A multinomial logit analysis of factors associated with severity of motorcycle crashes in Ghana

Objective: The objective of this study was to explore the factors affecting motorcycle crash severity in Ghana.

Methods: A retrospective analysis of motorcycle crash data between 2011 and 2015 was conducted using a motorcycle crash data set extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. Injury severity was classified into 4 categories: Fatal, hospitalized, injured, and damage only. A multinomial logit modeling framework was used to identify the possible determinants of motorcycle crash severity.

Results: During the study period, a total of 8,516 motorcycle crashes were recorded, of which 22.9% were classified as fatal, 42.1% were classified as hospitalized injuries, 29.4% were classified as slight injuries, and 5.6% were classified as damage-only crashes. The estimation results indicate that the following factors increase the probability of fatal injuries: At a junction; weekend; signage; poor road shoulder; village settlement; tarred and good road surface; and collision between motorcycle and heavy goods vehicle (HGV). Motorcycle crashes occurring during the daytime and on the weekend increases the probability of hospitalized injury. The results also suggest that motorcycle crashes occurring during the daytime, in curves or inclined portions of roads, or in unclear weather conditions decrease the probability of fatal injury.

Conclusions: This study provides further empirical evidence to support motorcycle crash modeling research, which is lacking in developing countries. The ability to understand the various factors that influence motorcycle crash severity is a step forward in providing an appropriate basis upon which informed motorcycle crash policies can be developed. Particular attention should be given to the provision of road signage at junctions and speed humps and controlling traffic during the weekend. In addition, road maintenance should be carried out periodically to address motorcycle safety in Ghana.     

Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model

ProblemThe severity of motorway accidents that occurred on the hard shoulder (HS) is higher than for the main carriageway (MC). This paper compares and contrasts the most important factors affecting the severity of HS and MC accidents on motorways in England.MethodUsing police reported accident data, the accidents that occurred on motorways in England are grouped into two categories (i.e., HS and MC) according to the location. A generalized ordered logistic regression model is then applied to identify the factors affecting the severity of HS and MC accidents on motorways. The factors examined include accident and vehicle characteristics, traffic and environment conditions, as well as other behavioral factors.ResultsResults suggest that the factors positively affecting the severity include: number of vehicles involved in the accident, peak-hour traffic time, and low visibility. Differences between HS and MC accidents are identified, with the most important being the involvement of heavy goods vehicles (HGVs) and driver fatigue, which are found to be more crucial in increasing the severity of HS accidents.Practical applicationsMeasures to increase awareness of HGV drivers regarding the risk of fatigue when driving on motorways, and especially the nearside lane, should be taken by the stakeholders.     

Investigation of key factors for accident severity at railroad grade crossings by using a logit model

Although several studies have used logit or probit models and their variants to fit data of accident severity on roadway segments, few have investigated accident severity at a railroad grade crossing (RGC). Compared to accident risk analysis in terms of accident frequency and severity of a highway system, investigation of the factors contributing to traffic accidents at an RGC may be more complicated because of additional highway–railway interactions. Because the proportional odds assumption was violated while fitting cumulative logit modeled by the proportional odds models with stepwise variable selection to ordinal accident severity data collected at 592 RGCs in Taiwan as suggested by Strokes et al. [Strokes, M.E., Davis, C.S., Koch, G.G., 2000. Categorical Data Analysis Using the SAS System, second ed. SAS Institute, Inc., Cary, NC, p. 249], a generalized logit model with stepwise variable selection was used instead to identify explanatory variables (factors or covariates) that were significantly associated with the severity of collisions. Hence, the fitted model was used to predict the level of accident severity, given a set of values in the explanatory variables. Number of daily trains, highway separation, number of daily trucks, obstacle detection device, and approaching crossing markings significantly affected levels of accident severity at an RGC (p-value = 0.0009, 0.0008, 0.0112, 0.0017, and 0.0003, respectively). Finally, marginal effect analysis on the number of daily trains and law enforcement camera was conducted to evaluate the effect of the number of daily trains and presence of a law enforcement camera on the potential accident severity.     

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.     

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, “adverse weather,” which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.     

Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

Investigating the risk factors associated with pedestrian injury severity in Illinois

IntroductionPedestrians are known as the most vulnerable road users, which means their needs and safety require specific attention in strategic plans. Given the fact that pedestrians are more prone to higher injury severity levels compared to other road users, this study aims to investigate the risk factors associated with various levels of injury severity that pedestrians experience in Illinois.MethodOrdered-response models are used to analyze single-vehicle, single-pedestrian crash data from 2010 to 2013 in Illinois. As a measure of net change in the effect of significant variables, average direct pseudo-elasticities are calculated that can be further used to prioritize safety countermeasures. A model comparison using AIC and BIC is also provided to compare the performance of the studied ordered-response models.ResultsThe results recognized many variables associated with severe injuries: older pedestrians (more than 65 years old), pedestrians not wearing contrasting clothing, adult drivers (16–24), drunk drivers, time of day (20:00 to 05:00), divided highways, multilane highways, darkness, and heavy vehicles. On the other hand, crossing the street at crosswalks, older drivers (more than 65 years old), urban areas, and presence of traffic control devices (signal and sign) are associated with decreased probability of severe injuries.Conclusions and practical applicationsThe comparison between three proposed ordered-response models shows that the partial proportional odds (PPO) model outperforms the conventional ordered (proportional odds—PO) model and generalized ordered logit model (GOLM). Based on the findings, stricter rules to address DUI driving is suggested. Educational programs need to focus on older pedestrians given the increasing number of older people in Illinois in the upcoming years. Pedestrians should be educated to use pedestrian crosswalks and contrasting clothing at night. In terms of engineering countermeasures, installation of crosswalks where pedestrian activity is high seems a promising practice.     

Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

IntroductionAs a convenient and affordable means of transportation, the e-bike is widely used by different age rider groups and for different travel purposes. The underlying reasons for e-bike riders suffering from severe injury may be different in each case.MethodThis study aims to examine the underlying risk factors of severe injury for different groups of e-bike riders by using a combined method, integration of a classification tree and a logistic regression model. Three-year of e-bike crashes occurring in Hunan province are extracted, and risk factor including rider’s attribute, opponent vehicle and driver’s attribute, improper behaviors of riders and drivers, road, and environment characteristics are considered for this analysis.ResultsE-bike riders are segmented into five groups based on the classification tree analysis, and the group of non-occupational riders aged over 55 in urban regions is associated with the highest likelihood of severe injury among the five groups. The logistics analysis for each group shows that several risk factors such as high-speed roads have commonly significant effects on injury severity for different groups; while major factors only have significant effects for specific groups.Practical applicationBased on model results, policy implications to alleviate the crash injury for different e-bike riders groups are recommended, which mainly include enhanced education and enforcement for e-bike risky behaviors, and traffic engineering to regulate the use of e-bikes on high speed roads.     

Road traffic accident severity analysis: A census-based study in China

Background: In China, despite the decrease in average road traffic fatalities per capita, the fatality rate and injury rate have been increasing until 2015. Purpose: This study aims to analyze the road traffic accident severity in China from a macro viewpoint and various aspects and illuminate several key causal factors. From these analyses, we propose possible countermeasures to reduce accident severity. Method: The severity of traffic accidents is measured by human damage (HD) and case fatality rate (CFR). Different categorizations of national road traffic census data are analyzed to evaluate the severity of different types of accidents and further to demonstrate the key factors that contribute to the increase in accident severity. Regional data from selected major municipalities and provinces are also compared with national traffic census data to verify data consistency. Results: From 2000 to 2016, the overall CFR and HD of road accidents in China have increased by 19.0% and 63.7%, respectively. In 2016, CFR of freight vehicles is 33.5% higher than average; late-night accidents are more fatal than those that occur at other periods. The speeding issue is severely becoming worse. In 2000, its CFR is only 5.3% higher than average, while in 2016, the number is 42.0%. Conclusion and practical implementation: A growing trend of accident severity was found to be contrasting to the decline of road traffic accidents. From the analysis of casual factors, it was confirmed that the release way of the impact energy and the protection worn by the victims are key variables contributing to the severity of road traffic accidents.     

Injuries associated with the use of riding mowers in the United States, 2002-2007

Context: To examine injuries among patients treated in an emergency department (ED) related to the use of a riding lawn mower.Design and Setting: Data were obtained from the National Electronic Injury Surveillance System for the years 2002-2007. National estimates of ED visits for injuries associated with the use of a riding lawn mower were analyzed. Narrative text entries were categorized to provide a detailed record of the circumstances precipitating the injury. Average annual rates were calculated and logistic regression analyses were employed to determine risk estimates for patient disposition and demographic characteristics related to ED visits for injuries associated with riding mowers.Results: From 2002 through 2007, there were an estimated 66,341 ED visits for injuries related to the use of riding lawnmowers in the U.S., with an average annual rate of 6.0 ED visits per 100,000 males, and 1.6 ED visits per 100,000 females. Older adults had higher rates of ED visits for injuries (7.2/100,000) than younger age groups. The most common injuries involved contusions (24%); sprains/strains (22%) and fractures (17%). The majority of patients (90%) were treated and released the same day. Results of logistic regression analyses revealed that older adults were more likely to be hospitalized when compared to younger age groups; and incidents involving rollovers [OR = 5.45 (95% CI = 3.22-9.23)] and being run over [6.01 (95% CI 3.23-11.17)] were more likely to result in hospitalization when compared to all other circumstances of injury.Conclusions: Riding mowers present injury patterns and circumstances that are different than those reported for push mowers. Circumstances related to injuries and age groups affected were varied, making prevention of riding mower injuries challenging. Application/Impact: Findings support the need to increase awareness and/or change the design of riding mowers with respect to risk of rollover injuries.     

Exploring motorcyclist injury severity resulting from various crash configurations at T-junctions in the United Kingdom--an application of the ordered probit models

OBJECTIVES: The fact that motorcycle users tend to be more vulnerable to injuries than those using other motorized vehicles may act synergistically with the complexity of conflicting movements between vehicles and motorcycles to increase injury severity in a junction-type accident. A junction-type collision tends to be more severe than a non-junction case due to the fact that some of the injurious crashes such as angle-collision commonly occur. Existing studies have applied several statistical modeling techniques to examine influential factors on the occurrences of different crashes among motorized vehicles but surprisingly very little has empirically explored whether a particular crash type, resulting from a junction-type accident, is more injurious to motorcyclists. This article attempts to investigate whether a particular collision is more deadly to motorcyclists conditioned on crash occurrence at T-junctions in the U.K., while controlling for environment, vehicle, and demographic factors. METHODS: The statistical modeling technique employed is the ordered probit models using the data extracted from the STATS19 accident injury database (1999-2004). RESULTS: The modeling found determinants of injury severity among motorcyclists at T-junctions in the U.K. For example, an approach-turn/head-on collision is much more injurious to motorcyclists; and, those riding in early morning (i.e., 0000-0659) are more likely to be severely injured. CONCLUSIONS: This study offers a guideline for future research, as well as insight into potential prevention strategies that might help moderate motorcyclist injuries.     

A modified accident analysis and investigation model for the general aviation industry: Emphasizing on human and organizational factors

IntroductionCurrently, there is a lack of specific analytical tools for general aviation accidents (GAAs). This has led to loopholes in the prevention of GAAs.MethodsA Swiss Cheese model for general aviation (SCM-GA) is proposed to identify the human and organizational factors involved in GAAs. In the proposed SCM-GA, 5 categories, 45 subcategories, a general aviation safety management system (GA-SMS) and safety culture were developed based on the classic accident causation models combined with the laws and regulations and safety management practices in the general aviation industry.ResultsOne GAA was analyzed using SCM-GA. The human and organizational causes revealed by SCM-GA were more complete than the causes revealed through the accident report. The identification results of the deficiencies in the subcategories of GA-SMS and the safety culture were more consistent with the requirements in the general aviation laws and regulations than the organizational factors in the accident report. Based on the subcategories of SCM-GA, 41 GAAs that occurred between 1996 and 2010 in China were statistically analyzed and χ² test analyses were performed to estimate the statistical strength of the association between two adjacent subcategories of SCM-GA. The results showed that two adjacent subcategories of SCM-GA were significantly associated. They helped to determine the hidden problems in the accident report based on the path of accident.ConclusionsSCM-GA is an accident analysis tool that can comprehensively analyze the human and organizational deficiencies involved in GAAs. The accident causes revealed by SCM-GA were more consistent with the general aviation safety management practices.Practical applicationsGeneral aviation companies should establish their own GA-SMS and safety culture based on the subcategories developed herein. Using SCM-GA for routine safety inspection and accident investigation will help the management and the staff make effective safety decisions to effectively prevent GAAs.     

Risk factors for the severity of injury incurred in crashes involving on-duty police cars

Objective: This article explores the risk factors associated with police cars on routine patrol and/or on an emergency run and their effects on the severity of injuries in crashes.

Methods: The binary probit model is used to examine the effects of important factors on the risk of injuries sustained in crashes involving on-duty police cars.

Results: Several factors significantly increase the probability of crashes that cause severe injuries. Among those causes are police officers who drive at excessive speeds, traffic violations during emergency responses or pursuits, and driving during the evening (6 to 12 p.m.) or in rainy weather. Findings also indicate some potential issues associated with an increase in the probability of crashes that cause injuries. Younger police drivers were found to be more likely to be involved in crashes causing injuries than middle-aged drivers were. Distracted driving by on-duty police officers as well as civilian drivers who did not pull over to let a police car pass in emergency situations also caused serious crashes.

Conclusions: Police cars are exempted from certain traffic laws under emergency circumstances. However, to reduce the probability of being involved in a crash resulting in severe injuries, officers are still obligated to drive safely and follow safety procedures when responding to emergencies or pursuing a car. Enhancement of training techniques for emergency situations or driving in pursuit of an offender and following the safety procedures are essential for safety in driving during an emergency run by police.     

Bicycle helmet use among children in the United States: The effects of legislation, personal and household factors

Introduction

Children ages 5-14 years have the highest rate of bicycle-related injuries in the country. Bicycle helmets can prevent head and brain injuries, which represent the most serious type of bicycle-related injury.

Objectives

This paper compares children's bicycle helmet use to that estimated from an earlier study, and explores regional differences in helmet use by existing helmet legislation.

Methods

This study was a cross-sectional, list-assisted random-digit-dial telephone survey. Interviews were completed by 9,684 respondents during 2001-2003. The subset with at least one child in the household age 5-14 years (2,409 respondents) answered questions about bicycle helmet use for a randomly selected child in their household.

Results

Almost half (48%) of the children always wore their helmet, 23% sometimes wore their helmet, and 29% never wore their helmet. Helmet wearing was significantly associated with race, ethnicity, and child age but was not associated with the sex of the child. Other significant predictors of use included household income, household education, census region, and bicycle helmet law status. Statewide laws were more effective than laws covering smaller areas. The proportion of children who always wore a helmet increased from 25% in 1994 to 48% in 2001-2002. Significant increases in helmet use from 20% to 26% were seen among both sexes, younger (5-9 years) and older (10-14 years) children, and in all four regions of the country.

Conclusions

While there has been substantial progress in the number of children who always wear their helmets, more than half do not. Further progress will require using a combination of methods that have been shown to successfully promote consistent helmet use. Impact on industry: minimal.     

Speaking of human factors: Emergent meanings in interviews with professional accident investigators

This paper presents a qualitative analysis of the human factor concept, more specifically what it means and includes in everyday professional discourse. It is founded on 10 extensive interviews with professional investigators within the road, maritime and rail administration concerning their practical investigative work. General and specific results are generated of interview contributions using a pragmatic communicative approach and discourse analysis.Results show that human factors is an expression tied to individual professional experience, sparks dissatisfaction and demands specification due to recurring indexicality problems. It tends to be used for negative matters. The specific results, listing eight different meanings, indicate that there is no such thing as a professional usage of the human factor but a spectrum of meanings. The study concludes that the meanings of the human factor (a) always evolve in the dynamic process of producing and understanding language, (b) are context-dependent, and (c) emerge through talk, as one type of discourse. Contrary to ordinary conceptions, there is no simple matter as a human factor that may be used in a routine manner. A non-specific use of the notion may even obscure a course of events and prevent necessary investigation, for example, if the human factor simply replaces a factor such as ‘fatigue’. Although contemporary interdisciplinary research focuses peripheral factors, the idea of individual humans and their erroneous acts has survived – and lives – in the practical world of professional investigators. Empirically deduced meanings need to be continuously highlighted and problematised if theory is to approach everyday professional practice.     

The effect of age and gender on motor vehicle driver injury severity at highway-rail grade crossings in the United States

IntroductionBased on the Federal Railway Administration (FRA) database, there were 25,945 highway-rail crossing accidents in the United States between 2002 and 2011. With an extensive database of highway-rail grade crossing accidents in the United States from 2002 to 2011, estimation results showed that there were substantial differences across age/gender groups for driver's injury severity.MethodThe study applied an ordered probit model to explore the determinants of driver injury severity for motor vehicle drivers at highway-rail grade crossings.ResultsThe analysis found that there are important behavioral and physical differences between male and female drivers given a highway-rail grade crossing accident happened.Practical applicationsOlder drivers have higher fatality probabilities when driving in open space under passive control especially during bad weather condition. Younger male drivers are found to be more likely to have severe injuries at rush hour with high vehicle speed passing unpaved highway-rail grade crossings under passive control. Synthesizing these results led to the conclusion that the primary problem with young is risk-taking and lack of vehicle handling skills. The strength of older drivers lies in their aversion to risk, but physical degradation issues which result in longer reaction/perception times and degradation in vision and hearing often counterbalance this attribute.     

Occupational accidents with ladders in Spain: Risk factors

Introduction

Occupational accidents suffered by workers in Spain when using ladders were analyzed over a six year period from 2003-2008, during which the total of notified ladder-related accidents amounted to 21,725. Method: Different accident-related factors were identified for the purpose of developing a pattern of those factors that had the greatest influence on the seriousness and the fatality of such accidents. Thus, a series of variables were examined such as age and length of service of the injured worker, firm size, the work sector, the injury suffered, and the part of the body that was injured. Since falls is the most frequent and most serious of ladder related occupational accidents, a special analysis of falls was performed. Results: The findings showed that the seriousness of ladder-related accidents increased with the age of the injured worker. Likewise, accidents at places other than the usual workplace were more serious and registered higher fatalities than those that occurred at the usual place of work. Conclusions: The analysis of falls from ladders established that accidents in smaller-sized firms were of greater seriousness and involved more fatalities than those in larger-sized firms. The investigation also underlined the need for stricter compliance with preliminary safety assessments when working with ladders.     

Sobriety checkpoint and open container laws in the United States: Associations with reported drinking-driving

Objective: The objective of this study was to assess how 2 types of drinking-driving laws—permitting sobriety checkpoints and prohibiting open containers of alcohol in motor vehicles—are associated with drinking-driving and how enforcement efforts may affect these associations.

Methods: We obtained 2010 data on state-level drinking-driving laws and individual-level self-reported drinking-driving from archival sources (Alcohol Policy Information System, NHTSA, and Behavioral Risk Factor Surveillance System). We measured enforcement of the laws via a 2009 survey of state patrol agencies. We computed multilevel regression models (separate models for each type of law) that first examined how having the state law predicted drinking-driving, controlling for various state- and individual-level covariates; we then added the corresponding enforcement measure as another potential predictor.

Results: We found that states with a sobriety checkpoint law, compared with those without a law, had 18.2% lower drinking-driving; states that conducted sobriety checks at least monthly (vs. not conducting checks) had 40.6% lower drinking-driving (the state law variable was not significant when enforcement was added). We found no significant association between having an open container law and drinking-driving, but states that conducted open container enforcement, regardless of having a law, had 17.6% less drinking-driving.

Conclusion: Our results suggest that having a sobriety checkpoint law and conducting checkpoints as well as enforcement of open containers laws may be effective strategies for addressing drinking-driving.