discussion
4.1. Red Light Violations and Injury Severity for Motorcyclists
By comparing the inﬂuential factors that affect the probability of motorcyclists running
red lights and the severity of injuries in crashes caused by red light violations, we found
that the common risk factor is daylight condition. Rider gender, rider age, passengers,
vehicle number plates, vehicle safety conditions, road type and junction type affect the
probability of motorcyclists running red lights but do not affect the severity of injuries.
Poor visibility does not affect the likelihood of motorcyclists’ RLR violations, but in the
event of RLR crashes, this factor often leads to serious casualties. Notably, the following
factors have inconsistent effects on the probability of red light violations and the severity of
injuries for motorcycle riders: on the one hand, the likelihood of red light violations when
a motorcycle rider is turning left/right is higher than when going straight, but this turning
factor has a nonsigniﬁcant impact on the severity of injuries; on the other hand, reversing,
making a U-turn and changing lanes have nonsigniﬁcant effects on the probability of
motorcyclists’ RLR violations in contrast to going straight, but have a very signiﬁcant
Int. J. Environ. Res. Public Health 2021, 18, 553
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impact on the severity of injuries. The likelihood of red light violations during the early
morning is higher than off-peak hours, but this time factor has a negative impact on the
severity of injuries.
4.2. Comparison Between Motorcyclists and Other Road Users
Previous studies have reported on the factors contributing to trafﬁc signal violations
for car drivers, cyclists and pedestrians in Guangdong, China [2], where the effects of
gender have been found to be insigniﬁcant for all three groups. In contrast, in this study,
male motorcycle riders are conﬁrmed to be more likely to violate trafﬁc signals than female
riders. While the likelihood of violating trafﬁc signals during dark conditions is lower
than during light conditions for both car drivers and pedestrians, motorcyclists have a
signiﬁcantly increased probability of red light violations during dark conditions. Since
travelling on second-class or below highways is a factor contributing to red light violations
for car drivers, cyclists, pedestrians and motorcyclists, this factor shall not be treated as a
unique factor for motorcyclists running red lights.
For the resulting severe casualties in RLR crashes, as reported in a previous section,
poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe
injury in RLR crashes; therefore, we can infer that the visibility factor is not unique to
motorcyclists with regard to severe injuries related to red light violations.
4.3. Policy Implications and Further Remarks
The empirical evidence presented in this article suggests the need for an increase
in inspections and punishment for riding a motorcycle with a poor vehicle safety status.
In addition, supervision of red light violations should be strengthened during the early
morning hours. The lack of regard for and awareness of trafﬁc laws and safety is a major
factor affecting behavior related to running red lights. According to the empirical results in
this study, males under the age of 24 are the main target group that should be engaged in
trafﬁc safety promotion, campaigns, and educational activities.
Due to data availability, the present study analyzed the trafﬁc crash data for 2006–
2010 in Guangdong Province, China, which limits its implications, because road safety
trends may be different for other provinces in China and may have changed in the same
province over the years. Using data from other provinces and cities in China would be
of merit in future research. Moreover, a meta-analysis of old and new data from the
same province could help us to understand the signiﬁcant differences in risky behaviors
of motorcycle riders in China. Potential factors, such as trafﬁc volume, speed limits,
trafﬁc light characteristics and other risky driving behaviors among motorcyclists were
not analyzed in this study. It would be worth exploring the effects of these factors on
motorcyclists’ RLR violations and injuries in the future.
5. Conclusions
Using data collected from Guangdong Province of China, this study offers insights into
risk factors related to personal characteristics, vehicle characteristics, road conditions and
environmental conditions affecting red light violations and injury severity resulting from
motorcyclists’ running red lights. Measures including road safety educational programs
for targeted groups and focused enforcement of trafﬁc policy and regulations are suggested
to reduce the number of crashes and the severity of injuries resulting from motorcyclists
running red lights.
Author Contributions: Conceptualization, G.Z. and Y.T.; formal analysis, G.Z., Y.T. and Q.Z.; method-
ology, G.Z., Y.T. and Q.Z.; software, Q.Z.; writing—original draft, Y.T. and Q.Z.; writing—review and
editing, G.Z., Y.T., Q.Z. and R.H. All authors have read and agreed to the published version of the
manuscript.
Int. J. Environ. Res. Public Health 2021, 18, 553
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Funding: This research was supported in part by the National Natural Science Foundation of China
grant 71573286 and Ministry of Education Project for Humanities and Social Sciences Research
(16JJDGAT006).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conﬂicts of Interest: The authors declare no conﬂict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
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Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China.
Zhang, Guangnan,Tan, Ying,Zhong, Qiaoting,Hu, Ruwei