Rule-Based Model for Traffic Accident Visualization and Analysis

An Accident Causation Analysis and Taxonomy (ACAT) model of complex industrial system from both system safety and control theory perspectives

Disaggregate model of highway accident occurrence using survival theory

On the application of near accident data to risk analysis of major accidents

Engineering ethics within accident analysis models

On-road car accidents are immensely unfortunate but quite common occurrences worldwide. Instant data-centric and informed decisions of crisis management are rarely experienced due to the absence of real-time car accident detection and severity analysis mechanisms. On this background, the current paper presents a deep learning model for car accident detection and analysis of its severity so that the crisis management activities might follow without any delay saving invaluable human lives. The existing works lack in using time-series data, the proper learning model for accurate prediction, and minimizing the time taken in post-accident scenarios for the victims to receive immediate medical help. This paper introduces the Long Short Term Memory (LSTM) model in conjunction with the Gradient Boosted Regression Trees (GBRT) technique for the determination of car accidents with different levels of severity. The proposed model works with the accelerometer and gyroscopic data collected through an application installed in the smartphones of the users inside the car. The LSTM-GBRT hybrid model is proposed to achieve higher accuracy than LSTM which deals with time-variant data. The satisfactory performance of the proposed technique has been reported and the results are extensively investigated in comparison with another hybrid technique such as LSTM with Random Forest (RF) as well. The statistics confirm the superiority of the proposed model over other parallel models in terms of several performance metrics, like Accuracy, Precision, etc.

Using severe accident analysis program MELCOR, the small break loss of coolant accident (SBLOCA) analysis model was established for a marine reactor. The release and migration of radionuclides were analyzed during a severe accident induced by SBLOCA. The analysis of the hydrogen source term release showed that the maximum hydrogen release amount was 248.567 kg, and the hydrogen release amount accounted for less than 4% of the air volume. So, there would be no danger of hydrogen explosion accidents. The research mainly focused on the behaviors of the release, the transport, the retention, and the final distribution of inert gases represented by Xe, volatile gases represented by CsI, and nonvolatile nuclides represented by Ba. The results showed that the reactor core exposed completely with a lagging by 510 s and the initial release time of nuclides was lagged by 1916 s. The release shares of Xe in the primary circuit system, the containment, and the environment were 0.013%, 0.06%, and 32.71%, respectively. Also, Ba shared 0.016%, 0.0032%, and 3.28%, respectively, and CsI shared 0.0145%, 0.0012%, and 2.845%, respectively.

In June, 1988, the Department of Public Safety (DPS) in South Carolina launched a media campaign, called "Highways or Dieways?", in an attempt to reduce motor vehicle fatalities. Deaths per vehicle mile declined during the succeeding years and the DPS attributed all of the reduction to the campaign, gleaning some 62 awards in the process. This research indicates that the DPS took credit for a decline in fatalities that resulted from other factors. Fatality rates in South Carolina were actually higher during the campaign than would have been expected from the historic association of South Carolina's rates with the national rate or those of other states in the region. The campaign actually illustrates the principle that ad campaigns alone are often ineffective in changing public health.

The Brazilian electricity sector has recorded high work-related mortality rates that have been associated with outsourcing, used to cut costs. In order to decrease the power outage time for consumers, the industry adopted the automatic circuit recloser as the technical solution. The device has hazardous implications for maintenance workers. The aim of this study was to analyze the origins and consequences of work accidents in power systems with automatic circuit recloser, using the Accident Analysis and Prevention (AAP) model. The AAP model was used to investigate two work accidents, aimed to explore the events' organizational origins. Case 1 - when changing a deenergized secondary line, a worker received a shock from the energized primary cable (13.8kV). The system reclosed three times, causing severe injury to the worker (amputation of a lower limb). Case 2 - a fatal work accident occurred during installation of a new crosshead on a partially insulated energized line. The tip of a metal cross arm section strap touched the energized secondary line and electrocuted the maintenance operator. The circuit breaker component of the automatic circuit recloser failed. The analyses revealed how business management logic can participate in the root causes of work accidents through failures in maintenance management, outsourced workforce management, and especially safety management in systems with reclosers. Decisions to adopt automation to guarantee power distribution should not overlook the risks to workers in overhead power lines or fail to acknowledge the importance of ensuring safe conditions.

Accident data analysis deals with identifying a set of conditions of accident occurrences and the importance of the corresponding implication. It is of prime importance because it gives an insight into the reasons behind the number of fatal and other major injuries. Accident data has an inherent spatial context associated with it, as the location of the accident has an important role to play in its severity. This paper aims at categorizing and analyzing the accident data and drawing some meaningful inferences, that are implicit to the data. A spatial decision tree based approach has been used and implemented to draw some useful conclusions, which are spatially relevant with the severity of the accident. The experimentation has been carried out on the accident dataset, collected from the National Highway (NH6) connecting Kharagpur and Kolkata, India. The results exhibit some latent patterns, useful for further accident management.

Though accident data have been collected across industries, they may inherently contain uncertainty of randomness and fuzziness which in turn leads to misleading interpretation of the analysis. To handle the issue of uncertainty within accident data, the present work proposes a rough set theory (RST)-based approach to provide rule-based solution to the industry to minimize the number of accidents at work. Using RST and RST-based rule generation algorithm Learning by Example Module: Version 2 (LEM2), 279 important rules are extracted from the accident data obtained from an integrated steel industry to analyze the incident outcomes (injury, near miss and property damage). The results of the proposed methodology explore some of the important findings which are useful for the industry perspective. Therefore, the RST-based approach can be effective and efficient as well because of its potential to produce good results in the presence of uncertainty in data.

Objective. To determine the association between pediatric restraint use during motor vehicle crashes and patient transports by emergency medical services (EMS) providers. Methods. Children under 16 years of age who were involved in motor vehicle crashes in Houston or Harris County, Texas, in 1997 were identified from Texas Department of Public Safety (DPS) crash data. DPS data were linked probabilistically to City of Houston Fire Department EMS data to identify whether the children were transported from the scene by EMS. Odds ratios were calculated to determine the association between restraint use and EMS transport. Logistic regression was used to control for age and injury severity in this relationship. Results. From the linked data for 1997, 1,696 children were involved in reported automobile or truck crashes to which EMS responded. Of the 1,580 children about whom information concerning restraint use was available, 1,309 (83%) were wearing some type of safety restraint and 1,342 (85%) were transported by EMS. Adjusting for injury severity, children wearing safety restraints during motor vehicle crashes were 60% (95% confidence interval 34−75%) less likely to be transported by EMS than those not wearing safety restraints during such crashes. Conclusions. The use of safety restraints during motor vehicle crashes is associated with a reduction in the number of children transported from the scene by EMS providers. This effect is independent of age and injury severity.

In order to reduce casualties in traffic accidents, more analyses are required from the engineering and medical science viewpoints when investigating traffic accidents. A key technology in this respect is injury prediction, which reduces the response time of the emergency rescue teams through automatic collision notification, and evaluates the injury mechanisms and risks. In this study, research questions are clarified by studying the factors that influence side impact crashes in traffic accidents in Japan, and determining significant factors that reduce accident casualties by constructing an injury prediction method. In this study, statistical accident analysis is applied to a combination of data obtained from the Institute for the Traffic Accident Research and Data Analysis (ITARDA) in-depth accident investigation database (Micro Data) and ITARDA Macro Data which is the official database for traffic accidents that occur throughout Japan. First, 73 nearside and 108 farside crashes are selected from Micro Data. Second, an object variable and 22 explanatory variables from the accident information are selected and categorized. The object variable is the maximum of AIS (MAIS). Third, an ordinal logistic regression analysis is performed to construct the injury prediction models using these variables. Finally, a comparison of the obtained results with Macro Data validates these models, and the outlier cases show extraordinary accidents. The results show that the significant factors are delta-V, damage grade, and striking vehicle weight for nearside crashes; and delta-V, vehicle category, lap zone-vertical, and seat belt use for farside crashes. Thus, these two types of crashes have different factors. The MAIS predictive accuracy rate is approximately 75, and the judgment rate of fatal and serious injuries (MAIS 3+) is approximately 90 %. The prediction models can correspond to Macro Data. The accident conditions arising from ±1 MAIS residual exhibit several characteristics. Eight cases with a large degree of error in nearside crashes involved five cases of SUV or truck side impact crashes with serious chest injuries. In farside crashes, 12 cases with a large degree of error included four unbelted drivers with serious head or neck injuries. These cases indicate that these extraordinary accidents have additional factors that influence injuries. At present, there is no published research on the side impact prediction method using Japanese Micro Data and Macro Data. In the United States, several injury prediction methodologies have been studied. However, only a few studies refer to accidents with residuals. In conclusion, this study shows the results of the proposed injury prediction methods and significant factors in side impact crashes by using data on traffic accidents in Japan. These prediction models can correspond to documented accident data. Furthermore, the residual analysis conducted indicates characteristic accident factors such as SUV side impacts for nearside crashes and unbelted drivers for farside crashes. Further investigations into these outlier accidents would prove beneficial in reducing traffic accident casualties in Japan.

A comprehensive analysis method of urban rail transit operation accidents and safety management strategies based on text big data

Public safety has come a long way in the past few years in terms of technological advancements, and technology is increasingly being used by public safety departments on college campuses. Some of the technology has greatly assisted departments as we now have alarms, surveillance cameras, card access, and other types of technology that make it easier to keep campuses secure. This technology helps to prevent crimes but can also help in identifying individuals of interest when any incidents occur.

Objectives Shared e-scooter service has been offered by various operators in Türkiye since 2019. The use of private and shared e-scooters is increasing, especially in large cities. This raises social concerns about the dangers e-scooters pose regarding traffic safety and injuries requiring medical attention. This study aims to investigate the accidents related to e-scooters in Türkiye to determine the contributing factors and accident characteristics. Methods In this study, accident reports (collision reports) for 780 e-scooter collisions that occurred in 2021 in Türkiye were examined, and 771 accidents were included. Accident data were obtained from the Traffic Department of the Ministry of Interior, General Directorate of Security. Descriptive statistics of the factors affecting e-scooter accidents are presented to determine the relationship and differences; chi-square tests, independent samples t-tests, one-way analysis of variance, and binary logistic regression methods were used. Results Male e-scooter riders are involved in crashes and injured approximately 4 times more often than female riders. The average age for men injured in e-scooter accidents is 30.4, and the mean age for women is 27.2. For both males and females, most injuries occurred in the 15 to 20 age group. Riders under the age of 18 constitute a significant proportion of the accidents (32.5%). Most e-scooter accidents occur on Mondays and during the month of August. Most accidents occurred between 12:00 p.m. and 1:59 p.m. (15.7%) and between 4:00 p.m. and 5:59 p.m. (15.7%), mainly during the daytime. About half of the accidents occurred at intersections. In 10.5% of accidents, the accident occurred at a pedestrian crossing. Approximately one-fifth of the accidents involved falls, and the most common type of collision was a side collision (44.2%). The binary logistic regression model showed that multivehicle accidents occur more often at intersections and during busy traffic hours. Single-vehicle accidents are more common on concrete roads and stone block roads. Conclusions Deaths and injuries caused by road traffic accidents are a public health problem in Türkiye and constitute a significant health burden. If necessary precautions are not taken, this burden is likely to increase. We hope that the findings from this study will help reduce e-scooter accidents in Türkiye.