Traffic safety and public health in China - Past knowledge, current status, and future directions

Transportation-related harms have developed into a social disease, threatening public safety and health in China. We aimed to increase the global understanding of traffic safety and public health in China from past knowledge, current status, and future directions by collecting, collating, and analyzing the Chinese traffic incidents reported in the published literature. A systematic search of China National Knowledge Infrastructure, Weipu, and published articles referenced in PubMed, Web of Science and ProQuest between January 1, 1988 and April 30, 2023 was performed. China encountered the first recorded traffic accident as early as three thousand years ago in the Shang Dynasty. An increase in vehicle capacity and velocity increased the traffic risks during the transition from rickshaws and livestock to motor vehicles in varying traffic environments. Humans are not only the decisive factor of a large number of vehicles, traffic routes, and environmental variables, but also the victims at the end and starting point of traffic accidents. Injuries (mechanical force, burns) and diseases (traffic-related air pollution, noise) caused by traffic activities not only threaten public health, but also cause risks to safe driving. Analysis of traffic activities and biomarkers promotes the treatment of traffic injuries in ethology and medicine. China prepared for the construction of healthy transportation in the "decade of road safety" toward an estimation of worldwide road traffic injuries in 2030. Improvement of traffic safety concerning public health under the "Outline of the National Comprehensive Three-dimensional Transportation Network Planning" in China will propel the realization of worldwide traffic environmental advancement.

A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns

BACKGROUND

Pattern recognition of pedestrians' traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians' traffic behavior pattern by fuzzy clustering algorithm and assess the factors related to higher-risk traffic behavior of pedestrians. Study Design: This study is a secondary methodological study based on the data from a cross-sectional study.

METHODS

The fuzzy c-means (FCM), as a machine learning clustering method, was conducted to identify the pattern of traffic behaviors by collecting data from 600 pedestrians in Urmia, Iran via "the Pedestrian Behavior Questionnaire" (PBQ) and using 5 domains of PBQ. Multiple logistic regression was fitted to identify risk factors of traffic behaviors.

RESULTS

Results revealed two clusters consisting of lower-risk and higher-risk behaviors. The majority of pedestrians (64.33%) were in the lower-risk cluster. Subjects≤33 years old (Odds ratio [OR]=1.92, P<0.001), subjects with≤6 years of education (OR=1.74, P=0.010), males (OR=1.90, P=0.001), unmarried pedestrians (OR=3.61, P=0.007), and users of public transportation (OR=2.01, P=0.002) were more likely to have higher-risk traffic behavior.

CONCLUSION

We identified traffic behavior patterns of Urmia pedestrians with lower-risk and higher-risk behaviors via FCM. The findings from this study would be helpful for policymakers to promote safety measures and train pedestrians.

Global road safety 2010-18: An analysis of Global Status Reports

BACKGROUND

Road traffic injuries (RTIs) pose a significant health burden with 1.35 million individuals dying on the world's roads annually. Nearly a decade ago, based on agreed road safety indicators, global commitments were encouraged to dramatically reduce this burden as part of the United Nation's Decade of Action for Road Safety 2011-2020.

METHODS

The analysis was based on global level data from three Global Status Reports on Road Safety published by the World Health Organization in 2013, 2015, and 2018. A total of 161 countries that consistently reported statistics for all three reports were included in the analysis. Descriptive analyses, t-test, Wilcoxon rank-sum test, and Spearman's rank correlation were performed to evaluate past and current trends in road traffic deaths and countries' progress in achieving key road safety indicators.

RESULTS

We found no significant decline in global road traffic death rates from 2010 to 2016 and in fact, death rates increased in low-income countries (LICs) and the African Region. Death rates were highly dependent on income level of the country, with deaths higher in low- and middle-income countries (LMICs) when compared to high-income countries (HICs). We found that the strength of enforcement of speed laws and child restraint laws increased from 2011 to 2017. However, we did not find a correlation between enforcement of the five key prevention policies (speeding, drink-driving, seatbelts, helmets, and child restraints) and death rate. In terms of advancement in achieving key road safety indicators, there was slow progress in adopting most of the recommended policies and practices based on the five pillars (road safety management, safer roads and mobility, safer vehicles, safer road users, and post-crash response).

CONCLUSION

Despite global efforts during the past decade, road traffic deaths remain disproportionally high in LMICs and African countries as shown by global reports, and progress in achieving global road safety indicators is slow. Countries need to greatly accelerate the implementation of interventions proven to reduce RTIs in order to meet the goals of the second Decade of Action for Road Safety.

Analysis of the regional distribution of road traffic mortality and associated factors in Japan

BACKGROUND

Regional differences in road traffic (RT) mortality among municipalities have not been revealed in Japan. Further, the association between RT mortality and regional socioeconomic characteristics has not been investigated. We analyzed geographic differences in RT mortality and its associated factors using the Vital Statistics in Japan.

METHODS

We used data on RT mortality by sex and municipality in Japan from 2013 to 2017. We calculated the standardized mortality ratio (SMR) of RT for each municipality by sex using an Empirical Bayes method. The SMRs were mapped onto a map of Japan to show the geographic differences. In addition, an ecological study investigated the municipal characteristics associated with the SMR using demographic socioeconomic, medical, weather, and vehicular characteristics as explanatory variables. The ecological study used a spatial statistical model.

RESULTS

The mapping revealed that the number of municipalities with a high SMR of RT (SMR > 2) was larger in men than in women. In addition, SMRs of capital areas (Kanagawa and Tokyo prefectures) tended to be low in men and women. The regression analysis revealed that population density was negatively associated with the SMR in men and women, and the degree of the association was the largest among explanatory variables. In contrast, there was a positive association between the proportion of non-Japanese persons and SMR. The proportions of lower educational level (elementary school or junior high school graduates), agriculture, forestry, and fisheries workers, service workers, and blue-collar workers were positively associated with the SMR in men. The proportion of unemployed persons was negatively associated with the SMR in men.

CONCLUSIONS

Socioeconomic characteristics are associated with geographic differences in RT mortality particularly in men. The results suggested preventive measures targeted at men of low socioeconomic status and non-Japanese persons are needed to decrease RT mortality further.

Malignant mixes: The overlap of motor vehicle crashes and crime in Stockholm, Sweden

Places that concentrate both motor vehicle crashes and crime in Stockholm, Sweden were examined for common socio-economic, land use, and neighborhood characteristics. Using vehicle crash (N = 3,700) and non-traffic crime (N = 605,052) data from 2016 to 2018, hot spots of these two sets of events and their overlap were identified. Crash hot spots captured 14% of the crashes in only 0.5% of Stockholm's area while crime hot spots captured 27% of the recorded offences in less than 1% of the area. There was overlap in these hot spots for 7% of the crashes and 10% of the crimes. To model predictors, the events were allocated to roadway segments (N = 5511) and tested using a Poisson-Gamma-CAR spatial regression model. Both crashes and crimes exhibit a clear center-periphery pattern that varies over time and by type of crashes and crimes. Crashes tended to occur on roadways with higher average daily traffic (ADT) while crimes tend to occur on roadways with lower ADT with around half occurring on residential streets. Both types of incidents tended to be higher in lower income neighborhoods. Land uses common to both types of harm were the location of underground stations, ATM machines, and alcohol-serving businesses. These are places where people and cars converge at particular times. The effect of these events on police, emergency, and medical services is discussed.

Facilitating research on racial and ethnic disparities and inequities in transportation: Application and evaluation of the Bayesian Improved Surname Geocoding (BISG) algorithm

OBJECTIVE

Racial and ethnic disparities and/or inequities have been documented in traffic safety research. However, race/ethnicity data are often not captured in population-level traffic safety databases, limiting the field's ability to comprehensively study racial/ethnic differences in transportation outcomes, as well as our ability to mitigate them. To overcome this limitation, we explored the utility of estimating race and ethnicity for drivers in the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse using the Bayesian Improved Surname Geocoding (BISG) algorithm. In addition, we summarize important recommendations established to guide researchers developing and implementing racial and ethnic disparity research.

METHODS

We applied BISG to estimate population-level race/ethnicity for New Jersey drivers in 2017 and evaluated the concordance between reported values available in integrated administrative sources (e.g., hospital records) and BISG probability distributions using an area under the receiver operator curve (AUC) within each race/ethnicity category. Overall AUC was calculated by weighting each AUC value by the population count in each reported category. In an exemplar analysis using 2017 crash data, we conducted an analysis of average monthly police-reported crash rates in 2017 by race/ethnicity using the NJ-SHO and BISG sets of race/ethnicity values to compare their outputs.

RESULTS

We found excellent or outstanding concordance (AUC ≥0.86) between reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers. We found poor concordance for American Indian/Alaskan Native drivers (AUC= 0.65), and concordance was no better than random assignment for Multiracial drivers (AUC = 0.52). Among White, Hispanic, Asian/Pacific Islander, and American Indian/Alaskan native drivers, monthly crash rates calculated using both NJ-SHO reported race/ethnicity values and BISG probabilities were similar. Monthly crash rates differed by 11% for Black drivers, and by more than 200% for Multiracial drivers.

CONCLUSION

Findings of excellent or outstanding concordance between and mostly similar crash rates derived from reported race/ethnicity and BISG probabilities for White, Hispanic, Black, and Asian/Pacific Islander drivers (98.9% of all drivers in this sample) demonstrate the potential utility of BISG in enabling research on transportation disparities and inequities. Concordance between race/ethnicity values were not acceptable for American Indian/Alaskan Native and Multiracial drivers, which is similar to previous applications and evaluations of BISG. Future work is needed to determine the extent to which BISG may be applied to traffic safety contexts.

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study

A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlation and intrinsic assumptions, which, if flouted, may yield biased predictions. The present study investigates the possibility of using the eXtreme Gradient Boosting (XGBoost) model compared with few traditional machine learning algorithms (logistic regression, random forest, and decision tree) for crash injury severity analysis. The data used in this study was obtained from the traffic safety department, ministry of transport (MOT) at Riyadh, KSA, and contains 13,546 motor vehicle collisions along 15 rural highways reported between January 2017 to December 2019. Empirical results obtained using k-fold (k = 10) for various performance metrics showed that the XGBoost technique outperformed other models in terms of the collective predictive performance as well as injury severity individual class accuracies. XGBoost feature importance analysis indicated that collision type, weather status, road surface conditions, on-site damage type, lighting conditions, and vehicle type are the few sensitive variables in predicting the crash injury severity outcome. Finally, a comparative analysis of XGBoost based on different performance statistics showed that our model outperformed most previous studies.

A state-of-the-art review of factors that predict mortality among traumatic injury patients following a road traffic crash

BACKGROUND

Traffic related injuries are a major public health problem worldwide with millions of people dying every year. The objective of this state-of-the-art review was to identify the factors reported in the literature as being associated with mortality for trauma patients following road traffic crashes.

METHOD

A systematic search was undertaken of PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane Library databases to identify articles published in the past two decades (2000-2020). Of 8257 records, 4507 remained for title, abstract and full text screening after duplicates were removed. The level of evidence of selected studies was assessed using The National Health and Medical Research Council (NHMRC) guideline.

RESULTS

This review included eighty primary research studies examining mortality risk factors following a road traffic crash. The study identified factors in five categories; (i) demographic factors; (ii) behavioural factors; (iii) crash characteristics; (iv) environmental and timing factors; (v) injury severity and pre-injury/condition. The primary studies are summarised in a matrix. Included studies included level II to level IV levels of evidence based on the NHMRC criteria.

CONCLUSION

This study shows that there are a large number of factors associated with increased risk of mortality following diverse types of traffic crashes. Understanding these wide-ranging factors can strengthen injury and mortality prevention by guiding decision makers about where to focus strategy implementation.

Discovering latent themes in traffic fatal crash narratives using text mining analytics and network topology

The proliferation of digital textual archives in the transportation safety domain makes it imperative for the inventions of efficient ways of extracting information from the textual data sources. The present study aims at utilizing crash narratives complemented by crash metadata to discern the prevalence and co-occurrence of themes that contribute to crash incidents. Ten years (2009-2018) of Michigan traffic fatal crash narratives were used as a case study. The structural topic modeling (STM) and network topology analysis were used to generate and examine the prevalence and interaction of themes from the crash narratives that were mainly categorized into pre-crash events, crash locations and involved parties in the traffic crashes. The main advantage of the STM over the other topic modeling approaches is that it allows the researchers to discover themes from documents and estimate how the topic relates to the document metadata. Topics with the highest prevalence for the angle, head-on, rear-end, sideswipe and single motor vehicle crashes were crash at stop-sign, crossing the centerline, unable to stop, lane change maneuver and run-off-road crash, respectively. Eigenvector centrality measure in network topology showed that event-related topics were consistently central in articulating the crash occurrence. The centrality and association between topics varied across crash types. The efficacy of generated topics in classifying crashes by type was tested using a machine learning algorithm, Random Forest. The classification accuracy in the held-out sample ranged between 89.3 % for sideswipe crashes to 99.2 % for single motor vehicle crashes. High classification accuracy suggests that automation of crash typing and consistency checks can be accomplished effectively by using extracted latent themes from the crash narratives.

"My right to walk, my right to live": pedestrian fatalities, roads and environmental features in Benin

Background

The implementation of road safety interventions in many developing countries usually focuses on the behavior of users. In order to draw more attention on the role of road infrastructure and physical environment in road safety interventions, this study aims to analyze the environmental and road factors associated with the pedestrians involved in traffic crashes in Benin.

Method

The method used was an analysis of national road crash statistics for the period 2008 to 2015. The information available included the circumstances surrounding the collision, the road infrastructure, the vehicles and the individuals involved. A multiple logistic regression was used to identify predictors of pedestrian mortality in traffic crashes.

Results

During the period studied, 3760 crashes involved at least one pedestrian. The death rate among these pedestrians was 27.74% (CI 95%: 26.31–29.20). The mortality predictors were the area in which the crash occurred (OR = 4.94; CI 95%: 4.10–5.94), the day of the crash (OR = 2.17; CI 95%:1.34–3.52), light levels (OR = 1.30; CI 95%: 1.06–1.59), road classification (OR = 1.79; CI 95%: 1.46–2.20), the condition of the road surface (2.04, CI 95%: 1.41–2.95) and the position of the pedestrian during the crash (OR = 1.69; CI 95%: 1.19–2.38).

Conclusions

These results support the need for a holistic approach to interventions aiming to tackle deaths on roads. Interventions should integrate environmental factors for greater pedestrian safety around roads with appropriate signs, roads in good condition and awareness campaigns for a proper use of road infrastructures.

Components of driving competency measurement in the elderly: A scoping review

Background: Iran will face the "aging Tsunami" phenomenon by the 2040s. Therefore, paying attention to the elderly's driving to maintain and promote their independence and quality of life on the one hand and paying attention to the dangers of driving by the elderly for road safety will be important. The purpose of this research was to determine the components of driving competency in the elderly.

Methods: The research has employed a scoping review. To this end, searches of scientific databases were conducted using keywords between 1990 and 2019. The process of selecting the documentation was-based on the PRISMA chart.

Results: In the first phase, 2769 records were found, and finally, 37 records met the inclusion criteria set for this study. The results indicated that 18 components were extracted that were classified into seven main categories including cognitive, sensory, motor, mental functions, and medications, diseases, and driving history.

Conclusion: Sensory, motor, and cognitive abilities are the most important components of elderly safe driving. Therefore, as age increases, chronic disease, multiple drug use, and subsequent problems increase. This can affect the ability to drive safely and can cause traffic injuries. Therefore, it is recommended to use the results of this research to design a suitable tool and model for assessing driving competency in the elderly.

Spatial relationships between alcohol outlet densities and drunk driving crashes: An empirical study of Tianjin in China

INTRODUCTION

Numerous studies have demonstrated the close relationship between alcohol availability and alcohol-related crashes. However, there is still a lack of spatial empirical analysis regarding this relationship, particularly in large cities of developing countries. Differences in alcohol outlets and drinking patterns in these cities may lead to quite different patterns of crash outcomes.

METHOD

3356 alcohol-related crashes were collected from the blood-alcohol test report of a forensic institution in Tianjin, China. Density of alcohol outlets such as retail locations, entertainment venues, restaurants, hotels, and companies were extracted based on 2114 Traffic Analysis Zones (TAZ) together with the residential and demographic characteristics. After applying the exploratory spatial data analysis, this research developed and compared the traditional Ordinary Least Square model (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM) to explore spatial effects of all the variables.

RESULTS

The results of incremental spatial autocorrelation show that the most significant distance threshold of alcohol-related roadway traffic crashes is 3 km. The SDM is found to be the optimal spatial model to characterize the relationship between alcohol outlets and crashes. The number of alcohol-involved traffic crashes is positively related to population density and retail density, but negatively related to the company density, hotel density, and residential density within the same TAZ. Meanwhile, dense population and hotels have reverse spillover effects in adjacent zones.

CONCLUSIONS

The significant spatial direct effect and spillover effect of alcohol outlet densities on drunk driving crashes should not be neglected. These findings could help improve transportation planning, traffic law enforcement and traffic management for large cities in developing countries.

Factors associated with sleep quality of Thai intercity bus drivers

This study aimed to describe sleep quality and explore factors associated with poor sleep quality in Thai intercity bus drivers. A cross-sectional design was employed with a sample of intercity bus drivers from 4 bus transportation companies. The Thai-PSQI was used to identify sleep quality, and the Thai Berlin Questionnaire used to assess sleep apnea risk. Data analysis included descriptive statistics and logistic regression. A total of 338 surveys were analyzed. All bus drivers were male; almost 66% of the bus drivers were defined as poor sleepers, and 18.1% were assessed as being at high risk for obstructive sleep apnea. Working night shifts (OR=20.6), rotating day or night shifts (OR=17.0), alcohol consumption (OR=2.7), being married (OR=3.1), and not exercising (OR=2.3) were related to poor sleep quality. The majority of the Thai intercity bus drivers in our study reported poor sleep quality indicating that action is required at both company and individual levels to encourage the adoption of healthy lifestyles and improvement of working conditions.

Pattern of road traffic crash hot zones versus probable hot zones in Tunisia: A geospatial analysis

Focusing on how hot zones mapping can predict spatial patterns of crashes and how different mapping approaches compare can help to better inform their application in practice. This study examines the stability of the performance of two spatial autocorrelation measures on the basis of a Road Safety Risk Index (RSRI) through the comparison of the results for three regions (North-West, Center-East, and Center-West) and for three time periods (2002-2005, 2006-2009 and 2010-2013) in Tunisia. Our study differs from others in that it discusses the identification of probable hot zones and enhances the capability to examine a given highway by determining "dangerous probable lengths", which aims to anticipate the traffic crashes in the future. The identified hot zones and probable hot zones exhibit different regional and temporal characteristics. There are clearly some outstanding spatial clusters of crashes covering specific locations. In both Northwest and Center-West regions, the majority of the identified hot zones and probable hot zones predominantly occur along mainly highways characterized by a dominant rural character. In the Center-East region, both hot zones and probable hot zones are mostly spread northeast and south-west more precisely in NH1 and NH2 where many urban activities are taking place. Spatial autocorrelation indices per region address the diversity within the regions and provide us with useful insights that can be translated into safety policies in Tunisia.

Fatal motorcycle crashes in wide urban area of Zagreb, Croatia-A 10-year review

Objective: The objective of our study was to determine the prevalence of alcohol and drug intoxication among fatally injured motorcyclists in a wide urban area of Zagreb, Croatia. Methods: We conducted a single-center observational retrospective study over a 10-year period (2007-2016) in 3 counties covering an area including 1.2 million residents. We reviewed the records on fatally injured motorcyclists, collecting information relating to sex, age, cause of death, time of death in relation to the time of the crash, and the circumstances of the crash (time of day, day of the week, season). Blood alcohol concentration (BAC) and toxicology analysis results were collected and analyzed. Results: We identified 163 deaths (95.7% males, 4.3% females). Overall, 64.2% of the victims were 20 to 39 years old. The majority (50.9%) of those fatally injured were responsible for causing the traffic crash; the rest were determined not to be responsible or the responsibility could not be determined. The most frequent causes of death were multiple injuries (55.8%) and isolated head trauma (23.3%). The rider's BAC was above the legal limit for driving (>0.50 g/kg) in 53.8% of cases, with a mean BAC of 1.91 g/kg. There was no difference in riding a motorcycle with a BAC above the legal limit between groups defined as younger (≤39 years of age) and older (≥40 years of age). The number of people with an illegal BAC was significantly higher during weekends than during the work week. The BAC of riders who were responsible for the crash was significantly higher than that of those who were not responsible or whose responsibility could not be determined. Use of illegal drugs or nontherapeutic use of legal drugs was not common and was detected in 10.4% of fatally injured riders. Conclusions: Alcohol intoxication has a major role in motorcycle crash-related mortality. A significant difference in BAC between fatally injured riders responsible for the accident and those who were not responsible implies that measures directed toward prevention of drinking and driving behavior could lower the number of fatal motorcycle crashes. Weekend measures, especially during spring and summer, could have particularly significant effects.

Machines versus humans: People's biased responses to traffic accidents involving self-driving vehicles

Although self-driving vehicles (SDVs) bring with them the promise of improved traffic safety, they cannot eliminate all crashes. Little is known about whether people respond crashes involving SDVs and human drivers differently and why. Across five vignette-based experiments in two studies (total N = 1267), for the first time, we witnessed that participants had a tendency to perceive traffic crashes involving SDVs to be more severe than those involving conventionally human-driven vehicles (HDVs) regardless of their severity (injury or fatality) or cause (SDVs/HDVs or others). Furthermore, we found that this biased response could be a result of people's reliance on the affect heuristic. More specifically, higher prior negative affect tagged with an SDV (vs. an HDV) intensifies people's negative affect evoked by crashes involving the SDV (vs. those involving the HDV), which subsequently results in higher perceived severity and lower acceptability of the crash. Our results imply that people's over-reaction to crashes involving SDVs may be a psychological barrier to their adoption and that we may need to forestall a less stringent introduction policy that allows SDVs on public roads as it may lead to more crashes that could possibly deter people from adopting SDVs. We discuss other theoretical and practical implications of our results and suggest potential approaches to de-biasing people's responses to crashes involving SDVs.

Patterns of traumatic brain injury and six-month neuropsychological outcomes in Uganda

BACKGROUND

Traumatic brain injuries in Uganda are on the increase, however little is known about the neuropsychological outcomes in survivors. This study characterized patients with traumatic brain injury (TBI) and the associated six-month neuropsychological outcomes in a Ugandan tertiary hospital.

METHODS

Patients admitted at Mulago Hospital with head injury from November 2015 to April 2016 were prospectively enrolled during admission and followed up at six months after discharge to assess cognition, posttraumatic stress symptoms (PTSS), depression symptoms and physical disability. The outcomes were compared to a non-head-injury group recruited from among the caretakers, siblings and neighbours of the patients with age and sex entered as covariates.

RESULTS

One hundred and seventy-one patients and 145 non-head injury participants were enrolled. The age range for the whole sample was 1 to 69 years with the non-head injury group being older (mean age (SD) 33.34 (13.35) vs 29.34 (14.13) years of age, p = 0.01). Overall, motorcycle crashes (36/171, 38.6%) and being hit by an object (58/171, 33.9%) were the leading causes of TBI. Head injury from falls occurred more frequently in children < 18 years (13.8% vs 2.8%, p = 0.03). In adults 18 years and older, patients had higher rates of neurocognitive impairment (28.4% vs 6.6%, p < 0.0001), PTSS (43.9% vs 7.9%, p < 0.0001), depression symptoms (55.4% vs 10%, p < 0.0001) and physical disability (7.2% vs 0%, p = 0.002). Lower Glasgow Coma Score (GCS) on admission was associated with neurocognitive impairment (11.6 vs 13.1, p = 0.04) and physical disability (10 vs 12.9, p = 0.01) six months later.

CONCLUSION

This first such study in the East-African region shows that depth of coma on admission in TBI is associated with neurocognitive impairment and physical disability.

Traffic crash analysis with point-of-interest spatial clustering

This paper presents a spatial clustering method for macro-level traffic crash analysis based on open source point-of-interest (POI) data. Traffic crashes are discrete and non-negative events for short-time evaluation but can be spatially correlated with long-term macro-level estimation. Thus, the method requires the evaluation of parameters that reflect spatial properties and correlation to identify the distribution of traffic crash frequency. A POI database from an open source website is used to describe the specific land use factors which spatially correlate to macro level traffic crash distribution. This paper proposes a method using kernel density estimation (KDE) with spatial clustering to evaluate POI data for land use features and estimates a simple regression model and two spatial regression models for Suzhou Industrial Park (SIP), China. The performance of spatial regression models proves that the spatial clustering method can explain the macro distribution of traffic crashes effectively using POI data. The results show that residential density, and bank and hospital POIs have significant positive impacts on traffic crashes, whereas, stores, restaurants, and entertainment venues are found to be irrelevant for traffic crashes, which indicate densely populated areas for public services may enhance traffic risks.

Distraction of cyclists: how does it influence their risky behaviors and traffic crashes?

Background

Undisputedly, traffic crashes constitute a public health concern whose impact and importance have been increasing during the past few decades. Specifically, road safety data have systematically shown how cyclists are highly vulnerable to suffering traffic crashes and severe injuries derived from them. Furthermore, although the empirical evidence is still very limited in this regard, in addition to other human factors involved in cycling crashes, distractions while cycling appear to be a major contributor to the road risk of cyclists.

Objectives

The main objectives of this study were, first, to explore the prevalence and trends of cycling distractions within an international sample of bike users, and second, to determine the influence of such distractions on road crashes suffered by cyclists, simultaneously considering the explanatory role of risky behaviors (errors and traffic violations) as potentially mediating variables between cycling distractions and traffic crashes.

Methods

For this cross-sectional study, we analyzed the data obtained from 1,064 cyclists—61.2% male and 38.8% female—from 20 different countries, who answered an on-line questionnaire on cycling-related features, habits, behaviors and accidents.

Results

The prevalence of different cycling distractions oscillated between 34.7% and 83.6%. The most common distractions were those related to the behavior of other users, physical elements of the road, weather conditions and phone calls. Age trends and differences were also found, thus establishing a positive association between age and distractibility during cycling. Furthermore, the effect of distractions on traffic crashes of cyclists was significant when tested together with age, risk perception and risky behaviors on the road.

Conclusion

The results of this study support the hypotheses that distractions have a major prevalence among bike users, and that they play a significant role in the prediction of the traffic crash rates of cyclists, through the mediation of risky behaviors.

Does gender really matter? A structural equation model to explain risky and positive cycling behaviors

INTRODUCTION

While the use of bicycles as mean of transport is growing worldwide, the increasing rates of traffic crashes involving cyclists have turned into a relevant scientific, public health, and road safety concern. According to several studies, and despite the fact that some countries are taking part in preventive actions, the data indicate that the problem of cycling injuries implies high costs for the community welfare, for the economy, and for healthcare systems, thus proving a clear need for solutions. In this regard, and considering the available empirical evidence, risky and positive riding behaviors have gained significant weight in terms of explaining, intervening in, and preventing traffic crashes of cyclists, and some evidence suggests that gender may influence the road behavior of users.

OBJECTIVE

The objective of this study was to examine the effect of gender on cyclists' risky and positive riding behavior, considering a set of demographic, psychosocial and bike-use-related variables as potential predictors.

METHOD

For this cross-sectional study, data from 1064 cyclists (61.2% males and 38.8% females, aged between 17 and 80) from 20 countries, responding an electronic survey, were analyzed through a multi-group structural equation modeling approach.

RESULTS

Although hourly intensity, psychological distress and level of knowledge of traffic rules similarly predict the risky road behaviors of both genders, age and risk perception are significant behavioral predictors only in the case of male cyclists. On the other hand, positive behaviors of men are predicted by cycling intensity, knowledge of traffic rules and risk perception, while in the case of women psychological distress predicts -to a significant extent- positive behaviors. Age had no significant effect on the explanation of positive behaviors.

CONCLUSION

The findings of this study support the influence of gender in the statistical explanation of risky and protective behaviors, and they also reveal differentiating variables predicting the riding behavior of male and female cyclists.

Fatal crashes involving large numbers of vehicles and weather

INTRODUCTION

Adverse weather has been recognized as a significant threat to traffic safety. However, relationships between fatal crashes involving large numbers of vehicles and weather are rarely studied according to the low occurrence of crashes involving large numbers of vehicles.

METHOD

By using all 1,513,792 fatal crashes in the Fatality Analysis Reporting System (FARS) data, 1975-2014, we successfully described these relationships.

RESULTS

We found: (a) fatal crashes involving more than 35 vehicles are most likely to occur in snow or fog; (b) fatal crashes in rain are three times as likely to involve 10 or more vehicles as fatal crashes in good weather; (c) fatal crashes in snow [or fog] are 24 times [35 times] as likely to involve 10 or more vehicles as fatal crashes in good weather. If the example had used 20 vehicles, the risk ratios would be 6 for rain, 158 for snow, and 171 for fog.

CONCLUSIONS

To reduce the risk of involvement in fatal crashes with large numbers of vehicles, drivers should slow down more than they currently do under adverse weather conditions. Driver deaths per fatal crash increase slowly with increasing numbers of involved vehicles when it is snowing or raining, but more steeply when clear or foggy.

PRACTICAL APPLICATIONS

We conclude that in order to reduce risk of involvement in crashes involving large numbers of vehicles, drivers must reduce speed in fog, and in snow or rain, reduce speed by even more than they already do.

Influence of deficiencies in traffic control devices in crashes on two-lane rural roads

One of the main objectives of all public administrations is reducing traffic crashes. To this end, Road Safety Inspections (RSI) stand out as a key measure. Signaling roads is one of the foremost tasks of RSI. A road that is improperly or poorly signaled can lead to incorrect placement or maneuvers of vehicles and ambiguous situations that can increase the risk of crashes. This paper analyses the relationship between road crashes in two-lane rural highways and certain deficiencies in signaling. The results show that deficiencies such as "incomplete removal of road works markings" or "no guide sign or in incorrect position" are the ones associated with a higher probability of crashes in two-lane rural highways. In view of these results, governmental agencies should verify that the original conditions of a highway are re-established after any construction work is completed. They should also continuously follow up on the signaling of this type of highway in order to maintain optimal conditions.

Hotspots and social background of urban traffic crashes: A case study in Cluj-Napoca (Romania)

Mobility practices have changed dramatically in Romanian towns over the last 25 years, following the collapse of socialist mobility restrictions. Urban areas like Cluj-Napoca are facing both increasing immigration and car mobility, and therefore increasing levels of road traffic crashes. The analysis of traffic crashes is one of the most important elements for improving the road safety policy. This paper is divided in two parts. In the first one, the authors focus on identifying the traffic crash hotspots along the street network, while in the second part they discuss the social background of road traffic crash occurrence. The first step in analyzing traffic crashes is to determine crash hotspots. A four-year record (2010-2013) provided by the Traffic Department of the General Inspectorate of Romanian Police (GIRPTD) was used. As a method of hotspot determination, the Kernel Density Estimation tool was employed, in the frame of the spatial analysis along network (SANET). The outcome was the hotspot map of traffic crashes in Cluj-Napoca. The results have revealed 4 categories of street segments: not-dangerous, low-dangerous, medium-dangerous and high-dangerous. Based on this classification, at least 4 dangerous areas were identified, located at the city entrances-exits (in the West, North-West and East) and the city center (the most dangerous zone). The second part of the paper focuses on social groups involved in car crashes. The following are considered: age, gender and blood alcohol concentration of the person (driver or pedestrian) found guilty for every individual crash.

Can child-pedestrians' hazard perception skills be enhanced?

OBJECTIVE

Traffic collisions yield a substantial rate of morbidity and injury among child-pedestrians. We explored the formation of an innovative hazard perception training intervention - Child-pedestrians Anticipate and Act Hazard Perception Training (CA(2)HPT). Training was based upon enhancing participants' ability to anticipate potential hazards by exposing them to an array of traffic scenes viewed from different angles.

METHOD

Twenty-four 7-9-year-olds have participated. Trainees underwent a 40-min intervention of observing typical residential traffic scenarios in a simulated dome projection environment while engaging in a hazard detection task. Trainees were encouraged to note differences between the scenarios presented to them from separate angles (a pedestrian's point-of-view and a higher perspective angle). Next, trainees and control group members were required to perform crossing decision tasks.

RESULTS

Trainees were found to be more aware of potential hazards related to restricted field of view relative to control.

CONCLUSIONS

Child pedestrians are responsive to training and actively detecting materialized hazards may enrich child-pedestrians' ability to cross roads.

Acceptance of drinking and driving and alcohol-involved driving crashes in California

BACKGROUND

Alcohol-impaired driving accounts for substantial proportion of traffic-related fatalities in the U.S. Risk perceptions for drinking and driving have been associated with various measures of drinking and driving behavior. In an effort to understand how to intervene and to better understand how risk perceptions may be shaped, this study explored whether an objective environmental-level measure (proportion of alcohol-involved driving crashes in one's residential city) were related to individual-level perceptions and behavior.

METHODS

Using data from a 2012 cross-sectional roadside survey of 1147 weekend nighttime drivers in California, individual-level self-reported acceptance of drinking and driving and past-year drinking and driving were merged with traffic crash data using respondent ZIP codes. Population average logistic regression modeling was conducted for the odds of acceptance of drinking and driving and self-reported, past-year drinking and driving.

RESULTS

A non-linear relationship between city-level alcohol-involved traffic crashes and individual-level acceptance of drinking and driving was found. Acceptance of drinking and driving did not mediate the relationship between the proportion of alcohol-involved traffic crashes and self-reported drinking and driving behavior. However, it was directly related to behavior among those most likely to drink outside the home.

DISCUSSION

The present study surveys a particularly relevant population and is one of few drinking and driving studies to evaluate the relationship between an objective environmental-level crash risk measure and individual-level risk perceptions. In communities with both low and high proportions of alcohol-involved traffic crashes there was low acceptance of drinking and driving. This may mean that in communities with low proportions of crashes, citizens have less permissive norms around drinking and driving, whereas in communities with a high proportion of crashes, the incidence of these crashes may serve as an environmental cue which informs drinking and driving perceptions. Perceptual information on traffic safety can be used to identify places where people may be at greater risk for drinking and driving. Community-level traffic fatalities may be a salient cue for tailoring risk communication.

"Crashing the gates" - selection criteria for television news reporting of traffic crashes

This study investigates which crash characteristics influence the probability that the crash is reported in the television news. To this purpose, all news items from the period 2006-2012 about traffic crashes from the prime time news of two Belgian television channels are linked to the official injury crash database. Logistic regression models are built for the database of all injury crashes and for the subset of fatal crashes to identify crash characteristics that correlate with a lower or higher probability of being reported in the news. A number of significant biases in terms of crash severity, time, place, types of involved road users and victims' personal characteristics are found in the media reporting of crashes. More severe crashes are reported in the media more easily than less severe crashes. Significant fluctuations in media reporting probability through time are found in terms of the year and month in which the crash took place. Crashes during week days are generally less reported in the news. The geographical area (province) in which the crash takes place also has a significant impact on the probability of being reported in the news. Crashes on motorways are significantly more represented in the news. Regarding the age of the involved victims, a clear trend of higher media reporting rates of crashes involving young victims or young fatalities is observed. Crashes involving female fatalities are also more frequently reported in the news. Furthermore, crashes involving a bus have a significantly higher probability of being reported in the news, while crashes involving a motorcycle have a significantly lower probability. Some models also indicate a lower reporting rate of crashes involving a moped, and a higher reporting rate of crashes involving heavy goods vehicles. These biases in media reporting can create skewed perceptions in the general public about the prevalence of traffic crashes and eventually may influence people's behaviour.

The effects of age, gender, and crash types on drivers' injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers' age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers' age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.

Interaction between socio-demographic characteristics: traffic rule violations and traffic crash history for young drivers

Young drivers' high traffic violation involvement rate and significant contribution to traffic crashes compared to older drivers creates the need for detailed analyses of factors affecting young drivers' behaviors. This study is based on survey data collected from 2,057 18-29 year old young adults. Data were collected via face-to-face questionnaire surveys in four different cities in Turkey. The main objective of this study is to identify the relationship between socio-demographic characteristics, traffic rule violations, and traffic crashes among young drivers. Four main traffic rule violations are examined: red light violations, seat belt violations, speeding, and driving under the influence of alcohol, which are decisive in determining driving behavior and traffic crashes. The survey investigates the socio-demographic characteristics, traffic rule violation behavior and traffic crash histories of young adults. Four hypothetical scenarios were prepared for each traffic rule violation and data from the scenarios were modeled using the ordered probit model. Significant variables affecting each traffic rule violation are stated. Finally, significant variables that interact with crash involvements were investigated with binary logit models. According to the data analysis, 23.9% of drivers stated that they were involved in at least one traffic crash within the last three years. This crash rate increases to 38.3% for those who received at least one traffic citation/violation in last three years and peaks to 47.4% for those who were fined for seat belt violations in last three years.