An algorithm for assessing the risk of traffic accident

INTRODUCTION: This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area. METHOD: The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm. RESULTS: The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis. IMPACT ON INDUSTRY: The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.     

西部大开发要加强生态环境保护和建设

在实施西部大开发进程中,对原本生态环境十分脆弱的现状必须予以重视,切实加强生态环境保护与建设。     

突发性环境污染事故应急监测准备与响应

突发性环境污染事故具有不确定性，因此要求应急监测准备工作常备不懈；应急监测准备和响应贯穿于事前、事发、事后几个阶段，包括日常准备、应急监测、结果发布、跟踪监测、经验总结等主要环节。     

Investigation of seat belt use among the drivers of different education levels

Seat belt use habit has been investigated according to the education level of drivers. Copies of a questionnaire were distributed to 1000 participants of four different education levels. Factors such as seat belt usage habit, restricting factors and crash data have been investigated. Data have been analyzed with SPSS 15.0 software. Increased level of education leads to increased seat belt usage, lower numbers of crashes and crash severities. The factors restricting seat belt use are lack of habit, discomfort and short distance driving. The use of precaution signal and increases in comfort can increase seat belt usage. The primary and high school education on traffic safety and seat belt usage has been serving the purpose.     

Toluene decomposition performance and NOx by-product formation during a DBD-catalyst process

Characteristics of toluene decomposition and formation of nitrogen oxide (NOx) by-products were investigated in a dielectric barrier discharge (DBD) reactor with/without catalyst at room temperature and atmospheric pressure. Four kinds of metal oxides, i.e., manganese oxide (MnOx), iron oxide (FeOx), cobalt oxide (CoOx) and copper oxide (CuO), supported on Al₂O₃/nickel foam, were used as catalysts. It was found that introducing catalysts could improve toluene removal efficiency, promote decomposition of by-product ozone and enhance CO₂ selectivity. In addition, NOx was suppressed with the decrease of specific energy density (SED) and the increase of humidity, gas flow rate and toluene concentration, or catalyst introduction. Among the four kinds of catalysts, the CuO catalyst showed the best performance in NOx suppression. The MnOx catalyst exhibited the lowest concentration of O₃ and highest CO₂ selectivity but the highest concentration of NOx. A possible pathway for NOx production in DBD was discussed. The contributions of oxygen active species and hydroxyl radicals are dominant in NOx suppression.     

Assessment of Integrated Pedestrian Protection Systems with Autonomous Emergency Braking (AEB) and Passive Safety Components

Objective: Autonomous emergency braking (AEB) systems fitted to cars for pedestrians have been predicted to offer substantial benefit. On this basis, consumer rating programs—for example, the European New Car Assessment Programme (Euro NCAP)—are developing rating schemes to encourage fitment of these systems. One of the questions that needs to be answered to do this fully is how the assessment of the speed reduction offered by the AEB is integrated with the current assessment of the passive safety for mitigation of pedestrian injury. Ideally, this should be done on a benefit-related basis.

The objective of this research was to develop a benefit-based methodology for assessment of integrated pedestrian protection systems with AEB and passive safety components. The method should include weighting procedures to ensure that it represents injury patterns from accident data and replicates an independently estimated benefit of AEB.

Methods: A methodology has been developed to calculate the expected societal cost of pedestrian injuries, assuming that all pedestrians in the target population (i.e., pedestrians impacted by the front of a passenger car) are impacted by the car being assessed, taking into account the impact speed reduction offered by the car's AEB (if fitted) and the passive safety protection offered by the car's frontal structure. For rating purposes, the cost for the assessed car is normalized by comparing it to the cost calculated for a reference car.

The speed reductions measured in AEB tests are used to determine the speed at which each pedestrian in the target population will be impacted. Injury probabilities for each impact are then calculated using the results from Euro NCAP pedestrian impactor tests and injury risk curves. These injury probabilities are converted into cost using “harm”-type costs for the body regions tested. These costs are weighted and summed. Weighting factors were determined using accident data from Germany and Great Britain and an independently estimated AEB benefit. German and Great Britain versions of the methodology are available. The methodology was used to assess cars with good, average, and poor Euro NCAP pedestrian ratings, in combination with a current AEB system. The fitment of a hypothetical A-pillar airbag was also investigated.

Results: It was found that the decrease in casualty injury cost achieved by fitting an AEB system was approximately equivalent to that achieved by increasing the passive safety rating from poor to average. Because the assessment was influenced strongly by the level of head protection offered in the scuttle and windscreen area, a hypothetical A-pillar airbag showed high potential to reduce overall casualty cost.

Conclusions: A benefit-based methodology for assessment of integrated pedestrian protection systems with AEB has been developed and tested. It uses input from AEB tests and Euro NCAP passive safety tests to give an integrated assessment of the system performance, which includes consideration of effects such as the change in head impact location caused by the impact speed reduction given by the AEB.     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.     

New Methodology for an Expert-Designed Map From International Classification of Diseases (ICD) to Abbreviated Injury Scale (AIS) 3+ Severity Injury

Objective: There has been a longstanding desire for a map to convert International Classification of Diseases (ICD) injury codes to Abbreviated Injury Scale (AIS) codes to reflect the severity of those diagnoses. The Association for the Advancement of Automotive Medicine (AAAM) was tasked by European Union representatives to create a categorical map classifying diagnoses codes as serious injury (Abbreviated Injury Scale [AIS] 3+), minor/moderate injury (AIS 1/2), or indeterminate. This study's objective was to map injury-related ICD-9-CM (clinical modification) and ICD-10-CM codes to these severity categories.

Methods: Approximately 19,000 ICD codes were mapped, including injuries from the following categories: amputations, blood vessel injury, burns, crushing injury, dislocations/sprains/strains, foreign body, fractures, internal organ, nerve/spinal cord injury, intracranial, laceration, open wounds, and superficial injury/contusion. Two parallel activities were completed to create the maps: (1) An in-person expert panel and (2) an electronic survey. The panel consisted of expert users of AIS and ICD from North America, the United Kingdom, and Australia. The panel met in person for 5 days, with follow-up virtual meetings to create and revise the maps. Additional qualitative data were documented to resolve potential discrepancies in mapping. The electronic survey was completed by 95 injury coding professionals from North America, Spain, Australia, and New Zealand over 12 weeks. ICD-to-AIS maps were created for: ICD-9-CM and ICD-10-CM. Both maps indicated whether the corresponding AIS 2005/Update 2008 severity score for each ICD code was AIS 3+, 1/2, or indeterminable. Though some ICD codes could be mapped to multiple AIS codes, the maximum severity of all potentially mapped injuries determined the final severity categorization.

Results: The in-person panel consisted of 13 experts, with 11 Certified AIS specialists (CAISS) with a median of 8 years and an average of 15 years of coding experience. Consensus was reached for AIS severity categorization for all injury-related ICD codes. There were 95 survey respondents, with a median of 8 years of injury coding experience. Approximately 15 survey responses were collected per ICD code. Results from the 2 activities were compared, and any discrepancies were resolved using additional qualitative and quantitative data from the in-person panel and survey results, respectively.

Conclusions: Robust maps of ICD-9-CM and ICD-10-CM injury codes to AIS severity categories (3+ versus <3) were successfully created from an in-person panel discussion and electronic survey. These maps provide a link between the common ICD diagnostic lexicons and the AIS severity coding system and are of value to injury researchers, public health scientists, and epidemiologists using large databases without available AIS coding.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

北京市道路积雪污染及特性研究

通过2009~2012年对北京市区道路旁积雪的取样,分析了积雪中污染物的浓度和特性,并对国内外普遍关注的融雪剂污染问题及其控制进行了初步分析。得出道路积雪中+NH 4-N、TN、TP、COD、SS、Cd等污染物主要来自交通活动,氯化物主要来自氯盐融雪剂;与《地表水环境质量标准》相比,道路积雪中主要污染物为NH+4-N、TN、COD、BOD和氯化物,Cu、Zn、Pb、Cd等重金属的污染水平不高,可达到Ⅴ类水体标准;相同地点近似降水量条件下,积雪中TP、COD、SS的平均浓度高于降雨径流;北京市交通主干道及人行道上喷撒氯盐融雪剂的量高于国外道路,控制其污染主要通过源头减少使用量、使用替代融雪剂、严格融雪剂标准和使用方法、严肃责任追究、加强宣传教育等措施。