Road Traffic Events Research Articles

Possibilities to evaluate the vehicle pre-collision travel velocity in the case of a frontal impact with a fixed object, through numerical modelling

Abstract The present paper evaluates, from a physical-mathematical perspective, the pre-collision travel speed of a vehicle, in the case of a frontal impact with a rigid, fixed pole-type obstacle. The evaluation considers both energy methods and deformation coefficients, taking into account the parameters resulting from the primary investigation of the accident site. The reconstruction of such road accidents aims to determine the kinematic parameters that influence the behavior of the involved vehicle, and to allow the analysis of avoidance possibilities of such events. The developed working algorithm can be applied to solve a large number of road accident cases by changing the input data and to obtain the necessary kinematic and dynamic parameters for the accident reconstruction process. Thus, it is possible to establish the dynamics of such road traffic events, which facilitates the assessment and comparison of different conditions under study. The validation of the fore-mentioned reconstruction algorithm is performed by comparing the results obtained through numerical modelling with those determined experimentally. The relative error between the two is, as well, assessed.

Regarding the issue of the influence of factors on the braking parameters of M1 category vehicles with electric or hybrid power unit

Problem. The relevance of this work is explained by the fact that there are no works at all in the reference and normative literature on the theory and practice of forensic auto technical examination, and even more so, methodical recommendations regarding the values ​​of the braking parameters of vehicles of the M1 category with an electric power plant. Goal. The purpose of the work is to establish the factors that affect the braking parameters of M1 category vehicles with an electric or hybrid power unit. Methodology. The approaches adopted in the work to solve the set goals are based on the theory of conducting the experiment, the theory of the interaction of the pneumatic tire with the road surface, the theory of the car and the laws of theoretical mechanics. Results. On the basis of the obtained experimental data regarding the value of permanent deceleration and the time value of the increasing deceleration during emergency braking of the M1 category vehicles with an electric or hybrid power unit, the tasks will be solved necessary for conducting forensic engineering and transport examinations for specific road traffic events with the participation of M1 category vehicles with an electric or hybrid power unit. Originality. The results of the study provided an opportunity to get an idea of the influence of factors on the braking parameters of M1 category vehicles with an electric or hybrid power unit. Practical value. The obtained results can be recommended to forensic experts when writing expert opinions or expert studies.

Laporan Kasus : Pemeriksaan Forensik Pada Kasus Kecelakaan Lalu Lintas

Background: A traffic accident is an unexpected and unwanted road traffic event which is difficult to predict when and where it will occur, involving at least one vehicle with other road users or due to negligence in driving a vehicle, be it a car or motorcycle which can cause death, disability, serious injury or minor injury. Within the territory of Indonesia itself, every hour has an average of at least three people who die as a result of the accident with many causal factors such as the human factor itself, which is around 61%, then the vehicle factor is 9% and environmental factors and infrastructure contribute as much as 30%.
 Case Description : We report a case of a traffic accident with multiple trauma. This traffic accident involved 2 motorbike riders who were traveling at maximum speed so that they collided with a truck that was stopped at a red light in Makassar City, South Sulawesi, on Thursday 15 June 2022 at around 01.00 WITA and caused the two motorbike riders to die. The motorbike that was traveling at high speed suddenly stopped and hit the back of the truck, as a result the body moving at the speed of the vehicle suddenly experienced a deceleration, causing multiple trauma to several regions or organs of the body. The effects on the victim's corpse varied in the form of head injuries, open wounds, and fractures to several bones.
 Conclusion : Forensic examination is very useful to determine the cause of death, mechanism of death, and nature of death. In the medical report of a traffic accident, the forensic doctor can explain his findings in the form of a post mortem et repertum.

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.

A scalable blockchain-based scheme for traffic-related data sharing in VANETs

Recent advances in wireless technology and embedded systems enable vehicles to share relevant traffic-related data to improve the transportation Quality-of-Service (QoS). However, due to the ubiquitousness of cyber-attacks, it is challenging to ensure the integrity of the data collected from cars. This paper proposes a novel architecture for road traffic events management in Vehicular Ad hoc NETworks (VANETs) relying on a permissioned blockchain. It also introduces the concept of micro-transactions to minimize communication and storage overhead. Through simulations, a rigorous performance evaluation of the proposed approach was conducted, and the micro-transactions effectiveness was assessed. In addition, a comparison with close works in the literature was performed. The proposed scheme ensures road traffic records integrity and traceability, and simulation results on the considered scenarios showed good performance.

A review of road traffic accidents reconstruction methods and their limitations with respect to the national legal frameworks

Road traffic accidents are the main issue in terms of public safety among Romania and Europe. Reconstructionists aim to ease their work and to offer results as exact as possible, by using a variety of reconstruction methods, from numerical modelling to software simulations of the collisions in which vehicles are involved. Depending on the conditions of a road traffic event, several methodologies can be used, with proper validation. However, the national legal frameworks may be considered as barriers in using a large variety of reconstruction methods. Therefore, this area of research should be further developed, in order to enlarge experts’ choice, especially in the context of rapid development of the automobile industry.

ОЦЕНКА ТРАВМЫ ДЛЯ ПРОГНОЗИРОВАНИЯ ВНУТРИБОЛЬНИЧНОЙ СМЕРТНОСТИ И ИСХОДА ТРАВМЫ ПАЦИЕНТА

The present study is a retrospective, descriptive and analytical study that was conducted on 300 patients with multiple trauma, aged 5 to 75 years. Inclusion criteria included the presence of multiple injuries caused by road traffic accidents and other events. The exclusion criterion was injury to only one area of the body. The causes of injury were divided into four categories: 1) injuries from road traffic crashes, 2) injuries caused by falls from a height, 3) injuries caused by assault, and 4) injuries caused by road traffic accidents with pedestrians. The most common cause of hospitalization due to injury in men and women was road traffic injuries, and there was a significant correlation between injury types and gender (p = 0.001). The results showed that 28 injured people (9.3%) died in hospital, with the largest percentage of deaths occurring in the 21 to 33 age group. The results were based on the number of people who died: 7 (25.0%) of them had penetrating injuries, and 21 (75.0%) had blunt injuries. Data analysis showed a significant correlation between mortality and type of injury (p = 0.004). Key words: Key words: trauma, medical assessment, organ damage, patient, trauma outcome, in-hospital mortality

Epidemiology of Road Traffic Accidents in Nepal from 2009/10 to 2019/20: A 10 Year Study.

Road traffic accidents are the leading cause of deaths worldwide among children and young adults aged between 5 to 29 years. The risk of road traffic accident-related death is higher in low-income countries. Thus, we aim to evaluate the epidemiology of road traffic accidents in Nepal during the fiscal years from 2009/10 to 2019/20. This study is a secondary data analysis of the published police records publicly available on the official webpage of Nepal Police. A study was carried out after written permission from Nepal Police. The data obtained were analysed using Microsoft Excel-13. Analysing available data about reported road traffic accidents events showed an increasing trend in road traffic accidents from the fiscal years 2011/12 till 2019/20, with a steeper increase in the number of road traffic accidents in fiscal years 2017/18 and 2019/20. In fiscal years 2009/10, 11,747 vehicles collided; which rose to 25,788 in the fiscal years 2019/20. More cases of road traffic accidents were reported to occur from 12 noon-6 pm in a day. Among reported road traffic accidents commonest predispositions behind road traffic accidents was the negligence of driver followed by over speed. Among those individuals who died due to road traffic accidents, most of them were male. We found an increasing trend of road traffic accidents, vehicle collisions, and injuries in Nepal over the past ten years especially among two-wheelers and four-wheelers like the car, bus, and autorickshaw.

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning.

Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

Heavy truck driving attitude and behavior contributing to road traffic accidents

According to the World Health Organisation, road traffic accidents are the leading cause of most emergency admissions in hospitals worldwide. Many people have indicated that road traffic collisions are a significant contributor to DALYs lost. For example, according to the World Health Survey in 2010, road traffic events have been reported as the ninth most common cause of injury-modified life years (DALYs) lost across all age and gender classes. The World Health Organization (WHO) reports that one out of every 10 deaths in the world are caused by road traffic accidents. More than 200,000 people are killed in the United States each year from different reasons. The number of disabled people related to this disease will grow even further by 2020. There are nearly 1.5 million out of about 47 million people who are wounded in the highways every year in the United States Drivers were found to be more likely to saddle up for the journey as they set out on main roads. Car seat-belts can make driving more safe by reducing the effect made on the drivers and the passengers in the car. The driver's mood about the problem of distracted driving can be a critical factor in avoiding road traffic accidents. To be safe, drivers should have at least one restful day a week, should not drive for more than eight hours a day, should not travel more than 800 kilometers a day, and will need to have routine checks so that everything is going well and is running properly. Traffic laws, traffic signs, other vehicles, and many other items when on the road; drivers should not eat any nourishment, they should not consume any quantity of alcoholic beverages, they should not use electronic cigarettes, they should not put their mobile phones on.

Iktishaf: a Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine Learning

Road transportation is the backbone of modern economies despite costing annually millions of human deaths and injuries and trillions of dollars. Twitter is a powerful information source for transportation but major challenges in big data management and Twitter analytics need addressing. We propose Iktishaf, developed over Apache Spark, a big data tool for traffic-related event detection from Twitter data in Saudi Arabia. It uses three machine learning (ML) algorithms to build multiple classifiers to detect eight event types. The classifiers are validated using widely used criteria and against external sources. Iktishaf Stemmer improves text preprocessing, event detection and feature space. Using 2.5 million tweets, we detect events without prior knowledge including the KSA national day, a fire in Riyadh, rains in Makkah and Taif, and the inauguration of Al-Haramain train. We are not aware of any work, apart from ours, that uses big data technologies for event detection of road traffic events from tweets in Arabic. Iktishaf provides hybrid human-ML methods and is a prime example of bringing together AI theory, big data processing, and human cognition applied to a practical problem.

Application of the Intermittency Ratio Metric for the Classification of Urban Sites Based on Road Traffic Noise Events.

Human hearing adapts to steady signals, but remains very sensitive to fluctuations as well as to prominent, salient noise events. The higher these fluctuations are, the more annoying a sound is possibly perceived. To quantify these fluctuations, descriptors have been proposed in the literature and, among these, the intermittency ratio (IR) has been formulated to quantify the eventfulness of an exposure from transportation noise. This paper deals with the application of IR to urban road traffic noise data, collected in terms of 1 s A-weighted sound pressure level (SPL), without being attended, monitored continuously for 24 h in 90 sites in the city of Milan. IR was computed on each hourly data of the 251 time series available (lasting 24 h each), including different types of roads, from motorways to local roads with low traffic flow. The obtained hourly IR values have been processed by clustering methods to extract the most significant temporal pattern features of IR in order to figure out a criterion to classify the urban sites taking into account road traffic noise events, which potentially increase annoyance. Two clusters have been obtained and a “non-acoustic” parameter x, determined by combination of the traffic flow rate in three hourly intervals, has allowed to associate each site with the cluster membership. The described methodology could be fruitfully applied on road traffic noise data in other cities. Moreover, to have a more detailed characterization of noise exposure, IR, describing SPL short-term temporal variations, has proved to be a useful supplementary metric accompanying LAeq, which is limited to measure the energy content of the noise exposure.

Research on visual analysis technology and application of urban road data

Many cities use the current wealth of multi-source data to create intelligent transportation systems, which visually process road data so that traffic consumers and traffic managers can better understand urban traffic conditions. Due to the heterogeneity, complexity and mass of these data, it is not easy to conduct effective analysis on them, and it is often necessary to integrate human perception in the analysis process, so as to cause extensive application of visualization. In this paper, we first briefly introduce the pre-processing of urban trajectory data, then systematically analyze the visualization of two data types of road traffic flow and traffic events, and finally briefly summarize the research trend of traffic visualization in recent years and put forward corresponding challenges. The results show that the early research focused on the visualization of road flow, including the main arrow diagram, Mosaic diagram, track wall, etc. With the deepening of visual analysis, the analysis of traffic flow increases to the analysis of traffic events. In recent years, there has been a new trend of mining and utilizing the attributes or semantics of traffic trajectories or traffic events. The research on traffic data visualization has been greatly expanded in breadth and depth.

Analytical model assessing the effect of increased traffic flow intensities on the road administration, maintenance and lifetime

Abstract The aim of this paper is to introduce the reader to the construction of an analytical model assessing the effect of increased traffic intensities on extraordinary road traffic events on the amount of expenditures that the road administrators are forced to spend on the extra maintenance and reshaping these alternative diversion routes into the initial technical conditions. The computational mechanism of this model is based on the one hand on parameters of diversion routes temporarily transferring increased traffic intensities (e.g. motorway traffic at closure), on the other hand on approved cost databases for the assessment of road constructions and other regulations concerning the design of road maintenance and repair. In the final part of the paper the applicability of this model in practice is assessed and some negatives that are not included in the final calculation when determining the total costs of the road administrator are mentioned.

Crowdsourced Traffic Event Detection and Source Reputation Assessment Using Smart Contracts.

Real-time data about various traffic events and conditions—offences, accidents, dangerous driving, or dangerous road conditions—is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we design and demonstrate a road traffic event detection and source reputation assessment system for unreliable data sources. Special care is taken to adapt the system for operation in decentralized mode, using smart contracts on a Turing-complete blockchain platform, eliminating single authority over such systems and increasing resilience to institutional data manipulation. The proposed solution was evaluated using both a synthetic traffic event dataset and a dataset gathered from real users, using a traffic event reporting mobile application in a professional driving simulator used for driver training. The results show the proposed system can accurately detect a range of manipulative and misreporting behaviors, and quickly converges to the final trust score even in a resource-constrained environment of a blockchain platform virtual machine.

Renewal of driving license in India and glaucoma: A study of prevalent practice and its lacunae.

Purpose:Glaucoma affects different aspects of vision including visual field. This prospective observational study aims to collect details of driving license (DL) renewal procedure (in an urban metro in India) among patients with diagnosed glaucoma and the method of reporting of vision-related requirements during renewal.Methods:One-hundred patients with diagnosed glaucoma above 40 years, having valid DL (with at least one renewal cycle), were included. Patients with other ocular comorbidities were excluded. Driving Habits Questionnaire and a questionnaire about license renewal were administered. Driving eligibility was compared to international guidelines.Results:Study population included patients with 69% early, 29% moderate, and 2% advanced glaucoma. Sixteen percent of patients had stopped driving. Legal license renewal procedure was bypassed by 45%. Form-1 was not submitted by 43% and 49% did not submit Form-1A at the time of renewal. Only 7.01% mentioned about glaucoma in the self-declaration form. None were asked about their visual field during renewal. Among 61 patients who submitted a medical certificate, the undersigning doctor was an ophthalmologist in only six patients. Thirty percent patients with valid Indian DL would not have satisfied International College of Ophthalmologists guidelines. Driving difficulties were experienced by 44%, more so in advanced glaucoma (F (1, 82) = 22.12, P < 0.001).Conclusion:Vision-related testing at the time of renewal of DL is inadequate in India. Chronic eye diseases such as glaucoma are commonly not self-declared or detected at pre-renewal testing. Clear-cut guidelines about visual requirements and implementation are required to prevent road traffic events because of vision-related errors.

DYNAMICS OF CHANGES DURING THE IMPLEMENTATION OF POLICE INTERVENTIONS IN THE MAŁOPOLSKIE VOIVODSHIP

The article focuses on road traffic events, and more specifically on the time of arrival of police units to fatal road events in the Małopolskie voivodship and the possibilities of improving the process of event handling resulting from the analysis of statistical data and interviews. Data on the arrival times of police privileged vehicles were taken from the Provincial Police Headquarters in Krakow.The analysis shows that the development of urban agglomerations in the Małopolskie voivodeship requires constant improvements under the safety account, which will affect the process of saving human life or health by institutions designated for this purpose.

EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings.

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.

Text Based Approach For Similar Traffic Incident Detection from Twitter

Microblog has been used as an information source to detect real-world event. Several related studies retrieved road traffic event based on textual content. Not only detect traffic incident, we found that it is necessary to recognize statuses with similar traffic incident content. Better representation of traffic information will help the handling of traffic incident by related parties. This study proposes text-based approach for identification of similar traffic incident from twitter posts. The proposed approach performs traffic incident information extraction and calculates information’s weight based on textual similarity upon traffic incident information gained. We evaluate the proposed method by using a traffic incident information retrieval system. We used Indonesian language corpus contains traffic incident tweets data. Best average f-measure 70% was achieved by retrieval system that tested using Jaccard coefficient. Therefore text matching such as Jaccard coefficient is more suitable to be implemented in very short text document such as extracted tweet document. The experiment result gives the conclusion that the proposed approach can be implemented for identification of similar traffic incident information from Twitter. &#x0D;

Improvement of the process of handling participants in road traf-fic events by the police in the Opole voivodship

The article presents the process of handling participants in road traffic accidents by the police privileged vehicles. The following research tools were used to conduct the study: theoretical (analysis, synthesis, generalization, comparison) and practical (method of ordering, observation, direct interview). The analysis of the number of road accidents in the Opole Voivodship between 2010 and July 2016 with particular focus on the handling of road accidents under the current research was conducted. The improvements to this process have been proposed as a result of the analysis.