A clustering approach to integrate traffic safety in road maintenance prioritization

Objectives: We combine data on roads and crash characteristics to identify patterns in road traffic crashes with regard to road characteristics. We illustrate how combined analysis of data regarding road maintenance, maintenance costs, road characteristics, crash characteristics, and geographical location can enrich road maintenance prioritization from a traffic safety perspective.

Methods: The study is based on traffic crash data merged with road maintenance data and annual average daily traffic (AADT) collected in Denmark. We analyzed 3,964 crashes that occurred from 2010 to 2015. A latent class clustering (LCC) technique was used to identify crash clusters with different road and crash characteristics. The distribution of crash severity and estimated road maintenance costs for each cluster was found and cluster differences were compared using the chi-square test. Finally, a map matching procedure was used to identify the geographical distribution of the crashes in each cluster.

Results: Results showed that based on road maintenance levels there was no difference in the distribution of crash severity. The LCC technique revealed 11 crash clusters. Five clusters were characterized by crashes on roads with a poor maintenance level (levels 4 and 3). Only a few of these crashes included a vulnerable road user (VRU) but many occurred on roads without barriers. Four clusters included a large share of crashes on acceptably maintained roads (level 2). For these clusters only small variations in road characteristics were found, whereas the differences in crash characteristics were more dominant. The last 2 clusters included crashes that mainly occurred on new roads with no need for maintenance (level 1). Injury severity, estimated maintenance costs, and geographical location were found to be differently distributed for most of the clusters.

Conclusions: We find that focusing solely on road maintenance and crash severity does not provide clear guidance of how to prioritize between road maintenance efforts from a traffic safety perspective. However, when combined with geographical location and crash characteristics, a more nuanced picture appears that allows consideration of different target groups and perspectives.     

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.     

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.     

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.     

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.     

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.     

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.     

A study of fatal pedestrian crashes at rural low-volume road intersections in southwest China

Objective: Although intersections correspond to a small proportion of the entire roadway system, they account for a disproportionally high number of fatal pedestrian crashes, especially on rural roads situated in low- and middle-income countries. This article examines pedestrian safety at rural intersections and suggests applicable accident prevention treatments by providing an in-depth analysis of 28 fatal pedestrian crashes from 8 low-volume roads in southwest China.

Methods: The driving reliability and error analysis method (DREAM) is a method to support a systematic classification of accident causation information and to facilitate aggregation of that information into patterns of contributing factors. This is the first time that DREAM was used to analyze pedestrian–vehicle crashes and provide suggestions for road improvements in China.

Results: The key issues adversely affecting pedestrian safety can be organized in 4 distinctive thematic categories, namely, deficient intersection safety infrastructure, lack of pedestrian safety education, inadequate driver training, and insufficient traffic law enforcement. Given that resources for traffic safety investments in rural areas are limited, it is determined that the potential countermeasures should focus on low-cost, easily implementable, and long-lasting measures increasing the visibility and predictability of pedestrian movement and reducing speeding and irresponsible driving among drivers and risk-taking behaviors among pedestrians.

Conclusions: Accident prevention treatments are suggested based on their suitability for rural areas in southwest China. These countermeasures include introducing better access management and traffic calming treatments, providing more opportunities for pedestrian education, and enhancing the quality of driver training and traffic law enforcement.     

Identifying the crash characteristics on freeway segments based on different ramp influence areas

Objective: This study aimed to explore the relationship between crash types and different freeway segments and identify the factors contributing to crashes on different freeway segments. Unlike most of the previous studies on freeway segments, this study separately investigates basic freeway segments, single ramp influence segments, and multiple ramp influence segments.

Methods: Nonlinear canonical correlation analysis (NLCCA) and proportionality test were used to identify the relationship between crash types and different freeway segments. The data sets for the different freeway segments accumulated for this study consist of 9,867 crash samples with complete information on all 22 chosen variables. A multinomial logit model (MNL) was used to estimate the influence of crash factors on different freeway segments.

Results: The results show that weaving and diverge overlap influence segments (WD) are more likely to have injury or fatal crashes; diverge and diverge overlap influence segments (DD) are more likely to have property damage–only (PDO) crashes; merge and merge overlap influence segments (MM) are more likely to have sideswipe crashes; and WD have non-sideswipe crashes; WD and weaving overlap influence segments (MW) are more likely to have rear end crashes; and MM segments are less likely to have hit object crashes. The contributing factors are identified by MNL and the results show that different traffic variables, environmental variables, vehicle variables, driver variables, and geometric variables significantly affected the likelihood of crashes on different freeway segments.

Conclusions: Investigation of crash types and factors contributing to crashes on different freeway segments is based on multiple ramp influence segments, which can promote a better understanding of the safety performance of various freeway segments.     

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.     

Fatally injured drivers in Norway 2005–2015—Trends in substance use and crash characteristics

Objective: Norway introduced a “Vision Zero” strategy in 2001, using multiple approaches, aiming toward a future in which no one will be killed or seriously injured in road traffic crashes (RTCs). Official statistics show that the number of fatally injured road users has declined substantially from 341 deaths in 2000 to 117 in 2015. In-depth crash investigations of all fatal RTCs started in Norway in 2005. The aim of this study was to investigate whether fatal crash characteristics, vehicle safety features, and prevalence of drugs and/or alcohol among fatally injured drivers and riders has changed during 2005–2015, accompanying the reduction in road fatalities.

Methods: Data on all car/van drivers and motorcycle/moped riders fatally injured in RTCs during 2005–2015 were extracted from Norwegian road traffic crash registries and combined with forensic toxicology data.

Results: The proportion of cars and motorcycles with antilock braking systems and cars with electronic stability control, increased significantly during the study period. The prevalence of nonuse of seat belts/helmets and speeding declined among both fatally injured drivers and riders. In addition, the prevalence of alcohol declined, though no significant change in the total prevalence of other substances was noted.

Conclusion: The observed changes toward more safety installations in cars and motorcycles and lower prevalence of driver-related risk factors like alcohol use, speeding, and nonuse of seat belts/helmets among fatally injured drivers/riders may have contributed to the decrease in road traffic deaths.     

Modeling crash injury severity by road feature to improve safety

Objective: The objective of this research is 2-fold: to (a) model and identify critical road features (or locations) based on crash injury severity and compare it with crash frequency and (b) model and identify drivers who are more likely to contribute to crashes by road feature.

Method: Crash data from 2011 to 2013 were obtained from the Highway Safety Information System (HSIS) for the state of North Carolina. Twenty-three different road features were considered, analyzed, and compared with each other as well as no road feature. A multinomial logit (MNL) model was developed and odds ratios were estimated to investigate the effect of road features on crash injury severity.

Results: Among the many road features, underpass, end or beginning of a divided highway, and on-ramp terminal on crossroad are the top 3 critical road features. Intersection crashes are frequent but are not highly likely to result in severe injuries compared to critical road features. Roundabouts are least likely to result in both severe and moderate injuries. Female drivers are more likely to be involved in crashes at intersections (4-way and T) compared to male drivers. Adult drivers are more likely to be involved in crashes at underpasses. Older drivers are 1.6 times more likely to be involved in a crash at the end or beginning of a divided highway.

Conclusions: The findings from this research help to identify critical road features that need to be given priority. As an example, additional advanced warning signs and providing enlarged or highly retroreflective signs that grab the attention of older drivers may help in making locations such as end or beginning of a divided highway much safer. Educating drivers about the necessary skill sets required at critical road features in addition to engineering solutions may further help them adopt safe driving behaviors on the road.     

Wrong-way driving crashes: A multiple correspondence approach to identify contributing factors

Objective: Wrong-way driving (WWD) crashes result in 1.34 fatalities per fatal crash, whereas for other non-WWD fatal crashes this number drops to 1.10. As such, further in-depth investigation of WWD crashes is necessary. The objective of this study is 2-fold: to identify the characteristics that best describe WWD crashes and to verify the factors associated with WWD occurrence.

Methods: We collected and analyzed 15 years of crash data from the states of Illinois and Alabama. The final data set includes 398 WWD crashes. The rarity of WWD events and the consequently small sample size of the crash database significantly influence the application of conventional log-linear models in analyzing the data, because they use maximum-likelihood estimation. To overcome this issue, in this study, we employ multiple correspondence analysis (MCA) to define the structure of the crash data set and identify the significant contributing factors to WWD crashes on freeways.

Results: The results of the present study specify various factors that characterize and influence the probability of WWD crashes and can thus lead to the development of several safety countermeasures and recommendations. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions were among the most significant contributors to WWD crashes.

Conclusions: Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.     

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.     

Impact of perceptual countermeasures on driving behavior at curves using driving simulator

Objective: The probability of crash occurrence on horizontal curves is 1.5 to 4 times higher than that on tangent sections. A majority of these crashes are associated with human errors. Therefore, human behavior in curves needs to be corrected.

Methodology: In this study, 2 different road marking treatments, optical circles and herringbone patterns, were used to influence driver behavior while entering a curve on a 2-lane rural road section. A driving simulator was used to perform the experiment. The simulated road sections are replicas of 2 real road sections in Flanders.

Results: Both treatments were found to reduce speed before entering the curve. However, speed reduction was more gradual when optical circles were used. A herringbone pattern had more influence on lateral position than optical circles by forcing drivers to maintain a safe distance from opposing traffic in the adjacent lane.

Conclusion: The study concluded that among other low-cost speed reduction methods, optical circles are effective tools to reduce speed and increase drivers’ attention. Moreover, a herringbone pattern can be used to reduce crashes on curves, mainly for head-on crashes where the main problem is inappropriate lateral position.     

Reliability and validity of the Multidimensional Driving Style Inventory in Chinese drivers

Objective: The Multidimensional Driving Style Inventory (MDSI) has been widely used in assessing the associations between driving styles and traffic violations and accidents in different cultural contexts. Due to the lack of a valid instrument to assess driving style, studies concerning driving style and its influence factors are limited in China. Thus, this study aimed to adapt and validate a Chinese version of the MDSI.

Methods: Seven hundred and sixty drivers aged from 19 to 60 years old were asked to complete the MDSI and a personality scale (trait anger, sensation seeking, altruism, and normlessness). Exploratory factory analysis (EFA) and confirmatory factor analysis (CFA) were used to obtain the factorial structure of the MDSI. The external validity of the MDSI was then evaluated by examining the associations between driving styles and personality traits, demographic variables, and traffic violations and crashes.

Results: EFA revealed a 6-factor structure of the MDSI (i.e., risky, anxious, angry, distress reduction, careful, and dissociative driving styles). CFA confirmed that the model fit of the MDSI was acceptable. The MDSI factors were moderately or weakly correlated with trait anger, sensation seeking, altruism, and normlessness. Significant gender and age differences in driving styles were found. Moreover, drivers who had traffic violations or crashes in the past year scored higher on risky and angry driving styles and lower on careful driving style than those who had not have traffic violations or crashes.

Conclusions: The Chinese version of the MDSI proved to be a reliable, valid, and highly useful instrument. It could be used to assess Chinese drivers who are at risk due to their maladaptive driving styles.     

Road traffic injuries among riders of electric bike/electric moped in southern China

Objective: Electric bike/moped-related road traffic injuries have become a burgeoning public health problem in China. The objective of this study was to identify the prevalence and potential risk factors of electric bike/moped-related road traffic injuries among electric bike/moped riders in southern China.

Methods: A cross-sectional study was used to interview 3,151 electric bike/moped riders in southern China. Electric bike/moped-related road traffic injuries that occurred from July 2014 to June 2015 were investigated. Data were collected by face-to-face interviews and analyzed between July 2015 and June 2017.

Results: The prevalence of electric bike/moped-related road traffic injuries among the investigated riders was 15.99%. Electric bike/moped-related road traffic injuries were significantly associated with category of electric bike (adjusted odds ratio [AOR] = 1.36, 95% confidence interval [CI], 1.01–1.82), self-reported confusion (AOR = 1.77, 95% CI, 1.13–2.78), history of crashes (AOR = 6.14, 95% CI, 4.68–8.07), running red lights (AOR = 3.57, 95% CI, 2.42–5.25), carrying children while riding (AOR = 1.96, 95% CI, 1.37–2.85), carrying adults while riding (AOR = 1.68, 95% CI, 1.23–2.28), riding in the motor lane (AOR = 2.42, 95% CI, 1.05–3.93), and riding in the wrong traffic direction (AOR = 1.63, 95% CI, 1.13–2.35). In over 77.58% of electric bike/moped-related road traffic crashes, riders were determined by the police to be responsible for the crash. Major crash-causing factors included violating traffic signals or signs, careless riding, speeding, and riding in the wrong lane.

Conclusion: Traffic safety related to electric bikes/moped is becoming more problematic with growing popularity compared with other 2-wheeled vehicles. Programs need to be developed to prevent electric bike/moped-related road traffic injuries in this emerging country.     

Ticketing aggressive cars and trucks (TACT): How does it work on city streets?

Objective: The purpose of this study was to determine the feasibility of modifying the Ticking Aggressive Cars and Trucks (TACT) program, originally designed to work on state highways, within a metropolitan area to reduce unsafe interactions and their related crashes between drivers of large trucks and passenger vehicles.

Methods: Using crash data, the driving behaviors most commonly associated with large truck and passenger vehicle crashes were identified. A public awareness campaign using media messaging and increased law enforcement was created targeting these associated behaviors. The frequency of these behaviors both before and after the public awareness campaign was determined through observation of traffic at 3 specific locations within the city. Each location had a sufficient volume of large truck and passenger traffic to observe frequent interactions. Pre- and postintervention data were compared using negative binomial regression with generalized estimating equations to evaluate whether the campaign was associated with a reduction in the identified driving behaviors.

Results: A comparison between crash data from before, during, and after the campaign and crashes during the same time periods in previous years did not show a significant difference (P =.081). The number of large trucks observed in traffic remained the same during pre- and postintervention periods (P =.625). The rates of negative interactions per 100 large trucks decreased for both large trucks and passenger vehicles after the intervention, with calculated rate ratios of 0.58 (95% confidence interval [CI], 0.48, 0.70) and 0.31 (95% CI, 0.20, 0.47). The greatest reduction was seen in passenger vehicles following too close, with a rate ratio of 0.21 (95% CI, 0.15, 0.30).

Conclusions: Although designed for reducing crashes on highways, the TACT program can be an effective approach for improving driver behaviors on city streets.     

A semi-automated tool for identifying agricultural roadway crashes in crash narratives

Objective: Crash reports contain precoded structured data fields and a crash narrative that can be a source of rich information not included in the structured data. The narrative can be useful for identifying vulnerable roadway users, such as agricultural workers. However, using the narratives often requires manual reviews that are time consuming and costly. The objective of this research was to develop a simple and relatively inexpensive, semi-automated tool for screening crash narratives and expediting the process of identifying crashes with specific characteristics, such as agricultural crashes.

Methods: Crash records for Louisiana from 2010 to 2015 were obtained from the Louisiana Department of Transportation (LaDOTD). Records with narratives were extracted and stratified by vehicle type. The majority of analyses focused on a vehicle type of farm equipment (Type T). Two keyword lists, an inclusion list and an exclusion list, were created based on the published literature, subject-matter experts, and findings from a pilot project. Next, a semi-automated tool was developed in Microsoft Excel to identify agricultural crashes. Lastly, the tool’s performance was assessed using a gold standard set of agricultural narratives identified through manual review.

Results: The tool reduced the search space (e.g., number of narratives that need manual review) for narratives requiring manual review from 6.7 to 59.4% depending on the research question. Sensitivity was high, with 96.1% of agricultural crash narratives being correctly classified. Of the gold standard agricultural narratives, 58.3% included an equipment keyword and 72.8% included a farm equipment brand.

Conclusion: This article provides information on how crash narratives can supplement structured crash data. It also provides an easy-to-implement method to facilitate incorporating narratives into safety research along with keyword lists for identifying agricultural crashes.