Data mining approach to model bus crash severity in Australia

Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians’ distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.     

Crashes of novice teenage drivers: characteristics and contributing factors

OBJECTIVE: The initial months of licensure are especially hazardous for teenagers. Factors leading to crashes of novice 16-year-old drivers were identified. METHOD: Sixteen year-olds in Connecticut who were involved in nonfatal crashes during the first 8 months of licensure were interviewed, and police crash reports were examined. Crash types and contributing factors were identified. RESULTS: Three-fourths of the crash-involved teenagers were at fault. Their crashes resulted primarily when they ran off the road, rear ended another vehicle, or collided with another vehicle that had the right-of-way. Three factors contributed about equally to their crashes: failing to detect another vehicle or traffic control, speeding, and losing control of the vehicle or sliding. Slippery roads also were an important factor. Most failures to detect another vehicle or traffic control involved not looking thoroughly, distraction, or inattention. DISCUSSION: Based on the findings, potential countermeasures for reducing crashes of novice teenage drivers include adequate practice driving, in-vehicle monitoring devices, and electronic stability control. IMPACT ON INDUSTRY: More than half of the nonfatal, at-fault crashes of newly licensed 16-year-old drivers involved more than one contributing factor including speed, loss of control, and slippery roads. Efforts to reduce teenage crashes should focus on these factors.     

Temporal patterns of crashes of 16-to 17-year-old drivers in Fairfax County, Virginia

OBJECTIVE: To develop a better understanding of the frequency and characteristics of teenage driver crashes occurring during school commute times. METHOD: Data were obtained from police reports of crashes involving drivers ages 16-17 that occurred between September 2001 and August 2004 in Fairfax County, Virginia. Temporal patterns and other characteristics of crash involvement during the school year were examined, and crashes during school commute times were compared with those at other times. RESULTS: Teenage driver crash involvement spiked during weekday school commute times. Compared with other times, crashes during school commute times were significantly more likely to involve multiple vehicles but less likely to result in injuries or involve drivers who were male, made driving errors, or had been drinking alcohol. Crashes during school commute times were more likely to involve more than one teenage driver and occur close to schools. CONCLUSIONS: Crashes involving teenage drivers are prevalent during school commute times. Many of these crashes involve multiple teenage drivers and occur near schools. Schools and communities should consider programs and policies that reduce teenage driving to school and enhance the safety of teenagers that do drive.     

The impact of environmental, vehicle and driver characteristics on injury severity in older drivers hospitalized as a result of a traffic crash

INTRODUCTION: Compared to younger age groups, older people are more likely to be seriously injured or to die as a result of a traffic crash. METHOD: The aim of the study is to examine the impact of environmental, vehicle, crash, and driver characteristics on injury severity in older drivers involved in traffic crashes by using recently linked police crash records and hospitalization data from New South Wales, Australia. The severity of injury resulting from traffic crashes was measured using the International Classification of Diseases, 10th revision (ICD-10) Injury Severity Score (ICISS). RESULTS: Multivariate analysis identified rurality, presence of complex intersections, road speed limit, driver error, speeding, and seat belt use as independent predictors of injury severity in older people. The type of intersection configuration explained over half of the observed variations in injury severity. IMPACT ON INDUSTRY: Environmental modification such as intersection treatments might contribute to a decrease in the severity of injury in older people involved in road crashes.     

Responsibility of drivers, by age and gender, for motor-vehicle crash deaths

Problem: Motor-vehicle crash rate comparisons by age and gender usually are based on the extent to which drivers in a particular age/gender category are themselves injured or involved in crashes (e.g., the number of 20-year-old females in crashes). Basing comparisons instead on the extent to which drivers in various age/gender groups are responsible for deaths (including themselves) in their crashes is more revealing of their overall contribution to the problem. Methods: Data from the Fatality Analysis Reporting System (FARS, 1996–2000) were used in the analysis, which was based on crashes that involved one or two vehicles only. Drivers in fatal single-vehicle crashes were assumed to have responsibility for the crash. In fatal two-vehicle crashes, driver operator errors reported by police were used to assign crash responsibility. Results: When all crashes were considered, both the youngest and oldest drivers were most likely to be responsible for deaths in their crashes. In two-vehicle crashes, the oldest drivers were more likely than young drivers to be responsible. Young males were more likely than young females to be responsible for crash deaths, whereas females in their 50s and older were more likely than same-age males to be responsible. In terms of responsibility for deaths per licensed driver, young drivers, especially males, had the highest rates because of their high involvement rates and high responsibility rates. The majority of deaths for which young drivers were responsible occurred to people other than themselves, especially passengers in their vehicles, whereas the bulk of the deaths for which older drivers were responsible were their own. Discussion: The results highlight the contribution of young drivers to the motor-vehicle crash problem, the need for measures such as passenger restrictions in graduated licensing systems, and the need for vehicle modifications to better protect older occupants.     

Antilock Brake Systems and Risk of Different Types of Crashes in Traffic

While antilock braking systems (ABS) have been convincingly demonstrated to enhance test track braking performance, their effect on crash risk in actual driving remains less clear. This paper examines how ABS influences crash risk using mainly two published studies which used police-reported crashes. The published findings are augmented by including new data and additional results. All the work is based on seven General Motors (GM) passenger vehicles having ABS as standard equipment for 1992 models but not available for 1991 models. The ratio of crashes under an adverse condition (say, when the pavement is wet) to under a normal condition (say, when the pavement is dry) is compared for ABS and non-ABS vehicles. After correcting for such factors as model year effects not linked to ABS, the following associations between ABS and crash risk were found by averaging data from the five states Texas, Missouri, North Carolina, Pennsylvania and Indiana (the errors are one standard error): a (10 ± 3)% relative lower crash risk on wet roads compared to the corresponding comparison on dry roads; a (22 ± 11)% lower risk of a pedestrian crash compared to the risk of a non-pedestrian crash; a (39 ± 16)% increase in rollover crash risk compared to the risk of a non-rollover crash. Data from the same Ave states were used to examine two-vehicle rear-end collisions. Using the assumption that side-impact crashes estimate exposure, it was found that for wet roads ABS reduces the risk of crashing into a lead vehicle by (32 ± 8)%, but increases the risk of being struck in the rear by (30 ± 14)%. The results from this study and from all available reported studies are summarized in tabular form.     

Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study

Problem: Previous research have focused extensively on crashes, however near crashes provide additional data on driver errors leading to critical events as well as evasive maneuvers employed to avoid crashes. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive data on real world driving and offers a reliable methodology to study near crashes. The current study utilized the SHRP2 database to compare the rate and characteristics associated with near crashes among risky drivers. Methods: A subset from the SHRP2 database consisting of 4,818 near crashes for teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers was used. Near crashes were classified into seven incident types: rear-end, road departure, intersection, head-on, side-swipe, pedestrian/cyclist, and animal. Near crash rates, incident type, secondary tasks, and evasive maneuvers were compared across age groups. For rear-end near crashes, near crash severity, max deceleration, and time-to-collision at braking were compared across age. Results: Near crash rates significantly decreased with increasing age (p < 0.05). Young drivers exhibited greater rear-end (p < 0.05) and road departure (p < 0.05) near crashes compared to adult and older drivers. Intersection near crashes were the most common incident type among older drivers. Evasive maneuver type did not significantly vary across age groups. Near crashes exhibited a longer time-to-collision at braking (p < 0.01) compared to crashes. Summary: These data demonstrate increased total near crash rates among young drivers relative to adult and older drivers. Prevalence of specific near crash types also differed across age groups. Timely execution of evasive maneuvers was a distinguishing factor between crashes or near crashes. Practical Applications: These data can be used to develop more targeted driver training programs and help OEMs optimize ADAS to address the most common errors exhibited by risky drivers.     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

Motor Vehicle Crashes on Bridges and Countermeasure Opportunities

There are approximately 600,000 highway bridges in the United States. These structures differ from surface streets and highways in terms of their physical properties and operational characteristics, which in turn, affect the risk of motor-vehicle crashes and the kinds of crashes that occur. The purpose of this study was to identify motor-vehicle crash patterns and contributing factors to bridge crashes and related countermeasure opportunities on four urban bridges in the New York metropolitan area. A crash typology was developed using the narratives and diagrams in police crash reports in addition to standard police classifications of crashes. The final dataset contained records of 1,381 police-reported crashes. The four bridges studied had higher crash rates than their respective approach roads. Four major crash types accounted for approximately 90% of the bridge crashes. Primary collision factors are reviewed, and potential countermeasures are discussed.     

Miss-and-run: Factors contributing to two-vehicle phantom vehicle crashes in Florida

Objective: Phantom vehicle crashes (PVCs), or miss-and-run crashes, are a topical issue in car insurance coverage because of controversies over testimony and compensation. However, no peer-reviewed literature has examined the perceptions and deliberations involved in this infrequent type of car crash. A novel taxonomy of roadway traffic crashes is proposed in this study on the basis of whether physical collisions did occur (hit or miss) and whether the perpetrators stayed at the crash scene (stay or run). In this way, this study poses the issue of PVCs within the scope of traffic safety research and aims to investigate the statistically significant factors that are likely to induce PVCs.

Methods: A binary logistic regression method was adopted to model the probability and occurrence of 2-vehicle PVCs (TV-PVCs) in Florida. Data derived from the Crash Analysis Reporting system in 2012–2014 consisted of 45,319 2-vehicle crashes, of which 1,376 (3.04%) were confirmed as positive TV-PVCs. Sixteen factors with 50 variables on crash information, roadway characteristics, and environmental conditions were included in the original consideration of the TV-PVC model.

Results: The results indicated that a 2-vehicle crash is more likely to be a PVC when the crash happens on weekends, on roadways with no traffic control or speed control, full access control; on curving and sloping roadways; on roadways of National Highway System; and in low-density and other areas. A TV-PVC is less likely to occur on dry roads, in daylight, or at intersections or driveways. Moreover, alcohol involvement in a 2-vehicle crash is associated with hit-and-stay crashes than PVCs, and uninsured motorists are more likely to be the victims of PVCs because they tend to avoid physical collisions due to the potential self-paid loss.

Conclusions: Several conclusions for better understanding the occurrence of PVCs are proposed for traffic management departments and car insurers. Cautious driving behavior including concentrated attention and deliberate lane changes should be encouraged for motorists to engage in appropriate levels of driving freedom, and drunk driving should be strictly supervised to keep motorists behind the wheel conscious. Car insurance is encouraged to compensate for economic loss resulting from roadway crashes. Road monitoring systems with well-performing illumination devices are recommended to help drivers provide potent testimony for compensation claims.     

Effect of vehicle and crash factors on older occupants

PROBLEM: The expected substantial increase in people aged 65 or older is important for those concerned about transportation injuries. However, much of the previous research concentrates on older drivers and overlooks the fact that vehicle and crash factors may provide significant explanations of older occupant injury rates. METHOD: Differences across age groups are explored using two nationwide travel surveys, crash involvement, fatalities, and injuries from crash databases and an ordered probit model of injury severity. RESULTS AND DISCUSSION: Two noticeable differences that help explain injury risk are that older people are more likely to travel in passenger cars than younger people who frequently use light trucks, and that seriously injured older occupants are more likely to be involved in side-impact crashes than their younger counterparts. IMPACT: Increased attention to vehicle engagement in side-impact crashes and to vehicle technologies that can help drivers avoid side collisions would be particularly helpful for older occupants.     

Using medico-legal data to investigate fatal older road user crash circumstances and risk factors

Objective: This study used medico-legal data to investigate fatal older road user (ORU, aged 65 years and older) crash circumstances and risk factors relating to 4 key components of the Safe System approach (e.g., roads and roadsides, vehicles, road users, and speeds) to identify areas of priority for targeted prevention activity.

Method: The Coroners' Court of Victoria's (CCOV) Surveillance Database was searched to identify and describe the frequency and rate per 100,000 population of fatal ORU crashes in the Australian state of Victoria for 2013–2014. Information relating to the deceased ORU, crash characteristics and circumstances, and risk factors was extracted and analyzed.

Results: One hundred and thirty-eight unintentional fatal ORU crashes were identified in the CCOV Surveillance Database. Of these fatal ORU crashes, most involved older drivers (44%), followed by older pedestrians (32%), older passengers (17%), older pedal cyclists (4%), older motorcyclists (1%), and older mobility scooter users (1%). The average annual rate of fatal ORU crashes per 100,000 population was 8.1 (95% confidence interval [CI], 6.0–10.2). In terms of the crash characteristics and circumstances, most fatal ORU crashes involved a counterpart (98%), of which the majority were passenger cars (50%) or fixed/stationary objects (25%), including trees (46%) or embankments (23%). In addition, most fatal ORU crashes occurred close to home (73%), on-road (87%), on roads that were paved (94%), on roads with light traffic volume (37%), and during low-risk conditions: between 12 p.m. and 6 p.m. (44%), on weekdays (80%), during daylight (75%), and under dry/clear conditions (81%). Road user (RU) error was identified by the police and/or the coroner for the majority of fatal crashes (55%), with a significant proportion of deceased ORUs deemed to have failed to yield (54%) or misjudged (41%).

Conclusions: RU error was the most significant factor identified in fatal ORU crashes, which suggests that there is a limited capacity of the road system to fully accommodate RU errors. Initiatives related to safer roads and roadsides, vehicles, speed zones, as well as behavioral approaches are key areas of priority for targeted activity to prevent fatal ORU crashes in the future.     

Considering built environment and spatial correlation in modeling pedestrian injury severity

Objective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.

Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.

Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.

Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.     

Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects

Objective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR.

Methods: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model.

Results: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime.

Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.     

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.     

Effects of speed humps on vehicle speed and pedestrian crashes in South Korea

Introduction: Speeding is a crucial risk factor for pedestrian safety because it shortens reaction time while increasing the impact force in collisions. Various types of traffic calming measures to prevent speeding have been devised. A speed hump—a raised bump installed in the pavement—has been widely used for this purpose. Method: To evaluate the effectiveness of speed humps, the speed profiles of vehicles passing speed humps were analyzed along with pedestrian crash records near speed humps. Results: The speed profiles showed that vehicles gradually diminished their speeds starting 30 m ahead of speed humps and, immediately after passing the humps, accelerated to regain their original speeds within a distance of 30 m. This speed reduction effect is substantial on both local and major roads: 18.4% and 24.0% reduction in speeds, respectively. The analysis of pedestrian crash records revealed that, inside the zones of speed reduction effect near speed humps (i.e., ±30 m from speed humps), fewer pedestrian crashes per roadway distance occurred and pedestrian injuries were less severe, compared with events outside the effect zones. This safety improvement was greater on major roads than local roads. Practical Applications: This work finds that the speed reductions that occurred near speed humps were gradual and influential ±30 m from their locations, suggesting that the hump installations should be close enough to the pedestrian crossings. It is noteworthy that, albeit that speed humps are more prevalent on local roads, the benefits of speed reduction effects from speed humps were more pronounced on major roads than on local roads. Therefore, speed humps on major roads can be considered a more effective measure for pedestrian safety.     

The effects of mobile speed camera introduction on road traffic crashes and casualties in a rural county of England

PROBLEM: This study assesses the impact of crash and casualty numbers in correspondence to the introduction of mobile speed cameras in the rural county of Norfolk, England. METHOD: Road traffic accident casualty and crash data were collected for two years before the introduction of cameras and two years subsequently. The casualties and crashes occurring at 29 camera sites were identified and separated from those occurring in the rest of the county. Trends in crashes and casualties, and their severity, were examined graphically and comparisons were made between before and after periods. The regression to the mean effect at individual sites was estimated. RESULTS: After the introduction of cameras, overall crashes declined by 1% and crashes involving fatalities or serious injuries declined by 9% on the roads without cameras. At the camera sites, crashes decreased by 19% and fatal and serious crashes by 44%. The reduction in total crashes was significantly greater than that expected from the effect of regression to the mean in 12 out of 20 sites tested. SUMMARY: The introduction of cameras appears to have resulted in real and measurable reductions in crash risk in this rural county. IMPACT ON INDUSTRY: Our results suggest the deployment of mobile speed cameras is an effective tool for organizations wishing to reduce road traffic casualties in areas where high crash rates have been associated with excessive vehicle speeds.     

Views of US drivers about driving safety

Problem: To assess how drivers view dangers on the highway, what motivates them to drive safely, how they say they reduce their crash and injury risk, and how they rate their own driving skills. Results: Most drivers rated their skills as better than average. The biggest motivating factor for safe driving was concern for safety of others in their vehicle, followed by negative outcomes such as being in a crash, increased insurance costs, and fines. The greatest threats to their safety were thought to be other drivers' actions that increase crash risk such as alcohol impairment or running red lights. In terms of reducing crashes and injuries, drivers tended to focus on actions they could take such as driving defensively or using seat belts. There was less recognition of the role of vehicles and vehicle features in crash or injury prevention. Impact on research, practice, and policy: Knowing how drivers view themselves and others, their concerns, and their motivations and techniques for staying out of trouble on the roads provides insight into the difficulty of changing driving practices.     

Injury risks for on-road farm equipment and horse and buggy crashes in Pennsylvania: 2010–2013

Objective: The purpose of this study was to investigate characteristics associated with farm equipment and horse and buggy roadway crashes in relation to person, incident, and injury characteristics to identify appropriate points for injury incident prevention.

Methods: Information on crashes occurring on public roads during the years 2010–2013 was obtained from the Pennsylvania Department of Transportation (PennDOT) and analyzed.

Results: There were 344 farm equipment and 246 horse and buggy crashes during the 4-year study period. These crashes involved 666 and 504 vehicles and 780 and 838 people, respectively. In incidents with farm equipment, the non-farm equipment drivers had an almost 2 times greater injury risk than farm equipment operators. Horse and buggy crashes were almost 3 times more injurious to the horse and buggy drivers than the drivers of the other vehicles.

Conclusions: The average crash rate for farm equipment was 198.4 crashes per 100,000 farm population and for horse and buggy the crash rate was calculated as 89.4 crashes per 100,000 Amish population per year. This study suggests that road safety and public health programs should focus not only on farm equipment operators and horse and buggy drivers but on other motorists sharing the roadway with them.