Association of Illegal Motorcyclist Behaviors and Injury Severity in Urban Motorcycle Crashes

Abstract

Motorcycle crashes have been a significant cause of death and serious injury in urban regions, which has a negative effect on the development of sustainable urban transportation. In this study, two logit models, one model for illegal motorcyclist behaviors and the other for crash injury severity, are developed using urban motorcycle crashes in Hunan Province, China. The two developed models could allow us to simultaneously identify significant factors related to specific illegal behaviors and the severity level of crashes. The results show that illegal motorcyclist behaviors, including unlicensed riding, drunk riding, and improper overtaking/lane changes, are more likely to cause serious injuries or death. Young motorcyclists are associated with a higher likelihood of illegal behaviors, while old motorcyclists are associated with a higher likelihood of serious injuries or death. Night conditions without street lights are significantly associated with a higher probability of illegal motorcyclist behaviors as well as higher injury severity. Crashes caused by drunk riding have the highest likelihood of fatal/severe injury. Based on these findings, efficient interventions are recommended to curb illegal motorcyclist behaviors and alleviate crash injury severity.

1. Introduction

Because it is typically vulnerable road users who are the casualties in traffic crashes involving motorcyclists, the issue of great concerns. In China, the motorcycle is an indispensable mode of transport. Since the 1980s, because of local government concerns about motorcycle problems such as traffic chaos, casualties, and pollution, motorcycles have been prohibited from traveling on public roads in many large cities such as Beijing, Shanghai, and Guangzhou. However, because they are a low-cost and convenient mode of transport, motorcycles are still prevalent in small to medium sized cities and rural areas. The issue of motorcycle safety is even more serious in small to medium sized cities than in rural areas because of the enormous number of motorcycles, combined with other motor vehicles, bicycles, and pedestrians. Meanwhile, due to a lack of safety consciousness and limited enforcement of traffic laws, motorcyclists in city regions are prone to violate the traffic rules, e.g., committing red-light violations, and do not often give way. This not only increases the crash risk of motorcyclists, but poses an increased risk for other road users. Thus, there is an urgent need to examine the risk factors that affect motorcycle crashes in the small and medium sized cities of China.

Compared to high-income countries, there are several special motorcycle-related features of low- and middle-income countries such as China. First, in high-income countries, motorcycles are commonly used for recreation, while in low- and middle-income countries they are more prevalent for transport and commercial purposes. Second, there are significantly different road and traffic environments in low- and middle-income countries, i.e., more congested traffic, poor transport facilities, and a greater mix of motor vehicles, bicycles, and pedestrians. Third, due to a lack of adequate education and lax law enforcement, the illegal behavior of motorcyclists, as well as the incorrect use of motorcycle safety devices, are more prevalent in low- and middle-income countries [1]. Fourth, in low- and middle-income countries, investments and infrastructure design facilitating road safety are mainly focused on motor vehicle transport; safe road infrastructure for vulnerable traffic groups is deficient [2,3]. Due to these differences in the use of motorcycles, the amount of riding exposure, and the prevalence of motorcycle riders, research findings in the high-income countries may not be applicable to the issues particular to China.

Using data of urban motorcycle crashes in Hunan Province, China, we identify the association between the motorcyclists’ injury severity, illegal behaviors, and other risk factors such as motorcyclist age and weather and light conditions. The aim of this study is to provide vital information about urban motorcycle safety in China that can assist in the formulation of evidence-based safety strategies for this prevalent mode of transport in low- and middle-income countries. The literature review is presented in Section 2. The methods relating to the data and the statistical model used are outlined in Section 3. Section 4 presents the results of the model calibrations. Section 5 discusses the findings by combining the results obtained in this study with previous research. Conclusions and suggestions for motorcycle safety strategies are outlined in Section 6.

2. Literature Review

Over the past three decades, considerable research efforts have addressed powered two-wheeled vehicles such as motorcycles and electric bicycles [1,4]. Since both motorcycle and electric bicycle crashes are accompanied by a higher likelihood of severe and fatal injury compared with other motor vehicle crashes, much of the previous research has focused on examining the factors that influence the severity of the injuries resulting from a crash. These significant factors can mainly be divided into the following groups: protective technology equipment (including helmets, headlights, and antilock brake systems) [5,6,7]; individual characteristics (such as age, gender, and behavior, e.g., speeding and alcohol-impaired riding behaviors) [4,8,9]; collision type (such as single motorcycle crash, collision with a pedestrian, and collision with a vehicle) [9,10]; road infrastructure and traffic control (such as geometry, road markings, separate lanes, collision-free crossings, barriers) [10,11]; and weather and light conditions [12].

On the other hand, because motorcyclists exhibit a lower degree of risk aversion relative to motor vehicle drivers [4,13], some scholars have concentrated on identifying the factors related with the risky/illegal behaviors of motorcyclists [14,15,16,17,18,19,20]. For example, previous studies have found that young motorcyclists and those with a low educational level are associated with a high probability of risky behaviors, such as not wearing a helmet while riding and alcohol-impaired riding [4,14]. With regard to the roadway and environment, motorcyclists are more likely to violate rules when riding on roads with high speed limits, on the curved sections of roads, during the night, and on the weekend [20].

However, the existing research on motorcycle safety is mainly conducted from high-income countries. Nevertheless, a few studies have focused on motorcyclist-related safety issues in China, mainly the use of motorcycle helmets [21] and the injury severity of motorcyclists [22,23]. A comprehensive analysis of the illegal motorcyclist behaviors that are the predominant contributing factors to motorcycle-related crashes, particularly in small to medium sized cities in China, is still lacking.

3. Methods

3.1. Objective and Research Strategy

Preventing the occurrence of crashes and reducing the injury severity resulting from crashes are two primary strategies to enhance traffic safety. For motorcycle crashes in urban regions, illegal motorcyclist behaviors are regarded as the most critical risk factors which contribute to the occurrence of motorcycle-related crashes. Furthermore, motorcycle crashes tend to have a higher fatal/severe injury risk than other motor vehicle crashes. Thus, two logit models, one model for the motorcyclist’s illegal behaviors and the other for crash injury severity, were developed using motorcycle crashes in small to medium sized cities in Hunan Province, China. This allowed us to simultaneously identify significant factors related to specific illegal behaviors and the severity level of injuries resulting from crashes. Based on this, appropriate countermeasures that improve China motorcycle safety are recommended.

3.2. Data Preparation

Hunan Province is located in South Central China, with an area of over two million km² and a population of nearly 70 million. In 2003, Changsha, the capital of Hunan province, became the first city in Hunan to forbid motorcycles from travelling on main roads. Since then, four other cities at the prefecture level have also introduced the same motorcycle policy. However, in most small and medium cities, and in rural areas, motorcycles are still prevalent as an important mode of transportation.

The crash data for this analysis originated from the Traffic Accident Database System (TADS), which is maintained by the Traffic Administration Bureau of Hunan Province. The information of TADS covers many aspects of a crash, such as pre-crash illegal behaviors, injury severity, driver/rider demographics, traffic conditions, road type, and environment. In China, the severity of an injury resulting from a crash is divided into four levels: fatal, serious, slight, and property damage only (PDO). Since cases of PDO are always handled by police officers by way of procedure, several useful variables related to PDO crashes are incomplete or missing. Thus, in this study, the PDO crashes were not considered. In this study, data from motorcycle crashes that occurred from January 2014 to December 2016 in Hunan Province were first extracted from TADS. Subsequently, PDO crashes, crashes that did not occur in urban regions, and a few crashes with substantial missing information, were removed from the dataset. Finally, 2431 motorcycle crashes were identified for the analysis.

This study intends to examine the factors contributing to illegal motorcyclist behaviors and injury severity. Illegal behaviors represent the police-reported pre-crash illegal behaviors for motorcyclists or drivers who were identified to be at-fault in the crash. The illegal behaviors of the motorcyclists are categorized in eight classes: unlicensed riding, failing to give way, alcohol-impaired riding, improper overtaking/lane changes, reversing/turning negligently, riding on the wrong side of the road, disobeying traffic signals/lights, and riding on forbidden roadways. The injury severity of the motorcyclists is categorized in three levels: fatal, serious, and slight. The selected independent variables include crash pattern, light conditions, weather conditions, location of crash, time of day, and presence or absence of a passenger, as well as motorcyclist characteristics such as age, gender, and helmet use. Descriptive statistics of variables are summarized in

Table 1. Descriptive statistics of variables.

Variables	Frequency	Proportion %	Variables	Frequency	Proportion %
Injury severity			Helmet use
Fatality	224	9.2	Using helmet	465	19.1
Serious injury	129	5.3	Not using helmet *	1966	80.9
Slight injury *	2078	85.5	Passenger
Irregular maneuver			With passenger	642	26.4
Unlicensed riding	353	14.5	Without passenger *	1789	73.6
Failing to give way	738	30.4	Crash pattern
Alcohol-impaired riding	42	1.7	Single-vehicle crash	319	13.1
Improper overtaking or lane changes	179	7.4	Multi-vehicle crash *	2112	86.9
Reversing/turning negligently	117	4.8	Light condition
Riding on the wrong side of the road	132	5.4	Night with light	623	25.6
Disobeying traffic signals/lights	147	6.0	Darkness	139	5.7
Riding on forbidden roadways	76	3.1	Daylight *	1669	68.7
No motorcyclist factor *	647	26.6	Weather condition
Age			Rainy	338	13.9
Young (≤20)	246	10.1	Snow/fog	2066	1.1
Middle (21–60) *	2058	84.7	Clear *	27	85.0
Old (≥61)	127	5.2	Location of crash
Gender			Segment	1430	58.8
Female	156	6.4	Intersection *	1001	41.2
Male *	2275	93.6	Time of day
			Peak time (7 a.m.–9 a.m., 5 p.m.–7 p.m.)	725	29.8
			Non-peak time *	1706	70.2

Note: * represents the control variable.

.

3.3. Multinomial Logit (MNL) Model

Irregular motorcyclist maneuvers have multiple discrete and unordered outcomes, and this can be developed well by applying the MNL model [24]. Thus, this study used the MNL model to identify significant factors that affect specific pre-crash irregular motorcyclist maneuvers, in comparison with the crashes without irregular motorcyclist maneuvers (cases in which the motorcyclist is identified as the not-at-fault party). The model is specified as:

$$P(i)=\frac{\exp \left[{\mathsf{\alpha }}_i+{\mathsf{\beta }}_i{X}_i\right]}{{\displaystyle \sum \exp \left[{\mathsf{\alpha }}_I+{\mathsf{\beta }}_I{X}_I\right]}}$$

(1)

where $P(i)$ is the probability of a motorcyclist engaging in illegal behavior $i$; $X_i$ is the vector of explanatory variables; ${\mathsf{\beta }}_i$ is the vector of coefficient estimates; ${\mathsf{\alpha }}_i$ is the intercept; and $I$ is the number of classifications of illegal behaviors (including the reference classification, no motorcyclist factor, $I=9$ in this study).

A generalized Hausman test was used to test the independence of irrelevant alternatives (IIA) property, which is the underlying restrictive property of the MNL model [22]. This restrictive property means that the probability ratio for two irregular maneuvers is necessarily the same, whether or not there are other alternative irregular maneuvers. Specifically, if we remove one irregular maneuver i from the alternatives, the coefficient estimator of the full model ${\widehat{\mathsf{\beta }}}_F$ is consistent with the estimator of the removed model ${\widehat{\mathsf{\beta }}}_R$; it will then hold the null hypothesis of the IIA property. The results of the Hausman test in this study are presented in

Table 2. Hausman tests of IIA assumptions.

Removed Irregular Maneuver	${\mathit{\chi }}^2$-Value	$\mathit{p}$-Value	Null Hypothesis	IIA Property
Unlicensed riding	0.18	>0.1	Fail to reject	Hold
Failing to give way	0.07	>0.1	Fail to reject	Hold
Alcohol-impaired riding	1.02	>0.1	Fail to reject	Hold
Improper overtaking/lane changes	0.85	>0.1	Fail to reject	Hold
Reversing/turning negligently	0.35	>0.1	Fail to reject	Hold
Riding on the wrong side of the road	0.34	>0.1	Fail to reject	Hold
Disobeying traffic signals/lights	0.22	>0.1	Fail to reject	Hold
Riding on forbidden roadways	0.12	>0.1	Fail to reject	Hold
No motorcyclist factor	0.05	>0.1	Fail to reject	Hold

. There is no reason to reject the null hypothesis or IIA property because all the p-values are greater than 0.1. This also indicates that the application of the MNL model is reasonable.

3.4. Ordered Logit (OL) Model

In contrast with illegal behaviors, we often categorize injury severity as a discrete ordered variable such as fatality, serious injury, and slight injury. In this study, we employ the OL model to identify factors affecting motorcyclist injury severity, in accordance with many previous studies in the field of road safety. In the OL model, the probability of injury severity for a given crash can be specified as:

$$P(y_i>j)=g(X_i\mathsf{\beta })=\frac{\exp \left[{\mathsf{\alpha }}_j+\mathsf{\beta }{X}_i\right]}{1+\exp \left[{\mathsf{\alpha }}_j+\mathsf{\beta }{X}_i\right]} j=1,2$$

(2)

where $X_i$ is the vector of explanatory variables; ${\mathsf{\alpha }}_j$ represents the cut-off point for the jth cumulative logit; and $\mathsf{\beta }$ is the vector of coefficient estimates. $\mathsf{\beta }$ is fixed across equations in the OL model, which is the major difference between the OL model and the MNL model.

4. Results

Two logit models, an MNL model for illegal motorcyclist behaviors and an OL model for motorcyclist injury severity, were developed. For model estimation, we used the backward stepwise method to exclude the insignificant variables at the significance level with p-value > 0.1, and the final model only obtained the statistically significant variables. The coefficient estimates and standard errors of the MNL model for illegal behaviors are presented in

Table 3. MNL model estimates for illegal motorcyclist behaviors.

Variable	Unlicensed Riding			Failing to Give Way
	Coefficient Estimate	Standard Error	Odds Ratio	Coefficient Estimate	Standard Error	Odds Ratio
Intercept	−0.832	0.156
Peak time	−0.262	0.161	0.769
Single-vehicle crash	0.748	0.194	2.114	0.688	0.170	1.989
young (≤20)	0.672	0.216	1.958
Variable	Alcohol-impaired riding			Improper overtaking/lane changes
	Coefficient estimate	Standard error	Odds ratio	Coefficient estimate	Standard error	Odds ratio
Intercept	−3.095	0.394		−1.283	0.198
Single-vehicle crash				−2.810	1.015	0.060
Night with light	1.594	0.380	4.922	−0.602	0.239	0.548
Darkness	2.298	0.514	9.955
Female				0.587	0.308	1.798
With passenger				0.463	0.185	1.589
Variable	Reversing/turning negligently			Riding on the wrong side of the road
	Coefficient estimate	Standard error	Odds ratio	Coefficient estimate	Standard error	Odds ratio
Intercept	−1.667	0.233		−2.045	0.232
Single-vehicle crash	−1.792	0.731	0.167	−2.359	0.217	0.095
Night with light				0.509	0.730)	1.663
Darkness				1.190	0.221	3.286
young (≤20)				0.698	0.290	2.010
With passenger	0.421	0.208	1.523
Variable	Disobeying traffic signals/lights			Riding on forbidden roadways
	Coefficient estimate	Standard error	Odds ratio	Coefficient estimate	Standard error	Odds ratio
Intercept				−2.058	0.269
Peak time	0.513	0.200	1.670
Night with light	0.594	0.210	1.812
Segment	−0.534	0.191	0.586
Female				0.660	0.321	1.935

. The estimated OL model for injury severity is presented in

Table 4. OL model estimates for motorcyclist injury severity.

Variable	Coefficient Estimate	Standard Error	Odds Ratio
Intercept
Serious injury	1.738	0.087
Fatality	2.263	0.096
illegal behavior (“no motorcyclist factor” as reference)
Unlicensed riding	0.315	0.159	1.370
Alcohol-impaired riding	0.838	0.351	2.313
Improper overtaking/lane changes	0.449	0.207	1.567
Single-vehicle crash	−0.506	0.195	0.603
Light condition (“daylight” as reference)
Darkness	0.909	0.202	2.481
Female	−0.581	0.284	0.559
Age (“21–60” as reference)
Old (≥61)	0.350	0.230	1.419
Helmet use	−0.324	0.161	0.723
Passenger	−0.327	0.140	0.721
Summary statistics
Number of observations	2431
−2 Log-likelihood	323.745

.

To directly interpret the effect of the explanatory variables, we calculated the exponential of coefficients to obtain odds ratio (OR) estimates in both the MNL and OL models. This ratio indicates the relative amount by which the odds of an event of interest (i.e., specific illegal behaviors or injuries of a specific severity) increases (when OR > 1) or decreases (when OR < 1) when the value of the corresponding independent variable increases by one unit. The OR values of the estimated coefficients for the two models were calculated and are listed in Table 3 and Table 4.

4.1. Analysis of Illegal Motorcyclist Behaviors

Most of these crashes were associated with illegal motorcyclist behaviors (73.4%). Regarding specific illegal motorcyclist behaviors, failing to give way accounts for the largest proportion (30.4%), and unlicensed riding is ranked second (14.5%). Crashes caused by alcohol-impaired riding account for the lowest proportion (1.7%). In addition, more than 90 percent of crash-involved motorcyclists were male. The distribution of illegal motorcyclist behaviors is illustrated in Table 1.

Parameter estimates for illegal behaviors are shown in Table 3. The demographic information of the motorcyclist has a significant influence on the illegal behavior of the motorcyclist. Young motorcyclists below 20 years of age were more likely to be involved in crashes due to unlicensed riding (OR = 1.958) and riding on the wrong side of the road (OR = 2.010) compared with motorcyclists 21–60 years of age. Female motorcyclists were more likely than male motorcyclists to be involved in crashes due to improper overtaking/lane changes (OR = 1.798) and riding on forbidden roadways (OR = 1.935).

Environmental factors have a significant effects as well. With regard to the light condition, the results show that the crash risk resulting from motorcyclist drunk riding increased at night (both with and without light); furthermore, this increased risk is even higher in darkness (OR = 9.955) than at night with light (OR = 4.922). Similarly, motorcyclists were more likely to ride on the wrong side of the road at night (OR = 1.633 for night with light; OR = 3.286 for darkness). Moreover, at night with light, motorcyclists were more likely to disobey traffic signals/lights (OR = 1.812), while they were less likely to engage in improper overtaking/lane changes (OR = 0.548) than in daylight. Concerning the effect of time, crashes occurring at peak time (7 a.m.–9 a.m. and 5 p.m.–7 p.m.) were more likely to be the result of motorcyclists disobeying traffic signals/lights (OR = 1.670) and less likely to be the result of unlicensed riding (OR = 0.769). In the presence of passengers, motorcyclists were more likely to be involved in crashes because of improper overtaking/lane changes (OR = 1.589) and reversing/turning negligently (OR = 1.523). The crash pattern was also significantly associated with illegal behaviors. Single-vehicle crashes were more likely to result from the motorcyclists’ unlicensed riding (OR = 2.114) and failing to give way (OR = 1.989), while they were less likely due to improper overtaking/lane changes (OR = 0.067), reversing/turning negligently (OR = 0.167), and riding on the wrong side of the road (OR = 0.095).

4.2. Analysis of Motorcyclist Injury Severity

Table 4 shows the factors that affected the injury severity of motorcyclists in motorcycle-related crashes. Of the eight types of illegal motorcyclist behaviors, three types, including unlicensed riding (OR = 1.370), alcohol-impaired riding (OR = 2.313), and improper overtaking/lane changes (OR = 1.567), significantly increased the risk of injury severity, compared with cases in which the motorcyclists were not-at-fault. Riding after drinking alcohol was the most significant cause, related with highest likelihood of fatal/severe injury.

Compared with the middle age group (21–60), motorcyclists aged over 61 were more likely to sustain fatal/severe injuries when they were involved in crashes (OR = 1.419). Female motorcyclists experienced fewer fatal/severe injuries than male motorcyclists (OR = 0.559). Helmet use by motorcyclists showed a protective effect and was associated with a lower likelihood of fatal/severe crashes (OR = 0.723). The presence of a passenger was also a significant factor associated with a lower probability of fatal/severe injuries (OR = 0.721). Regarding environmental related conditions, night time riding in the absence of street lights was more likely to result in a serious injury (OR = 2.481) than day time riding and riding at night with street lights. With regard to the crash pattern, single-vehicle crashes had a significantly lower probability of resulting in fatal/severe injuries (OR = 0.603).

4.3. Summary

The significant risk factors related to motorcycle crashes are summarized in

Table 5. A summary of risk factors influencing illegal motorcyclist behaviors and injury severity.

Variable	Injury Severity	Illegal Behaviors
		type1	type2	type3	type4	type5	type6	type7	type8
Young (≤20)		↑					↑
Old (≥61)	↑
Female					↑				↑
Night with light				↑	↓		↑	↑
Darkness	↑			↑			↑
Segment								↓
Peak time		↓
Helmet use	↓
Passenger	↓				↑	↑
Single-vehicle crash	↓	↑	↑		↓	↓	↓

. In the Table 5, “type1” to “type8” represent, respectively, unlicensed riding, failing to give way, Alcohol-impaired riding, improper overtaking/lane changes, reversing/turning negligently, riding on the wrong side of the road, disobeying traffic signals/lights, and riding on forbidden roadways. “↑” indicates that the explanatory variable has a significantly positive effect on the response variable while “↓” indicates that the explanatory variable has a significantly negative effect on the response variable.

Based on their different effects on illegal behaviors and injury severity, these risk factors can be divided to three categories. The first category relates to the factors that simultaneously increase (or decrease) the risk of injury severity and illegal behaviors, such as riding in darkness. This is ranked as the most important risk factor because of its safety effects in simultaneously decreasing the illegal behaviors of motorcyclists (thereby decreasing the risk of a crash) and reducing the severity of motorcycle crashes. The second category covers the factors that are solely significant for either injury severity or illegal behaviors, including the age of the motorcyclist being under 20 or over 60, the motorcyclist being female, riding at night with light, segment, peak time, and helmet use. These are ranked in the second level of important factors. The third category incorporates the factors that have inconsistent effects on illegal behaviors and injury severity, such as riding with a passenger and crashes involving only a single vehicle. This category of factors should be carefully considered since they have contradictory effects on the likelihood of crash occurrence and injury severity.

5. Discussion

Our study shows that nearly three quarters of motorcycle-related crashes occurring in city regions are the result of illegal motorcyclist behaviors. This implies that motorcyclists in China have a poor perception of road safety and hazardous driving behaviors. Moreover, a lack of strict traffic law enforcement for motorcyclist violations may be another important cause of the high proportion of motorcyclist faults in crashes.

Our study also shows that illegal motorcyclist behaviors such as unlicensed riding, drunk riding, and improper overtaking/lane changes are important factors affecting whether a crash will result in fatality or serious injury. In accordance with previous research conducted in the US and other countries [4], we found in our study that riding without a valid license is associated with a higher probability of severe injury. This may be due to the fact that the lack of a license is correlated with less driving experience, a greater propensity to adopt risky behaviors, and lack of protective vehicle-related facilities; all of these factors increase the risk of motorcycle crashes and increase the likelihood of fatal/serious injuries. The higher injury severity observed among motorcyclists involving alcohol was consistent with the findings of many previous studies [1,5,17]. Improper overtaking/lane changes are associated with a higher risk of accidents resulting in fatal/serious injury; this result had not been revealed in previous studies. A possible explanation might be that overtaking or lane-changing behaviors are always associated with the sudden acceleration of the motorcycle, and thus increase the risk of severe injury. An important implication can be obtained from this result: if the rate of illegal motorcyclist behavior could be effectively controlled, correspondingly, the rate of serious injuries and fatalities would be substantially decreased.

Many previous studies have demonstrated that young motorcycle riders have a stronger propensity for risk-taking behavior/being at fault [4]. Our study shows a similar result, i.e., that motorcyclists aged under 20 years were associated with a higher likelihood of illegal behaviors, including unlicensed riding and riding on the wrong side of the road. The higher injury severity observed among motorcyclists aged over 60 years is in accordance with the findings of previous research [9,10]. This may be due to the physical fragility and decreased riding ability of older riders. Female motorcyclists were more prone to crashes caused by improper overtaking/lane changes and riding on forbidden roadways. This implies that female motorcyclists have relatively poor driving skills and less knowledge of traffic rules compared with males. In addition, there were no significant gender differences in injury severity resulting from the crashes.

Helmet use significantly decreased injury severity in our study, which is consistent with many prior studies [1,5]. However, there was no significant association between helmet use and illegal behaviors. This is not in accordance with previous findings, which suggest that riding without a helmet is always correlated with other risk-taking behaviors [13]. Another notable issue is the fairly low rate of helmet use in our sample (19.1%), although helmet use is an effective means of reducing the severity of motorcycle crash injuries. This number is much lower than the results of previous observational studies in more developed regions of China [21]. Two possible reasons may explain this. In previous studies, the helmet-wearing rate calculation came from random samples that were obtained from direct roadside observation of motorcycle riders, while in the present study it came from the selected motorcyclists who were involved in crashes. This can explain part of the difference. There may also be a difference in safety awareness, traffic law enforcement, and riding behaviors between distant geographic locations.

Light conditions exhibit significant associations with both illegal behaviors and injury severity. Of the three types of light conditions (daylight, night with light, and darkness), the crashes that occurred in darkness had highest probability of resulting in severe injuries or fatality, as well as the highest likelihood of involving illegal motorcyclist behaviors, including drunk riding and riding on the wrong side of the road. These results are consistent with those of previous studies [10,11,12]. We can explain this result with the following factors. First, police and equipment-aided enforcement directed toward alcohol-impaired riding and other rule violations are commonly less strict in dark road conditions. Second, with fewer vehicles on the road at night than in daylight, the motorcyclist is more likely to engage in riskier behaviors such as driving on the wrong side of the road. Third, the reduced visibility would more likely lead to an increased perception time in road users and weaken their risk avoidance ability. Therefore, when a crash occurs at night without street lights, it would be more likely to cause severe injury or death.

6. Conclusions and Suggestions

The issue of motorcyclist safety in China is crucial. This study has developed two logit models to simultaneously identify factors contributing to motorcyclists’ pre-crash illegal behaviors and crash injury severity, using crash data for small- to medium-sized cities in Hunan Province, China. The key findings are as follows:

• Illegal Motorcyclist behaviors are the major cause of motorcycle-related crashes; furthermore, traffic crashes due to illegal motorcyclist behaviors (such as unlicensed riding, drunk riding, and improper overtaking/lane changes) are more likely to result in serious injuries or death.
• Young motorcyclists are associated with a higher likelihood of illegal behaviors (such as unlicensed riding and riding on the wrong side of the road), while old motorcyclists have a higher likelihood of suffering serious injuries or death.
• Riding at night without street lights was significantly associated with a higher probability of illegal motorcyclist behaviors (such as drunk riding and riding on the wrong side of the road) as well as higher injury severity.
• Helmet use was found to result in a significant decrease in injury severity. In addition, the helmet use rate for motorcyclists that were involved in a crash was fairly low.
• Crashes caused by drunk riding had the highest likelihood of resulting in fatal/severe injury.

Efficient interventions can be suggested to curb illegal motorcyclist behaviors and alleviate crash injury severity.

• Implementing better motorcyclist licensing and road safety awareness campaigns on safe riding behaviors are critical for improving motorcycle-related safety, especially for young motorcyclists.
• Improving road lighting at night is an important measure in reducing illegal motorcyclist behaviors and crashes resulting in fatal/severe injury.
• Education and enforcement concerning alcohol use and helmet use for motorcyclists should be strengthened.

Overall, our study examines the risk factors affecting injury severity and illegal behaviors, which could assist in the formulation of evidence-based safety strategies for motorcycles in the urban regions of China. Since the data used for this study originated from information recorded by the police, they did not include a comprehensive range of crash-contributing factors, such as the motorcyclists’ behavioral/psychological status and physical condition. In future studies, data on more contributing factors should be collected through the integration of multi-source data, such as questionnaire surveys and field observations.