Assessing the Severity Level of Road Traffic Accidents Based on Machine Learning Techniques

2016 ◽  
Vol 22 (10) ◽  
pp. 3115-3119
Author(s):  
Seok-Lyong Lee
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Machine Learning Techniques ◽  
Road Traffic Accidents ◽  
Severity Level ◽  
Learning Techniques

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

scholarly journals Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents

2022 ◽  
Vol 12 (2) ◽  
pp. 828
Author(s):  
Tebogo Bokaba ◽  
Wesley Doorsamy ◽  
Babu Sena Paul
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Real Life ◽  
Support Vector ◽  
Road Traffic Accidents ◽  
Machine Learning Classifiers ◽  
Reduction Techniques ◽  
Learning Classifiers ◽  
Accident Data

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

2012 ◽  
Vol 4 (2) ◽  
pp. 32-59 ◽  
Author(s):  
K. K. Chaturvedi ◽  
V.B. Singh
Keyword(s):  
Machine Learning ◽  
Tracking System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Severity Level ◽  
Bug Reports ◽  
Learning Techniques ◽  
Bug Report ◽  
Closed Source ◽  
F Measure

Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine (SVM), probability based Naïve Bayes (NB), Decision Tree based J48 (A Java implementation of C4.5), rule based Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Random Forests (RF) learners in predicting the severity level (1 to 5) of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA’s PITS (Projects and Issue Tracking System) datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering (i) the value of accuracy and F-Measure for all severity level and (ii) number of best cases at different threshold level of accuracy and F-Measure.

scholarly journals A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3174 ◽  
Author(s):  
Renato Torres ◽  
Orlando Ohashi ◽  
Gustavo Pessin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Traffic Accidents ◽  
Driver Distraction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Smartphone Sensors

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

scholarly journals Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5866
Author(s):  
Gonzalo De-Las-Heras ◽  
Javier Sánchez-Soriano ◽  
Enrique Puertas
Keyword(s):  
Machine Learning ◽  
Traffic Accidents ◽  
Data Augmentation ◽  
Cloud Service ◽  
Machine Learning Techniques ◽  
Driver Assistance ◽  
Variable Message Signs ◽  
Learning Techniques ◽  
On The Road ◽  
Message Signs

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance system) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety. This project aims to develop a prototype of a VMS (variable message sign) reading system using machine learning techniques, which are still not used, especially in this aspect. The assistant consists of two parts: a first one that recognizes the signal on the street and another one that extracts its text and transforms it into speech. For the first one, a set of images were labeled in PASCAL VOC format by manual annotations, scraping and data augmentation. With this dataset, the VMS recognition model was trained, a RetinaNet based off of ResNet50 pretrained on the dataset COCO. Firstly, in the reading process, the images were preprocessed and binarized to achieve the best possible quality. Finally, the extraction was done by the Tesseract OCR model in its 4.0 version, and the speech was done by the cloud service of IBM Watson Text to Speech.

scholarly journals Predicting Severity Level of Road Traffic Accidents in Oromiya East Shewa Zone using Iterative Dichotomiser3

2020 ◽  
Vol 9 (1) ◽  
pp. 2262-2267
Keyword(s):  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Main Task ◽  
Road Traffic Accidents ◽  
Highway Traffic ◽  
Severity Level ◽  
Presentation Methods ◽  
Serious Injuries

Highway traffic accidents are a main community health problem unease ensuing millions fatalities and million serious injuries in the world each year. In the developing country like Ethiopia, is also the victim of road traffic accident or crush causing deaths, property damage and serious injuries. In order to analyses severity level of road traffic accidents, data is important to find out factors that are related to fatal, grievous, minor and non- injuries to gauge a fixed variables that contributes towards forecast the severity level of road traffic crashes. A lane traffic stream pound or impact happens when a vehicle slams into another vehicle, passerby, creature, or geological or building obstruction and result in injury, property harm, and lethal/demise. Path traffic control framework is, where basic information about the squash is recorded and saved for looming use. Expending that information the proposed examination have been extricated the contributing elements of street auto collision and create prescient model to foresee seriousness level for street car crash, wounds and fatalities utilizing information mining methods.. The main task of research is to make known the applicability of data mining techniques in emerging a model to support road traffic accident brutality analysis in preventing and extracting patterns that are corresponding with road accident in different ways of presentation methods. The road traffic accident historical data ,obtained from traffic Oromia police commission of East Shewa Zone, Oromia and police commission of Federal government of Ethiopia.

Stereo Vision by Combination of Machine-Learning Techniques for Pedestrian Detection at Intersections Utilizing Surround-View Cameras

2020 ◽  
Vol 32 (3) ◽  
pp. 494-502 ◽  
Author(s):  
Tokihiko Akita ◽  
Yuji Yamauchi ◽  
Hironobu Fujiyoshi ◽  
◽  
Keyword(s):  
Machine Learning ◽  
Stereo Vision ◽  
Traffic Accidents ◽  
Detection System ◽  
Pedestrian Detection ◽  
Field Of View ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Wide Field ◽  
Learning Techniques

The frequency of pedestrian traffic accidents continues to increase in Japan. Thus, a driver assistance system is expected to reduce the number of accidents. However, it is difficult for the current environmental recognition sensors to detect crossing pedestrians when turning at intersections, owing to the field of view and the cost. We propose a pedestrian detection system that utilizes surround-view fisheye cameras with a wide field of view. The system can be realized at low cost if the fisheye cameras are already equipped. It is necessary to detect the positioning of pedestrians accurately because more precise prediction of future collision points is required at intersections. Stereo vision is suitable for this purpose. However, there are some concerns regarding realizing stereo vision using fisheye cameras due to the distortion of the lens, asynchronous capturing, and fluctuating camera postures. As a countermeasure, we propose a novel method combining various machine-learning techniques. The D-Brief with histogram of oriented gradients and normalized cross-correlation are combined by a support-vector machine for stereo matching. A random forest was adopted to discriminate the pedestrians from noise in the 3D reconstructed point cloud. We evaluated this for images of crossing pedestrians at actual intersections. A tracking rate of 96.0% was achieved as the evaluation result. It was verified that this algorithm can accurately detect a pedestrian with an average position error of 0.17 m.

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