scholarly journals A Cognitive Contemplation of Road Accident Predicton Through Deep Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1020-1027
Keyword(s):  
Social Activity ◽  
Road Accident ◽  
Neural Systems ◽  
Control Approach ◽  
Accident Prediction ◽  
Vehicle Accidents ◽  
The Road ◽  
Car Accidents ◽  
Traffic Security ◽  
Prevention Methods

The research based on the vehicle accidents step to collect and structure a progressive secure transportation unfortunately vehicle crashes were unavoidable. The accident prediction related with the risky environment data collection and arrangements based on the high priority of reality of accidents. The social activity and roadway structures are useful in the progression of traffic security control approach. We believe that to secure the best possible setback decline impacts with limited budgetary resources, it is basic that measures be established on coherent and objective studies of the explanations behind mishaps and seriousness of wounds. A survey based on the different algorithms able to predict the road accidents prevention methods. This paper demonstrates a couple of models to predict the reality of harm that occurred in the midst of car accidents using three artificial intelligent approaches (AI). The proposed scheme contributes a neural systems prepared utilizing choice trees and fluffy c implies bunching strategy for division.

scholarly journals A Parametric Model for Accident Prediction Along Ado Ekiti – Ikole Ekiti Road, Ekiti State, Nigeria

2020 ◽  
Vol 5 (8) ◽  
pp. 980-985
Author(s):  
Olumuyiwa Samson Aderinola
Keyword(s):  
Prediction Models ◽  
Parametric Model ◽  
Road Accident ◽  
State University ◽  
Pavement Condition ◽  
Accident Prediction ◽  
The Road ◽  
Reconnaissance Survey ◽  
Proper Design ◽  
Frequency Occurrence

Road Accident Prediction Models have been used in different countries as a useful tool by road engineers and planners to predict the safety levels of roads, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. The research looked into developing a parametric model for predicting accidents at specific locations along Ado-Ekiti to Ikole-Ekiti road. The reconnaissance survey of the road and the identified accident vulnerable points along the road was carried out and the factors aiding the occurrence of accidents were isolated  as Spot speed [S],  Pavement condition [P], Condition of shoulder [C], Width of the road [W], Elevation(super)/cambering [E], Gradient [G] and Accident Vulnerability [AV] which form an acronym SPCWEG-AV. The spot speed in each of the locations was gotten by measuring a 60m length and noting the time vehicles covered the distance. The pavement and shoulder conditions were evaluated to determine their conditions. The width of the road, the elevation (super)/cambering and the gradient (horizontal) were measured using tape, twine and plumb. When the analyzed data from the investigated factors from the field were imputed into SPCWEG-AV Rating system and Weights, the index (which is a multiplication of the rating and weight) of each of the parameters was got and the addition of these indices produced what is called Total SPCWEG-AV Index (T.SPCWEG-AV.I) which defines the degree of accident vulnerability of the point in question. The higher the T.SPCWEG-AV.I is, the more vulnerable the location is. The results showed ten accident prone areas. They are Federal Government College, Ikole-Ekiti (Ch 0+000), NNPC, Ikole-Ekiti (Ch 3+200), Olokonla, Ikole-Ekiti (Ch 7+000), The Nigeria Police station, Oye-Ekiti (Ch 23+2000), Federal University, Oye-Ekiti (Ch 25+600), Ifaki-Ekiti (Ch 35+400), Iworoko-Ekiti (Ch 52+100), Iworoko market (Ch 53+100), Ekiti State  University, Iworoko-Ekiti (Ch 62+750), Ilasa-Ekiti (Ch 64+800). Federal University, Oye Ekiti, Oye Ekiti (Ch 25+600) and Ilasa-Ekiti (Ch 64+800) have the highest number of accidents each having 24 and 22 and also has highest T.SPCWEG—AV.I of 71 and 70 respectively and other points show similar pattern. It is therefore, reasonable to conclude that the parametric model can replicate and predict the occurrence of accidents along Ado-Ekiti to Ikole-Ekiti road and other roads with similar features. It is recommended that the results of researches should be put to use and that agencies in charge of roads should ensure proper design, supervision and construction and to make sure the roads are properly maintained. Road Accident Prediction Models have been used in different countries as a useful tool by road engineers and planners to predict the safety levels of roads, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. The research looked into developing a parametric model for predicting accidents at specific locations along Ado-Ekiti to Ikole-Ekiti road. The reconnaissance survey of the road and the identified accident vulnerable points along the road was carried out and the factors aiding the occurrence of accidents were isolated  as Spot speed [S],  Pavement condition [P], Condition of shoulder [C], Width of the road [W], Elevation(super)/cambering [E], Gradient [G] and Accident Vulnerability [AV] which form an acronym SPCWEG-AV. The spot speed in each of the locations was gotten by measuring a 60m length and noting the time vehicles covered the distance. The pavement and shoulder conditions were evaluated to determine their conditions. The width of the road, the elevation (super)/cambering and the gradient (horizontal) were measured using tape, twine and plumb. When the analyzed data from the investigated factors from the field were imputed into SPCWEG-AV Rating system and Weights, the index (which is a multiplication of the rating and weight) of each of the parameters was got and the addition of these indices produced what is called Total SPCWEG-AV Index (T.SPCWEG-AV.I) which defines the degree of accident vulnerability of the point in question. The higher the T.SPCWEG-AV.I is, the more vulnerable the location is. The results showed ten accident prone areas. They are Federal Government College, Ikole-Ekiti (Ch 0+000), NNPC, Ikole-Ekiti (Ch 3+200), Olokonla, Ikole-Ekiti (Ch 7+000), The Nigeria Police station, Oye-Ekiti (Ch 23+2000), Federal University, Oye-Ekiti (Ch 25+600), Ifaki-Ekiti (Ch 35+400), Iworoko-Ekiti (Ch 52+100), Iworoko market (Ch 53+100), Ekiti State  University, Iworoko-Ekiti (Ch 62+750), Ilasa-Ekiti (Ch 64+800). Federal University, Oye Ekiti, Oye Ekiti (Ch 25+600) and Ilasa-Ekiti (Ch 64+800) have the highest number of accidents each having 24 and 22 and also has highest T.SPCWEG—AV.I of 71 and 70 respectively and other points show similar pattern. It is therefore, reasonable to conclude that the parametric model can replicate and predict the occurrence of accidents along Ado-Ekiti to Ikole-Ekiti road and other roads with similar features. It is recommended that the results of researches should be put to use and that agencies in charge of roads should ensure proper design, supervision and construction and to make sure the roads are properly maintained.

Evaluation Of Major Causes Of Road Accidents Along North–East Highway, Nigeria

2015 ◽  
Vol 73 (4) ◽  
Author(s):  
Arafat Suleiman Yero ◽  
Tijanni Y Ahmed ◽  
Mohd Rosli Hainin
Keyword(s):  
Road Traffic ◽  
Road Accident ◽  
Eastern Region ◽  
Road Maintenance ◽  
Vehicle Accidents ◽  
The Road ◽  
Goods And Services ◽  
North East ◽  
The North ◽  
Road Users

A major road link in the North-Eastern region of Nigeria is the Bauchi – Maiduguri highway that is a 425 km road that links a section of the north east region to other regions of Nigeria. The goods and services to the region are basically transported by road.  This has increased vehicular traffic that resulted in increased road accident rates over the years.  It is paramount to investigate the major causes of vehicle accidents on this highway as much has not been done to investigate accident cases on the route. The five year accident record on that route was obtained from the Federal Road Safety Corp of Nigeria,   the Nigeria Police traffic unit, and the Nigeria union of road traffic workers. The study indicated that speed violation by drivers and bad road conditions contributes greatly in the rate of accidents along the route. Hence the study recommends better road maintenance culture and more sensitization of the road users and enforcement of speed limits.

scholarly journals Predicting and Preventing Recommender System for Telangana Road Accidents

2020 ◽  
Vol 8 (6) ◽  
pp. 1353-1358
Keyword(s):  
Road Safety ◽  
Recommendation System ◽  
Similarity Measures ◽  
Road Accident ◽  
Road Accidents ◽  
Driver Behaviour ◽  
Accident Prediction ◽  
The Road ◽  
Model Recognition

Today people are suffering with road accidents in world wide. Analyzing these Road accidents are the major challenge in identifying and predicting primary features related with catastrophes. All these features are valuable for anticipatory computes to conquer road mishaps. Integrating various analytics techniques can get better model recognition and avoid road mishaps. As road safety growing quiet apprehension, speedy analytics observes all safety techniques in dynamic to spot malfunction that may signifies road mishaps on identifying key features related with road , mishaps in Telangana state. In our propose work, a framework to analyze the road mishap with classification of accidents and clustering, which analyze mishap data of Telangana stated district wise. The proposed framework describes the recommendation system for predicting road accidents. For this, classify the road accidents into fatal, major and minor. We implemented district wise data into clustering and applying enhanced k-mean algorithm. Further, implemented similarity measures to detecting the places where the severity of accidents happened and also analysing the driver behaviour analysis while accidents occur. The implementation result reveals that the road accident prediction exhibits enhance in certain areas and those areas exists in districts should be the major concern to acquire anticipatory measure to conquer the road mishaps.

Estimating Vehicle Roadside Encroachment Frequencies by Using Accident Prediction Models

1997 ◽  
Vol 1599 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Shaw-Pin Miaou
Keyword(s):  
Data Collection ◽  
Prediction Models ◽  
Technical Difficulty ◽  
Road Accident ◽  
Roadside Safety ◽  
Accident Prediction ◽  
The Road ◽  
Run Off ◽  
Wide Range ◽  
Viable Approach

The existing data to support the development of roadside encroachment-based accident-prediction models are limited and largely outdated. Under FHWA and TRB sponsorship, several roadside safety projects have attempted to address this issue by proposing rather comprehensive data collection plans and conducting pilot data collection efforts. It is clear from these studies that the required cost for the proposed roadside field data-collection efforts will be very high. Furthermore, the validity of any field-collected roadside encroachment data may be questionable because of the technical difficulty of distinguishing intentional (or controlled) from unintentional (or uncontrolled) encroachments. A method to estimate some of the basic roadside encroachment parameters, including vehicle roadside encroachment frequency and the probability distribution of lateral extent of encroachments, using existing accident-based prediction models is proposed. The method is developed by utilizing the probabilistic relationships between a roadside encroachment event and a run-off-the-road accident event. With some assumptions, the method is capable of providing a wide range of basic encroachment parameters from conventional accident-based prediction models. To illustrate the concept and use of such a method, some basic encroachment parameters are estimated for rural two-lane undivided roads. In addition, the estimated encroachment parameters are compared with those estimated from the existing encroachment data. The illustration indicates that this method can be a viable approach to estimating basic encroachment parameters of interest and, thus, has the potential of reducing the roadside data collection cost.

scholarly journals Driving license regranting in DUI subjects: Road accident variables and predictive factors of substance use disorder

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110337
Author(s):  
Elena Beccegato ◽  
Angelo Ruggeri ◽  
Massimo Montisci ◽  
Claudio Terranova
Keyword(s):  
Substance Use ◽  
Comparison Group ◽  
Blood Alcohol Concentration ◽  
Road Accident ◽  
Ethyl Glucuronide ◽  
Binary Logistic Regression Analysis ◽  
Psychoactive Substance ◽  
Psychoactive Substances ◽  
Driving License ◽  
The Road

A comparative case study (2017–2020) was conducted to identify demographic, social, medico-legal, and toxicological variables associated with non-fatal accidents in driving under the influence (DUI) subjects. A second aim was to identify the factors predictive of substance use disorders among subjects. Drivers charged with alcohol DUI (blood alcohol concentration (BAC) > 0.5) and/or psychoactive substance DUI were included; cases included those involved in an accident while intoxicated, and the comparison group included DUI offenders negative for road accident involvement. Significance was determined by chi-square and Mann–Whitney tests. To prevent confounding effects, a multivariate binary logistic regression analysis was performed. Our sample encompassed 882 subjects (381 in the case group and 501 in the comparison group). Parameters such as psychoactive substances and BAC at the time of the road crash/DUI and the day of the week, when subjects were involved in the road accident or found DUI, resulted in significant differences ( p < 0.01) between groups. The model’s independent variables of BAC > 1.5 g/L ( p = 0.013), BAC > 2.5 g/L ( p < 0.001), and concurrent alcohol and psychoactive substance use ( p < 0.001) were independent risk factors for an accident. Smoking >20 cigarettes/day was an independent risk factor for unfitness to drive ( p < 0.01). Unfitness to drive was based primarily on ethyl glucuronide levels >30 pg/mg. Our results suggest a detailed assessment of DUI subjects with variables associated with accidents (BAC > 1.5 g/L and concurrent intake of psychoactive substances). Hair analysis, including ethylglucuronide (EtG) concentration, should be always performed. Based on our results, nicotine use should be investigated in cases of driving license regranting.

scholarly journals Striated Tire Yaw Marks—Modeling and Validation

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4309
Author(s):  
Wojciech Wach ◽  
Jakub Zębala
Keyword(s):  
Vehicle Dynamics ◽  
Linear Equations ◽  
Tire Model ◽  
Vehicle Accidents ◽  
The Road ◽  
Variable Curvature ◽  
Macro Scale ◽  
On The Road ◽  
Multi Body ◽  
Non Linear Equations

Tire yaw marks deposited on the road surface carry a lot of information of paramount importance for the analysis of vehicle accidents. They can be used: (a) in a macro-scale for establishing the vehicle’s positions and orientation as well as an estimation of the vehicle’s speed at the start of yawing; (b) in a micro-scale for inferring among others things the braking or acceleration status of the wheels from the topology of the striations forming the mark. A mathematical model of how the striations will appear has been developed. The model is universal, i.e., it applies to a tire moving along any trajectory with variable curvature, and it takes into account the forces and torques which are calculated by solving a system of non-linear equations of vehicle dynamics. It was validated in the program developed by the author, in which the vehicle is represented by a 36 degree of freedom multi-body system with the TMeasy tire model. The mark-creating model shows good compliance with experimental data. It gives a deep view of the nature of striated yaw marks’ formation and can be applied in any program for the simulation of vehicle dynamics with any level of simplification.

Phantom Brake Phenomenon in Survivors of Car Accidents

2021 ◽  
Vol 2 (3) ◽  
pp. 9-13
Author(s):  
Zack Z. Cernovsky ◽  
Milad Fattahi
Keyword(s):  
Motor Vehicle ◽  
Brief Pain Inventory ◽  
Severity Index ◽  
High Impact ◽  
Generalized Anxiety ◽  
Vehicle Accidents ◽  
Symptom Pattern ◽  
Car Accidents ◽  
And Gender ◽  
Almost All

Background: Survivors of high impact car accidents, when traveling in cars as passengers, may exhibit the phantom brake reaction. The reaction consists of involuntarily pressing the foot on the floor of the car in a reflexive attempt "to brake", even though there is no brake pedal in front of the passenger seat. This study examines the incidence and correlates of this special phenomenon. Method: De-identified data of 114 survivors (37 men, 77 women; mean age 38.6, SD=12.4) of high impact motor vehicle accidents (MVAs) were available, with their responses to the Brief Pain Inventory, Insomnia Severity Index, Rivermead Post-Concussion Symptoms Questionnaire, Subjective Neuropsychological Symptoms Scale (SNPSS), PTSD Checklist for DSM-5 (PCL-5), ratings of depression and of generalized anxiety, and 3 questionnaire measures of driving anxiety, i.e., Whetstone’s, Steiner’s, and the Driving Anxiety Questionnaire (DAQ). One item of the DAQ assesses the phantom brake phenomenon on a 4-point scale (0=No, 1=Mild, 2=Moderate, 3=Severe): this is the key variable in the present study. Results: Mild to severe forms of the phantom brake reaction were reported by 92.1% of the post-MVA patients. Significant correlations (p<0.05, 2-tailed) were found of the intensity of phantom brake reaction to the intensity of post-MVA pain (rs from 0.20 to 0.33), insomnia (r=0.40), the Rivermead post-concussion scale (r=.29), other post-concussive and whiplash symptoms as measured by the SNPSS (r=0.19), depression (r=0.30), generalized anxiety (r=0.32), and to DAQ (r=0.47) and Whetstone’s (r=0.50) measures of driving anxiety. No significant relationships were found of the phantom brake reaction to age and gender. Discussion and Conclusion: The phantom brake reaction was reported by almost all post-MVA patients and can be considered as a part of their post-MVA polytraumatic symptom pattern.

scholarly journals Nine Recommendations for the Safety on Road both of the People and the Traffic Police

2018 ◽  
Vol 1 (01) ◽  
pp. 79-85
Author(s):  
Madhur Dev Bhattarai
Keyword(s):  
Cost Effective ◽  
Road Accident ◽  
Public Hospitals ◽  
Occupational Hazards ◽  
Traffic Police ◽  
The Road ◽  
The People ◽  
On The Road ◽  
The Cost ◽  
Injured Patients

Safety of people and traffic police on road and the provision of prompt and appropriate treatment of injured persons in road accident are urgent concerns. The nine recommendations accordingly made are 1) Considering anyone who informs about or brings to the hospitals the accident victims as innocent until proved otherwise, 2) Annual payment by all vehicle owners (as per the cost of vehicles) to generate treatment fund for any road accident injured patients in the free general (not paying or private or extended health service) outdoor or emergency clinics or ward of the public hospitals irrespective of anyone’ fault in the accident (insurance or other agencies may be assigned to handle the amount deposited and reimbursement of the payments to the hospitals), 3) Implementation of helmet wearing by motorcycle riders and pillion riders in motorcycles, 4) Stricter fine for hazardous traffic offenses, 5) Drivers of the larger vehicles should not automatically be held responsible for any accidents involving other smaller vehicles (to prevent smaller vehicles and motorcycles to drive recklessly), 6) Drivers should not be just held responsible to bear health expenses of injured patients (which is much more than the compensation required in the event of death of injured persons); this is to encourage drivers to take injured persons immediately to hospitals and prevent inclination to allow their deaths indirectly or directly; the drivers should be proportionately fined or punished as per the traffic regulations if they are found to be negligent, 7) Safe and visible platform for the traffic police to stay on the road, 8) Provision of cost-effective respirators for traffic police and traffic supervisors, and 9) Compensation for occupational hazards to the traffic police and field traffic supervisors by distributing to them adequate proportion (e.g. one-third to one-half) of the fund collected by stricter fine paid for the hazardous traffic offences. Provision of various allowances, including for hazards, and benefits is a common practice in the country. Compensation for the occupational hazards of the traffic police provides incentives to and motivates them to remain vigilant about hazardous traffic offenses day and night everywhere and, thus, is essential for the safety of the people.   

scholarly journals RTA Mathematic Simulation Principles at the Autotechnical Expertise

2021 ◽  
Vol 334 ◽  
pp. 02026
Author(s):  
Badrudin Gasanov ◽  
Artem Efimov ◽  
Jurij Grebennikov
Keyword(s):  
Mathematical Model ◽  
Road Transport ◽  
Road Accident ◽  
Momentum Vector ◽  
The Road ◽  
Solid Friction ◽  
Strain Stress ◽  
Determination Methods ◽  
Determine Step ◽  
Energy Conservation Law

The features of carrying out an autotechnical expertise (ATE) are considered in case the vehicles (V) participating in the road transport accident (RTA) don’t leave skid imprints. The examples of momentum and energy conservation law application are given at developing the road accident mathematical model. Special attention is paid to the determination methods of vehicle (V) velocity, travel directions in various RTA diagrams and archeology of deformation. For this purpose it is offered to draw a momentum vector diagram. It is reasonable that for the calculation of V deformation at RTA it is necessary to determine step by step the strain-stress state in a contact area on the basis of the theories of elasticity, plasticity, solid friction and finite-element methods. The technique of constructing an RTA mathematical model is developed. It is recommended to use at ATE of RTAs at the runs-over into the fixed obstacle (a stationary V) and collisions.

scholarly journals PENGGUNAAN DATA AIS-IRSMS UNTUK PREDIKSI KECELAKAAN MELIBATKAN SEPEDA MOTOR PADA RUAS JALAN DI KOTA SURABAYA

2018 ◽  
Vol 1 (1) ◽  
pp. 8
Author(s):  
Muhammad Zainul Arifin ◽  
Imma Widyawati Agustin ◽  
Sonya Sulistyono
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Road Transport ◽  
Transport Policy ◽  
Policy Makers ◽  
Accident Prediction ◽  
The Road ◽  
Accident Data ◽  
On The Road ◽  
Accident Estimation

Accidents of involving motorcycles in Surabaya tend to increase. Recorded from 2014 to 2016 were reached 721, 929 and 1,325 accidents. This phenomenon is certainly not beneficial for the community and road transport policy makers. This study was conducted to determine the characteristics of motorcycle riders and accidents of involving motorcycles. This research further develops estimation of accident prediction involving motorcycle in Surabaya City. Accident data compiled from AIS-IRSMS to know the characteristics of users and accidents using the accidents approach. The research location was conducted in accident prone areas during January 2014 to February 2017 also using AIS-IRSMS. With the Generalized Linear Models (GLMs), the result of estimation of accident estimation involving motorcycle that is McA = 0.00225 Q1.030 e(0.034 S). Accidents of involving motorcycles are heavily influenced by the number of vehicles on the road and the speed of the vehicle. Kecelakaan melibatkan sepeda motor di Kota Surabaya cenderung mengalami peningkatan. Tercatat tahun 2014 hingga 2016 mencapai 721, 929 dan 1.325 kejadian kecelakaan. Fenomena ini tentunya tidak menguntungkan bagi masyarakat dan pengambil kebijakan terkait transportasi jalan raya. Penelitian ini dilakukan untuk mengetahui karakteristik pengendara sepeda motor terlibat kecelakaan dan kecelakaan melibatkan sepeda motor. Lebih lanjut penelitian ini melakukan pengembangan estimasi prediksi kecelakaan melibatkan sepeda motor di Kota Surabaya. Data kecelakaan dikompulir dari AIS-IRSMS untuk mengetahui karakteristik penguna dan kecelakaan menggunakan pendekatan frekwensi kejadian. Lokasi penelitian dilakukan pada daerah rawan kecelakaan sepanjang Januari 2014 hingga Februari 2017 juga menggunakan bantuan AIS-IRSMS. Menggunakan metode Generalized Linear Models (GLMs), hasil penelitian diperoleh estimasi prediksi kecelakaan melibatkan sepeda motor yaitu McA= 0,00225 Q1,030 e(0,034 S). Kecelakaan melibatkan sepeda motor sangat dipengaruhi oleh banyaknya kendaraan di jalan dan kecepatan kendaraan.

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