Traffic Database Research Articles

A Support Vector Machine Approach in Predicting Road Traffic Mortality in Malaysia


 
 
 The traffic mortality rate is the baseline through which road safety plans of a country could be evaluated. A reliable and reasonable analysis of road traffic-related injuries and their leading causes is vital to the road safety investigation, evaluation as well as policymaking. Malaysia has the third highest fatality rate from road traffic accidents in Asia as well as in Southeast Asia. Although many researchers have attempted to provide predictive models of Road Traffic Mortality (RTM) in the country, the predictions are found to be rather unsatisfactory in forecasting the causes as well as the future road fatality. It is hypothesized that the inability of the previous models to provide a good prediction of the RTM might be attributed to the complicated and non-linear data relationship of the underlying causes of road traffic accidents. A Support Vector Machine (SVM) is demonstrated to be effective in solving both classifications as well as regression problems owing to its efficacy to cater for the non-linear relationship of a dataset. The present investigation proposed the application of SVM based model variations namely the Linear, Quadratic, Cubic, Fine, Medium, as well as Coarse Gaussian-based SVM in predicting the RTM. A dataset from 1972 to 1994 was obtained from the Malaysian road traffic database. The data were trained on the SVM model variations. It was demonstrated that the Linear-based SVM model can provide a reasonable prediction of the RTM with only a 12 % error. It is, therefore, inferred that a reasonable prediction of RTM in Malaysia could be achieved through the employment of non-conventional statistical techniques.
 
 

Cost-effectiveness of mandatory bicycle helmet use to prevent traumatic brain injuries and death

BackgroundTraumatic brain injury (TBI) is the main cause of mortality and severe morbidity in cyclists admitted to Dutch emergency departments (EDs). Although the use of bicycle helmets is an effective way of preventing TBI, this is uncommon in the Netherlands. An option to increase its use is through a legal enforcement. However, little is known about the cost-effectiveness of such mandatory use of helmets in the Dutch context.The current study aimed to assess the cost-effectiveness of a law that enforces helmet use to reduce TBI and TBI-related mortality.MethodsThe cost-effectiveness was estimated through decision tree modelling. In this study, wearing bicycle helmets enforced by law was compared with the current situation of infrequent voluntary helmet use. The total Dutch cycling population, consisting of 13.5 million people, was included in the model. Model data and parameters were obtained from Statistics Netherlands, the National Road Traffic Database, Dutch Injury Surveillance System, and literature. Effects included were numbers of TBI, death, and disability-adjusted life years (DALY). Costs included were healthcare costs, costs of productivity losses, and helmet costs. Sensitivity analysis was performed to assess which parameter had the largest influence on the incremental cost-effectiveness ratio (ICER).ResultsThe intervention would lead to an estimated reduction of 2942 cases of TBI and 46 deaths.Overall, the incremental costs per 1) death averted, 2) per TBI averted, and 3) per DALY averted were estimated at 1) € 2,002,766, 2) € 31,028 and 3) € 28,465, respectively. Most favorable were the incremental costs per DALY in the 65+ age group: € 17,775.ConclusionsThe overall costs per DALY averted surpassed the Dutch willingness to pay threshold value of € 20,000 for cost-effectiveness of preventive interventions. However, the cost per DALY averted for the elderly was below this threshold, indicating that in this age group largest effects can be reached. If the price of a helmet would reduce by 20%, which is non-hypothetical in a situation of large-scale purchases and use of these helmets, the introduction of this regulation would result in an intervention that is almost cost-effective in all age groups.

Using Road traffic accidents data for hotspots identification: A case study in Marand, Iran

Background: Identification of the hotspots is the first step to for safety promotion. Road traffic accidents data is the best source to know the hotspots and draw the future interventions. The aim of this study was to identify the Marand city hotspots based on road traffic accidents data during 2015-2018. Methods: This was a cross-sectional study conducted in 2019. Traffic Police database was used to extract the road traffic accidents data. Accident type, cause, severity (death, injured and etc), location, month and year were included in the extracted data. P indicator (including all kinds of the accidents severity/ time and road type variables) was estimated for each of the identified zones. Field visit was done to identify the safety defects. Data were analyzed using Excel 2016. Results: Totally 1251 traffic accidents were occurred during 3 years in Marand city, which had led to 26 deaths. 28.8% and 56.83% of the accidents were related to the pedestrians and motorcycles, respectively. Distracted driving and non-respect of priority road were the main reason of the accidents. Based on the P indicator, Emam Hossein Square (p=36.99), Yaldir Bridge zone (p=35.6) and Abasi Street (p=35.6) were the most dangerous zones in Marand city. Safety promotion suggestions were provided for each of the hotspots based on the field visit. Conclusion: Accidents data provides us a vision to identify the hotspots and they could be used for evidence-based safety promotion. Safety promotion initiatives must be implemented through intersect-oral collaboration based on identified priorities.

REDT: Remote exploitation detection technology for network infrastructure

An attacker exploits a remote vulnerability to execute arbitrary code on network infrastructure. It seriously threatens the security of network infrastructure. To detect the exploitation attack in a timely and effective way, in this paper, we present REDT, a remote exploitation detection technology based on the effect of exploitation attack. This method determines whether there is an authentication bypass exploitation by detecting the remote authentication process and saves attack traffic to the anomalous traffic database. With the database, we can replay the attack and analyze the exploitation. In the experiment, the detection accuracy rate reached 97.67%. In addition, this method does not rely on the signatures of the vulnerability and shellcode, so it can detect undisclosed vulnerabilities exploitation and provide abnormal traffic for the study of unknown vulnerabilities.

Validation of not-at-fault driver representativeness assumption for quasi-induced exposure using U.S. national traffic databases

Introduction:The quasi-induced exposure (QIE) method has been widely implemented into traffic safety research. One of the key assumptions of QIE method is that not-at-fault drivers represent the driving population at the time of a crash. Recent studies have validated the QIE representative assumption using not-at-fault drivers from three-or-more vehicle crashes (excluding the first not-at-fault drivers; D3_other) as the reference group in single state crash databases. However, it is unclear if the QIE representativeness assumption is valid on a national scale and is a representative sample of driving population in the United States. The aims of this study were to assess the QIE representativeness assumption on a national scale and to evaluate if D3_other could serve as a representative sample of the U.S. driving population. Method: Using the Fatality Analysis Reporting System (FARS) and the National Occupant Protection Use Survey (NOPUS), distributions of driver gender, age, vehicle type, time, and roadway type among the not-at-fault drivers in clean two-vehicle crashes, the first not-at-fault drivers in three-or-more-vehicle crashes, and the remaining not-at-fault drivers in three-or-more vehicle crashes were compared to the driver population observed in NOPUS. Results: The results showed that with respect to driver gender, vehicle type, time, and roadway type, drivers among D3_other did not show statistical significant difference from NOPUS observations. The age distribution of D3_other driver was not practically different to NOPUS observations. Conclusions: Overall, we conclude that D3_other drivers in FARS represents the driving population at the time of the crash. Practical applications: Our study provides a solid foundation for future studies to utilize D3_other as the reference group to validate the QIE representativeness assumption and has potential to increase the generalizability of future FARS studies.

Improved Novel Clustering Technique for Diverse and Self-motivated Traffic Data Stream for IoT Scenario

Technologies are changing day by day and IoT is worldwide data and may of great business important to various users. sTo create such reasonable data, majority adaptive and K-mediod clustering techniques are employed in data mining. In research work, it focus on comparing adaptive, K-medisod and novel clustering technique to internet-of-things data collection in ITSs (Intelligence Traffic System). In traffic DataStream is composed form online site, it challenges of 30,000 instances with 9 attributes, clusters formed after evaluation and number of clusters is identified after the evaluation. Proposed techniques are significant too easy than some other clustering techniques with respect to all computation recall and precision parameters. In traffic databases depends on the data separation and cluster enhancement that is quality of clusters. To resolve the major issues that over load the system or Centre’s in IoT which consequences the huge kind of data on internet. It evaluated a set of consequences experiments using token and manufacture data from traffic use case view where the traffic considerations from the city monitor. Comparison of clustering methods that helps in determining suitable clustering approach for the offer internet of things database which results in optimal performance metrics.

Quantization and clustering on Riemannian manifolds with an application to air traffic analysis

The goal of quantization is to find the best approximation of a probability distribution by a discrete measure with finite support. When dealing with empirical distributions, this boils down to finding the best summary of the data by a smaller number of points, and automatically yields a K-means-type clustering. In this paper, we introduce Competitive Learning Riemannian Quantization (CLRQ), an online quantization algorithm that applies when the data does not belong to a vector space, but rather a Riemannian manifold. It can be seen as a density approximation procedure as well as a clustering method. Compared to many clustering algorithms, it requires few distance computations, which is particularly computationally advantageous in the manifold setting. We prove its convergence and show simulated examples on the sphere and the hyperbolic plane. We also provide an application to real data by using CLRQ to create summaries of images of covariance matrices estimated from air traffic images. These summaries are representative of the air traffic complexity and yield clusterings of the airspaces into zones that are homogeneous with respect to that criterion. They can then be compared using discrete optimal transport and be further used as inputs of a machine learning algorithm or as indexes in a traffic database.

Research on the Construction of Intelligent Transportation System Based on Internet of Things

At present, with the development of economy, people's living standards are getting higher and higher. Based on the current big city traffic congestion phenomenon, by introducing the concept of Internet and related technologies, the five layers of building based on Internet of things a subject three division wisdom metropolitan transportation system architecture, this architecture includes homework layer, perception layer, network layer, application layer and user layer, a main body is a big city traffic database is the core of the information transmission, three departments include traffic police supervision department, city management and cost management departments. Through this framework, the whole process of urban traffic information can be controlled in real time, so as to guarantee the demand of urban consumers and residents and promote the stable and healthy development of transportation in China's big cities. This paper USES the knowledge of Internet of things to build the intelligent transportation system of big cities, hoping to get inspiration from it.

The association between antidepressant use and deaths from road traffic accidents: a case-crossover study.

Antidepressants are some of the most commonly used psychiatric medications, but little information is available about the effects of antidepressant treatment on the risk of traffic accidents across classes of antidepressants or associated with each substance individually. To investigate the relationship between exposure to antidepressants and risk of fatality in road traffic accidents. We used a Korean national road traffic authority database linked with a national health insurance database between January 1, 2010 and December 31, 2014 and applied a case-crossover design. The study subjects were drivers in South Korea who died from traffic accidents and who had prescriptions for antidepressants within 1year prior to the date of the accident. We compared the status of prescription for antidepressants with the hazard period and four matched control periods using conditional logistic regression, adjusting for other drug use. The trends of antidepressant utilization were described in terms of the number of prescriptions. A case-case-time-control design was applied to drugs with an increasing trend in use and a significant case-crossover odds ratio (OR). A total of 1250 antidepressant-using drivers were included, and an increased risk was observed during the 30-day hazard period (adjusted OR 1.30; 95% CI 1.03-1.63). Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) showed significant risks, but tricyclic antidepressants did not. However, the associations of all antidepressants, SSRIs, SNRIs, escitalopram, and duloxetine did not remain significant after adjusting for trends in utilization. Paroxetine and milnacipran were associated with increased risks, with no obvious increase in their utilization, but the possibility of confounding by indication could have affected the results for milnacipran. Considering the trends of antidepressant prescription and utilization, the use of paroxetine increased the risk of fatal traffic accidents.

A Simulation Study of Predicting Real-Time Conflict-Prone Traffic Conditions

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. In this paper, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e., VISSIM) and the corresponding traffic conflicts data generated by the surrogate safety assessment model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e., support vector machines, k-nearest neighbours, and random forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative of the real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e., 30 s, 1 min, 3 min, and 5 min) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of random forests with 5-min temporal aggregation in the classification results. However, attention should be given to the calibration and validation of the simulation software so as to acquire more realistic traffic data, resulting in more effective prediction of conflicts.

Gender and age distribution of motorcycle crashes in Spain

This study analysed motorcycle crashes in Spain. Ninety-nine thousand three hundred and four motorcycle crash reports filed in the years 2006–2011 were extracted from the Directorate General of Traffic database of crashes with victims. These data were analysed in terms of gender, age groups, trip purpose, type of crash, speed violation, day of the week, harm caused, use of helmet and psychophysical conditions of the driver to study the characteristics of motorcycle crashes in Spain and to assess the differences between male and female motorcycle drivers in these crashes. Significant differences were found in all the variables considered in the study, which implies gender differences in the profile of the injured motorcycle driver. The severity of motorcycle crashes suffered by male drivers is higher than that of women. These results corroborate the need to develop measures differentiated by gender, based on their profile.

A High Resolution Spatiotemporal Model for In-Vehicle Black Carbon Exposure: First Application on Epidemiological Cohorts

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach. In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure with a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative proxy for traffic. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. An external validation, based on a dataset gathered in 2010–2011, quantifies recent exposure reductions inside cars at 33% (mean) and 50% (median). The EU Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. An in-depth analysis of the model covariates and modelling approach will be discussed. The requirements of the epidemiological databases to enable health evaluations will be summarized. A first real-life application of the model on an epidemiological cohort will be introduced.

교통사고-인체상해 데이터베이스 기반 차량 탑승자 상해 중증도 영향요인 분석

본 연구는 교통사고-인체상해 데이터베이스에 저장된 교통사고, 차량파손 자료 및 사고차량 탑승자의 심층 의료기록을 이용하여 사고 당시 탑승자가 중증 외상을 입었지만 생존하는 경우 대비 중증 외상 후 사망한 경우를 비교한 후, 탑승자의 생존 여부에 영향을 미치는 유의미한 요인들을 분석하였다. 이를 위하여 본 연구는 Sequential Logistic Regression모형의 두 포맷을 사용하여 외상 중증도를 예측하였고 Receiver Operation Characteristic 커브의 넓이를 비교하여 더 우수한 포맷을 선택하였다. 그 결과, Sequential Logistic Regression모형의 Forward 포맷이Backward 포맷보다 우수한 성능을 보였다. 특히, 병원 전 응급의료서비스 시간이 24분이 초과될 경우에 중증 외상 후 생존 대비 사망 확률을 약 540% 증가시키는 것으로 나타났다. 또한, 교통사고의 주 원인이 졸음이나 기기조작과 같은 운전자의 부주의일 경우, 중증 외상 후 생존 대비 사망 확률을 약 380% 증가시키는 것으로 나타났다. 이와 같은 결과를 근거로 본 연구는 응급의료 구급차의 개수, 응급의료 처치 인력 및 장비 증가, 긴급차량 우선신호 시스템 운영, 럼블스트립 혹은 고속도로 졸음쉼터 증설, 운전 중 스마트 폰 기기 제한 법제화 및 관련 캠페인, 운전자 교육과 같은 대안이 필요함을 제시하였다. 본 연구결과를 기반으로 사고장소의 위치정보를 추가적으로 통합하여 GIS공간 클러스터링으로 발전시킨다면 안전향상대책 시행의 우선순위 지역을 선정하는 근거로도 본 연구의 활용가치가 높을 것이다.

Qualifying information on the deaths and serious injuries caused by road traffic in five Brazilian capitals using record linkage

IntroductionRoad traffic crashes (RTCs) represent an public health problem, accounting for 1.2 million deaths per year. However, these figures may be even greater if health databases are linked to police records.
 Objectives and ApproachMeasure the numbers of deaths and seriously injured victims and to estimate the percentage of corrections of the underlying cause of death, cause of hospitalization, and injury severity of the victims in five representative state capitals of the five macro-regions of Brazil. This was cross-sectional, study using the Hospital Information System, Mortality Information System and Police Road Traffic database. The RecLink III was used for the record linkage by identifying true pairs to calculate the correction percentage of the underlying cause of death, cause of hospitalization, and injury severity of the victims in the traffic records.
 ResultsChange in the diagnosis of hospitalization in the Hospital Information System with percentage of correction of the cause at 24.4% for Belo Horizonte, 96.9% for Campo Grande, 100.0% for Palmas, and 33.2% for Teresina. The correction of the underlying cause of death in the Mortality Information System were 29.9%, 11.9%, 4.2%, and 33.5% for Belo Horizonte, Campo Grande, Curitiba, and Teresina, respectively. Change in the classification of injury severity was observed with an overall percentage of 100.0% for Belo Horizonte and Teresina, 48.0% for Campo Grande, and 51.4% for Palmas.
 Conclusion/ImplicationsThe results showed the importance of the record linkage between health and police databases for the qualification of the RTCs and the victims in the five capital cities studied. This implies that deaths and serious injuries caused by RTCs are underestimated in the absence of integration of databases that record

Automatic incident classification for large-scale traffic data by adaptive boosting SVM

Modern cities experience heavy traffic flows and congestions regularly across space and time. Monitoring traffic situations becomes an important challenge for the Traffic Control and Surveillance Systems (TCSS). In advanced TCSS, it is helpful to automatically detect and classify different traffic incidents such as severity of congestion, abnormal driving pattern, abrupt or illegal stop on road, etc. Although most TCSS are equipped with basic incident detection algorithms, they are however crude to be really useful as an automated tool for further classification. In literature, there is a lack of research for Automated Incident Classification (AIC). Therefore, a novel AIC method is proposed in this paper to tackle such challenges. In the proposed method, traffic signals are firstly extracted from captured videos and converted as spatial-temporal (ST) signals. Based on the characteristics of the ST signals, a set of realistic simulation data are generated to construct an extended large-scale traffic database to cover a variety of traffic situations. Next, a local mean filter is introduced to suppress the effect of noise and extract significant features from the ST signals. The extracted features are then associated with various types of traffic data: one normal type (inliers) and multiple abnormal types (outliers). For the classification, an adaptive boosting classifier is trained to detect outliers in traffic data automatically. Further, a Support Vector Machine (SVM) based method is adopted to train the model for identifying the categories of outliers. In short, this hybrid approach is called an Adaptive Boosting Support Vector Machines (AB-SVM) method. Experimental results show that the proposed AB-SVM method achieves a satisfied result with more than 92% classification accuracy on average.

A High Resolution Spatiotemporal Model for In-Vehicle Black Carbon Exposure: Quantifying the In-Vehicle Exposure Reduction Due to the Euro 5 Particulate Matter Standard Legislation

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as “microscopic land-use regression” (µLUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010–2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The µLUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers.

Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity

Safety Performance Functions (SPFs) provide a basis for identifying locations where countermeasures can be effective. While SPFs in the Highway Safety Manual (HSM) were calibrated based on data from select states, calibration factors can be developed to localize SPFs to other states. Calibration factors typically provide a coarse adjustment—time and space stationarity of associations between crash frequencies and various factors is still assumed, implying that the SPF functional form is transferable. However, with increasing availability of statewide geo-referenced safety data, new spatial analysis methods, and increasing computational power, it is possible to relax the stationarity assumption. Specifically, to address spatial heterogeneity in SPFs, this study proposes relaxing SPFs (referring to them as Localized SPFs (L-SPFs)) that can be developed by using sophisticated geo-spatial modeling techniques that allow correlates of crash frequencies to vary in space. For demonstration, a 2013 geo-referenced freeway crash and traffic database from Virginia is used. As a potential methodological alternative, crash frequencies are predicted by estimating Geographically Weighted Negative Binomial Regressions. This model significantly outperforms the traditional negative binomial model in terms of model goodness-of-fit, providing a better and fuller understanding of spatial variations in modeled relationships. Our study results uncover significant spatial variations in parameter estimates for Annual Average Daily Traffic (AADT) and segment length. Ignoring such variations can result in prediction errors. The results indicate low transferability of a single statewide SPF highlighting the importance of developing L-SPFs. From a practical standpoint, L-SPFs can better predict crash frequencies and support prioritizing safety improvements in specific locations.

Impact of improving vehicle front design on the burden of pedestrian injuries in Germany, the United States, and India

ABSTRACTObjective: European car design regulations and New Car Assessment Program (NCAP) ratings have led to reductions in pedestrian injuries. The aim of this study was to evaluate the impact of improving vehicle front design on mortality and morbidity due to pedestrian injuries in a European country (Germany) and 2 countries (the United States and India) that do not have pedestrian-focused NCAP testing or design regulations.Methods: We used data from the International Road Traffic and Accident Database and the Global Burden of Disease project to estimate baseline pedestrian deaths and nonfatal injuries in each country in 2013. The effect of improved passenger car star ratings on probability of pedestrian injury was based on recent evaluations of pedestrian crash data from Germany. The effect of improved heavy motor vehicle (HMV) front end design on pedestrian injuries was based on estimates reported by simulation studies. We used burden of disease methods to estimate population health loss by combining the burden of morbidity and mortality in disability-adjusted life years (DALYs) lost.Results: Extrapolating from evaluations in Germany suggests that improving front end design of cars can potentially reduce the burden of pedestrian injuries due to cars by up to 24% in the United States and 41% in India. In Germany, where cars comply with the United Nations regulation on pedestrian safety, additional improvements would have led to a 1% reduction. Similarly, improved HMV design would reduce DALYs lost by pedestrian victims hit by HMVs by 20% in each country. Overall, improved vehicle design would reduce DALYs lost to road traffic injuries (RTIs) by 0.8% in Germany, 4.1% in the United States, and 6.7% in India.Conclusions: Recent evaluations show a strong correlation between Euro NCAP pedestrian scores and real-life pedestrian injuries, suggesting that improved car front end design in Europe has led to substantial reductions in pedestrian injuries. Although the United States has fewer pedestrian crashes, it would nevertheless benefit substantially by adopting similar regulations and instituting pedestrian NCAP testing. The maximum benefit would be realized in low- and middle-income countries like India that have a high proportion of pedestrian crashes. Though crash avoidance technologies are being developed to protect pedestrians, supplemental protection through design regulations may significantly improve injury countermeasures for vulnerable road users.

Qualifying information on deaths and serious injuries caused by road traffic in five Brazilian capitals using record linkage

IntroductionRoad traffic crashes (RTC) are an important public health problem, accounting for 1.2 million deaths per year worldwide. In Brazil, approximately 40,000 deaths caused by RTC occur every year, with different trends in the Federal Units. However, these figures may be even greater if health databases are linked to police records. In addition, the linkage procedure would make it possible to qualify information from the health and police databases, improving the quality of the data regarding underlying cause of death, cause of injury in hospital records, and injury severity. ObjectiveThis study linked different data sources to measure the numbers of deaths and serious injuries and to estimate the percentage of corrections regarding the underlying cause of death, cause of injury, and the severity injury in victims in matched pairs from record linkage in five representative state capitals of the five macro-regions of Brazil. MethodsThis cross-sectional, population-based study used data from the Hospital Information System (HIS), Mortality Information System (MIS), and Police Road Traffic database of Belo Horizonte, Campo Grande, Curitiba, Palmas, and Teresina, for the year 2013 for Teresina, and 2012 for the other capitals. RecLink III was used to perform probabilistic record linkage by identifying matched pairs to calculate the global correction percentage of the underlying cause of death, the circumstance that caused the road traffic injury, and the injury severity of the victims in the police database. ResultsThere was a change in the cause of injury in the HIS, with an overall percentage of correction estimated at 24.4% for Belo Horizonte, 96.9% for Campo Grande, 100.0% for Palmas, and 33.2% for Teresina. The overall percentages of correction of the underlying cause of death in the MIS were 29.9%, 11.9%, 4.2%, and 33.5% for Belo Horizonte, Campo Grande, Curitiba, and Teresina, respectively. The correction of the classification of injury severity in police database were 100.0% for Belo Horizonte and Teresina, 48.0% for Campo Grande, and 51.4% for Palmas after linkage with hospital database. The linkage between mortality and police database found a percentage of correction of 29.5%, 52.3%, 4.4%, 74.3 and 72.9% for Belo Horizonte, Campo Grande, Palmas, Curitiba and Teresina, respectively in the police records. ConclusionsThe results showed the importance of linking records of the health and police databases for estimating the quality of data on road traffic injuries and the victims in the five capital cities studied. The true causes of death and degrees of severity of the injuries caused by RTC are underestimated in the absence of integration of health and police databases. Thus, it is necessary to define national rules and standards of integration between health and traffic databases in national and state levels in Brazil.

Integrasi Sistem Data Kecelakaan Lalu Lintas di Kabupaten Jember

The rapid development of technology can help people solve their problems faced every day. One of them is the problem in transportation sector related traffic accident data often occur in Jember. Many agencies and institutions that play a role, making the existing data to be different. Traffic accident report from police and patient report in emergency room from hospital is collected from 2012 to 2014. The result of this research is a good integration traffic accident database system between police and hospital using PostgreSQL, an information system about traffic accident recording with the location can be presented in digital map using Microsoft Visual Basic 6.0, and a recommendations to improve the accuracy of existing traffic accident data.