Evaluation of the effectiveness of toe board energy-absorbing material for foot,ankle, and lower leg injury reduction

Objective: Since 2000, numerous improvements have been made to the National Association for Stock Car Auto Racing, Incorporated (NASCAR®) driver restraint system, resulting in improved crash protection for motorsports drivers. Advancements have included seats, head and neck restraints (HNRs), seat belt restraint systems, driver helmets, and others. These enhancements have increased protection for drivers from severe crash loading. Extending protection to the driver's extremities remains challenging. Though the drivers’ legs are well contained for lateral and vertical crashes, they remain largely unrestrained in frontal and frontal oblique crashes.

Method: Sled testing was conducted for the evaluation of an energy-absorbing (EA) toe board material to be used as a countermeasure for leg and foot injuries. Testing included baseline rigid toe boards, tests with EA material–covered toe boards, and pretest positioning of the 50th percentile male frontal Hybrid III anthropomorphic test device (ATD) lower extremities. ATD leg and foot instrumentation included foot acceleration and tibia forces and moments.

Results: The sled test data were evaluated using established injury criteria for tibial plateau fractures, leg shaft fractures, and calcaneus, talus, ankle, and midfoot fractures.

Conclusion: A polyurethane EA foam was found to be effective in limiting axial tibia force and foot accelerations when subjected to frontal impacts using the NASCAR motorsport restraint system.     

Exploratory multinomial logit model–based driver injury severity analyses for teenage and adult drivers in intersection-related crashes

Objective: Teenage drivers are more likely to be involved in severely incapacitating and fatal crashes compared to adult drivers. Moreover, because two thirds of urban vehicle miles traveled are on signal-controlled roadways, significant research efforts are needed to investigate intersection-related teenage driver injury severities and their contributing factors in terms of driver behavior, vehicle–infrastructure interactions, environmental characteristics, roadway geometric features, and traffic compositions. Therefore, this study aims to explore the characteristic differences between teenage and adult drivers in intersection-related crashes, identify the significant contributing attributes, and analyze their impacts on driver injury severities.

Methods: Using crash data collected in New Mexico from 2010 to 2011, 2 multinomial logit regression models were developed to analyze injury severities for teenage and adult drivers, respectively. Elasticity analyses and transferability tests were conducted to better understand the quantitative impacts of these factors and the teenage driver injury severity model's generality.

Results: The results showed that although many of the same contributing factors were found to be significant in the both teenage and adult driver models, certain different attributes must be distinguished to specifically develop effective safety solutions for the 2 driver groups.

Conclusions: The research findings are helpful to better understand teenage crash uniqueness and develop cost-effective solutions to reduce intersection-related teenage injury severities and facilitate driver injury mitigation research.     

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.     

Motorcycle crashes potentially preventable by three crash avoidance technologies on passenger vehicles

Objective: The objective of this study was to identify and quantify the motorcycle crash population that would be potential beneficiaries of 3 crash avoidance technologies recently available on passenger vehicles.

Methods: Two-vehicle crashes between a motorcycle and a passenger vehicle that occurred in the United States during 2011–2015 were classified by type, with consideration of the functionality of 3 classes of passenger vehicle crash avoidance technologies: frontal crash prevention, lane maintenance, and blind spot detection. Results were expressed as the percentage of crashes potentially preventable by each type of technology, based on all known types of 2-vehicle crashes and based on all crashes involving motorcycles.

Results: Frontal crash prevention had the largest potential to prevent 2-vehicle motorcycle crashes with passenger vehicles. The 3 technologies in sum had the potential to prevent 10% of fatal 2-vehicle crashes and 23% of police-reported crashes. However, because 2-vehicle crashes with a passenger vehicle represent fewer than half of all motorcycle crashes, these technologies represent a potential to avoid 4% of all fatal motorcycle crashes and 10% of all police-reported motorcycle crashes.

Discussion: Refining the ability of passenger vehicle crash avoidance systems to detect motorcycles represents an opportunity to improve motorcycle safety. Expanding the capabilities of these technologies represents an even greater opportunity. However, even fully realizing these opportunities can affect only a minority of motorcycle crashes and does not change the need for other motorcycle safety countermeasures such as helmets, universal helmet laws, and antilock braking systems.     

A comparison of contributing effects on 2-vehicle alcohol-related crashes between impaired and nonimpaired operators

Objective: Driving under the influence of alcohol is a crime that places the lives of all motorists in danger. Though it is a largely preventable act, impaired driving has accounted for 31 to 38% of fatal crashes across the country over the last decade. When an impaired operator crashes his or her vehicle, there is often a second unit, of which the operator is not impaired, involved in the crash.

Methods: This research looks at approximately 14,000 2-unit crashes involving an impaired operator in the State of Ohio from 2008 through 2012. The research is focused on determining the effects of crash and operator characteristics in 2-unit alcohol-related crashes through the use of 2 mixed logit models.

Results: It is found that several factors have similar effects on the injury severities of both the impaired and nonimpaired operators, including head-on crashes, the use of seat belts, and the deployment of airbags. There are, however, several factors that affect the 2 operators differently. It is found that the impaired operator's injury severity is based on the type and, more important, the size of the vehicle he or she is driving, the roadway geometry, and the speed of the vehicle driven by the nonimpaired operator. The nonimpaired operator is equally affected by the speed of the impaired vehicle as much as his or her own speed, and the nonimpaired operator's injury severity is virtually independent of the type of vehicle being driven.

Conclusions: Researchers may disseminate the results to community groups such as Mothers Against Drunk Driving and Safe Communities to increase awareness of the dangers of drunk driving in an effort to reduce the number of alcohol-related crashes.     

Road traffic crashes among farm vehicle drivers in southern China: A cross-sectional survey

Objective: The objective of this study was to identify the prevalence and potential risk factors of farm vehicle–related road traffic crashes among farm vehicle drivers in southern China.

Methods: A cross-sectional study was used to interview 1,422 farm vehicle drivers in southern China. Farm vehicle–related road traffic crashes that occurred from December 2013 to November 2014 were investigated. Data on farm vehicle–related road traffic crashes and related factors were collected by face-to-face interviews.

Results: The prevalence of farm vehicle–related road traffic crashes among the investigated drivers was 7.2%. Farm vehicle–related road traffic crashes were significantly associated with self-reported vision problem (adjusted odds ratio [AOR] = 6.48, 95% confidence interval [CI], 3.86–10.87), self-reported sleep disorders (AOR = 10.03, 95% CI, 6.28–15.99), self-reported stress (AOR = 20.47, 95% CI, 9.96–42.08), reported history of crashes (AOR = 5.40, 95% CI, 3.47–8.42), reported history of drunk driving (AOR = 5.07, 95% CI, 2.97–8.65), and reported history of fatigued driving (AOR = 5.72, 95% CI, 3.73–8.78). The number of road traffic crashes was highest in the daytime and during harvest season. In over 96% of farm vehicle–related road traffic crashes, drivers were believed to be responsible for the crash. Major crash-causing factors included improper driving, careless driving, violating of traffic signals or signs, and being in the wrong lane.

Conclusion: Findings of this study suggest that farm vehicle–related road traffic crashes have become a burgeoning public health problem in China. Programs need to be developed to prevent farm vehicle–related road traffic crashes in this emerging country.     

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

Objective: The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

Methods: Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

Results: Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

Conclusions: This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50–60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol.

When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day.

The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.     

Prediction of vehicle crashes by drivers' characteristics and past traffic violations in Korea using a zero-inflated negative binomial model

Aims: Traffic safety is a significant public health challenge, and vehicle crashes account for the majority of injuries. This study aims to identify whether drivers' characteristics and past traffic violations may predict vehicle crashes in Korea.

Methods: A total of 500,000 drivers were randomly selected from the 11.6 million driver records of the Ministry of Land, Transport and Maritime Affairs in Korea. Records of traffic crashes were obtained from the archives of the Korea Insurance Development Institute. After matching the past violation history for the period 2004–2005 with the number of crashes in year 2006, a total of 488,139 observations were used for the analysis. Zero-inflated negative binomial model was used to determine the incident risk ratio (IRR) of vehicle crashes by past violations of individual drivers. The included covariates were driver's age, gender, district of residence, vehicle choice, and driving experience.

Results: Drivers violating (1) a hit-and-run or drunk driving regulation at least once and (2) a signal, central line, or speed regulation more than once had a higher risk of a vehicle crash with respective IRRs of 1.06 and 1.15. Furthermore, female gender, a younger age, fewer years of driving experience, and middle-sized vehicles were all significantly associated with a higher likelihood of vehicle crashes.

Conclusions: Drivers' demographic characteristics and past traffic violations could predict vehicle crashes in Korea. Greater resources should be assigned to the provision of traffic safety education programs for the high-risk driver groups.     

The effectiveness of lane departure warning systems—A reduction in real-world passenger car injury crashes

Objective: The objective of this study was to estimate the safety benefits of in vehicle lane departure warning (LDW) and lane keeping aid (LKA) systems in reducing relevant real-world passenger car injury crashes.

Methods: The study used an induced exposure method, where LDW/LKA-sensitive and nonsensitive crashes were compared for Volvo passenger cars equipped with and without LDW/LKA systems. These crashes were matched by car make, model, model year, and technical equipment; that is, low-speed autonomous emergency braking (AEB) called City Safety (CS). The data were extracted from the Swedish Traffic Accident Data Acquisition database (STRADA) and consisted of 1,853 driver injury crashes that involved 146 LDW-equipped cars, 11 LKA-equipped cars, and 1,696 cars without LDW/LKA systems.

Results: The analysis showed a positive effect of the LDW/LKA systems in reducing lane departure crashes. The LDW/LKA systems were estimated to reduce head-on and single-vehicle injury crashes on Swedish roads with speed limits between 70 and 120 km/h and with dry or wet road surfaces (i.e., not covered by ice or snow) by 53% with a lower limit of 11% (95% confidence interval [CI]). This reduction corresponded to a reduction of 30% with a lower limit of 6% (95% CI) for all head-on and single-vehicle driver injury crashes (including all speed limits and all road surface conditions).

Conclusions: LDW/LKA systems were estimated to lower the driver injury risk in crash types that the systems are designed to prevent; that is, head-on and single-vehicle crashes. Though these are important findings, they were based on a small data set. Therefore, further research is desirable to evaluate the effectiveness of LDW/LKA systems under real-world conditions and to differentiate the effectiveness between technical solutions (i.e., LDW and LKA) proposed by different manufacturers.     

Injury risk functions for frontal oblique collisions

Objective: The objective of this article was the construction of injury risk functions (IRFs) for front row occupants in oblique frontal crashes and a comparison to IRF of nonoblique frontal crashes from the same data set.

Method: Crashes of modern vehicles from GIDAS (German In-Depth Accident Study) were used as the basis for the construction of a logistic injury risk model. Static deformation, measured via displaced voxels on the postcrash vehicles, was used to calculate the energy dissipated in the crash. This measure of accident severity was termed objective equivalent speed (oEES) because it does not depend on the accident reconstruction and thus eliminates reconstruction biases like impact direction and vehicle model year. Imputation from property damage cases was used to describe underrepresented low-severity crashes―a known shortcoming of GIDAS. Binary logistic regression was used to relate the stimuli (oEES) to the binary outcome variable (injured or not injured).

Results: IRFs for the oblique frontal impact and nonoblique frontal impact were computed for the Maximum Abbreviated Injury Scale (MAIS) 2+ and 3+ levels for adults (18–64 years). For a given stimulus, the probability of injury for a belted driver was higher in oblique crashes than in nonoblique frontal crashes. For the 25% injury risk at MAIS 2+ level, the corresponding stimulus for oblique crashes was 40 km/h but it was 64 km/h for nonoblique frontal crashes.

Conclusions: The risk of obtaining MAIS 2+ injuries is significantly higher in oblique crashes than in nonoblique crashes. In the real world, most MAIS 2+ injuries occur in an oEES range from 30 to 60 km/h.     

Undercounting of large trucks in federal and state crash databases: Extent of problem and how to improve accuracy of truck classifications

Objective: Prior research suggested that single-unit trucks are undercounted when using vehicle body codes in the Fatality Analysis Reporting System (FARS). This study explored the extent of the misclassification and undercounting problem for crashes in FARS and state crash databases.

Methods: Truck misclassifications for fatal crashes were explored by comparing the Trucks Involved in Fatal Accidents (TIFA) database with FARS. TIFA used vehicle identification numbers (VINs) and survey information to classify large trucks. This study used VINs to improve the accuracy of large truck classifications in state crash databases from 5 states (Delaware, Maryland, Minnesota, Nebraska, and Utah).

Results: The vehicle body type codes resulted in a 19% undercount of single-unit trucks in FARS and a 23% undercount of single-unit trucks in state databases. Tractor-trailers were misclassified less often. Misclassifications occurred most frequently among single-unit trucks in the weight classes of 10,001–14,000 pounds.

Conclusions: The amount of misclassification of large trucks is large enough to potentially affect federal and state decisions on traffic safety. Using information from VINs results in more complete and accurate counts of large trucks involved in crashes. The National Transportation Safety Board recommended actions to improve federal and state crash data.     

Prevalence of alcohol impairment and odds of a driver injury or fatality in on-road farm equipment crashes

Objective: The objective of this article was to estimate the prevalence of alcohol impairment in crashes involving farm equipment on public roadways and the effect of alcohol impairment on the odds of crash injury or fatality.

Methods: On-road farm equipment crashes were collected from 4 Great Plains state departments of transportation during 2005–2010. Alcohol impairment was defined as an involved driver having blood alcohol content of ≥0.08 g/100 ml or a finding of alcohol impairment as a driver contributing circumstance recorded on the police crash report. Injury or fatality was categorized as (a) no injury (no and possible injury combined), (b) injury (nonincapacitating or incapacitating injury), and (c) fatality. Hierarchical multivariable logistic regression modeling, clustered on crash, was used to estimate the odds of an injury/fatality in crashes involving an alcohol-impaired driver.

Results: During the 5 years under study, 3.1% (61 of 1971) of on-road farm equipment crashes involved an alcohol-impaired driver. One in 20 (5.6%) injury crashes and 1 in 6 (17.8%) fatality crashes involved an alcohol-impaired driver. The non-farm equipment driver was significantly more likely to be alcohol impaired than the farm equipment driver (2.4% versus 1.1% respectively, P = .0012). After controlling for covariates, crashes involving an alcohol-impaired driver had 4.10 (95% confidence interval [CI], 2.30–7.28) times the odds of an injury or fatality. In addition, the non-farm vehicle driver was at 2.28 (95% CI, 1.92–2.71) times higher odds of an injury or fatality than the farm vehicle driver. No differences in rurality of the crash site were found in the multivariable model.

Conclusion: On-road farm equipment crashes involving alcohol result in greater odds of an injury or fatality. The risk of injury or fatality is higher among the non-farm equipment vehicle drivers who are also more likely to be alcohol impaired. Further studies are needed to measure the impact of alcohol impairment in on-road farm equipment crashes.     

Fatal and serious collisions involving pedal cyclists and trucks in London between 2007 and 2011

Objective: Increased numbers of people riding pedal cycles have led to a greater focus on pedal cycle safety. The aim of this article is to explore factors that are associated with fatal and a small number of serious-injury pedal cyclist crashes involving trucks that occurred in London between 2007 and 2011.

Methods: Data were collected from police collision files for 53 crashes, 27 of which involved a truck (≥3.5 tonnes) and a pedal cycle. A systematic case review approach was used to identify the infrastructure, vehicle road user, and management factors that contributed to these crashes and injuries and how these factors interacted.

Results: Trucks turning left conflicting with pedal cyclists traveling straight ahead was a common crash scenario. Key contributory factors identified included the pedal cyclists not being visible to the truck drivers, road narrowing, and inappropriate positioning of pedal cyclists.

Conclusions: Crashes involving trucks and pedal cyclists are complex events that are caused by multiple interacting factors; therefore, multiple measures are required to prevent them from occurring.     

Orthopedic injury in electric bicycle-related collisions

Objective: Although electric bicycle-related injuries have become the most common reason for hospitalization due to a road crash in China, no study has comprehensively investigated electric bicycle collisions and their impact on orthopedic injuries; such a study may provide evidence to support a new road safety policy.

Methods: A retrospective review of orthopedic injuries from electric bicycle collisions was performed in an urban trauma center. We collected variables including age, gender, location of fracture, presence of open or closed fractures, concomitant vascular, and neurologic injuries.

Results: A total of 2,044 cases were involved in electric bicycle collisions. The orthopedic injury victims were predominantly male and middle aged. The most common orthopedic injury was a femur fracture. Open fractures frequently involved the forearm and tibia/fibula. Male patients were more likely to suffer from multiple fractures and associated injuries than female patients. Fewer patients age 60 years old or older wore helmets at the time of the accident compared to those in other age groups.

Conclusions: Orthopedic injuries from electric bicycle-related accidents cause patients substantial suffering that could lead to serious social consequences. Helmet use and protective clothing or similar safety gear, especially for electric bicycle users, should be required to provide greater protection.     

Spatial patterns of off-the-system traffic crashes in Miami–Dade County,Florida, during 2005–2010

Objective: The objective of this study is to analyze the spatial distribution of the vehicles involved in crashes in Miami–Dade County. In addition, we analyzed the role of time of day, day of the week, seasonality, drivers’ age in the distribution of traffic crashes.

Method: Off-the-system crash data acquired from the Florida Department of Transportation during 2005–2010 were divided into subcategories according to the risk factors age, time of day, day of the week, and travel season. Various spatial statistics methods, including nearest neighbor analysis, Getis-Ord hot spot analysis, and kernel density analysis revealed substantial spatial variations, depending on the subcategory in question.

Results: Downtown Miami and South Beach showed up consistently as hotspots of traffic crashes in all subcategories except fatal crashes. However, fatal crashes were concentrated in residential areas in inland areas.

Conclusion: This understanding of patterns can help the county target high-risk areas and help to reduce crash fatalities to create a safer environment for motorists and pedestrians.     

Safe speed limits for a safe system: The relationship between speed limit and fatal crash rate for different crash types

Objective: The objective of this article is to provide empirical evidence for safe speed limits that will meet the objectives of the Safe System by examining the relationship between speed limit and injury severity for different crash types, using police-reported crash data.

Method: Police-reported crashes from 2 Australian jurisdictions were used to calculate a fatal crash rate by speed limit and crash type. Example safe speed limits were defined using threshold risk levels.

Results: A positive exponential relationship between speed limit and fatality rate was found. For an example fatality rate threshold of 1 in 100 crashes it was found that safe speed limits are 40 km/h for pedestrian crashes; 50 km/h for head-on crashes; 60 km/h for hit fixed object crashes; 80 km/h for right angle, right turn, and left road/rollover crashes; and 110 km/h or more for rear-end crashes.

Conclusions: The positive exponential relationship between speed limit and fatal crash rate is consistent with prior research into speed and crash risk. The results indicate that speed zones of 100 km/h or more only meet the objectives of the Safe System, with regard to fatal crashes, where all crash types except rear-end crashes are exceedingly rare, such as on a high standard restricted access highway with a safe roadside design.     

Etiology of fatal thoracic aortic injuries: Secondary data analysis

Objectives: Motor vehicle crashes remain a leading cause of death in the United States (US). Thoracic aortic dissection due to blunt trauma remains a major injury mechanism, and up to 90% of these injuries result in death on the scene. The objective of this study is to understand the modern risk factors and etiology of fatal thoracic aortic injuries in the current US fleet.

Methods: Using a unique, linked, Fatality Analysis Reporting System (FARS) and Multiple Cause of Death (MCOD) database from 2000–2010, 144,169 drivers over 16 years of age who suffered fatal injuries were identified. The merged database provides an unparalleled fidelity for identifying thoracic aortic injuries due to motor vehicle accidents. Thoracic aortic injuries were defined by ICD-10 codes S250. Univariate and multivariate logistic regression models for presence of any thoracic aortic injuries were fitted. Age, gender, BMI weight categories, vehicle class, model year, crash type/direction, severity of crash damage, airbag deployment location, and seatbelt use, fatal injury codes, and location of injury were considered. Odds ratios (OR) and corresponding 95% confidence intervals (95%CI) are calculated.

Results: There were 2953 deaths (2.10%) related to thoracic aortic injuries that met the inclusion criteria. Nearside crashes were associated with an increased odds (OR = 1.42, 1.1-1.83), while rollover crashes (OR =.44,.29-.66) were associated with a reduced odds of fatal thoracic aortic injury. Using backward selection on the full multivariate model, the only significant model effects that remained were vehicle type, crash type, body region, and injury type.

Conclusions: The increased prevalence of fatal thoracic aortic injury in nearside crashes, increasing age, and vehicle type provide some insight into the current US fleet. Important factors, including model year, had significantly lower levels of the injury in univariate analysis, demonstrating the effect of safety improvements in newer model vehicles. Further study of this fatal injury is warranted, including comparisons of those who survive the injury.     

Investigating factors influencing pedestrian injury severity at intersections

Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.

Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.

Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.

Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed.     

Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes

Objectives: Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle–pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle–pedestrian crashes to identify the hotspots and hot times.

Methods: Data for all vehicle–pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle–pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle–pedestrian crashes for different age groups and genders.

Results: Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle–pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle–pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle–pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars.

Conclusions: This research reveals that temporal and spatial distributions of vehicle–pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle–pedestrian crashes.