A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines

Asor, Jonardo R.; Catedrilla, Gene Marck B.; Estrada, Jheanel E.

doi:10.1109/icoiact.2018.8350662

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Similarly, J. R. Asor et al (2018) [7] investigated three prediction methods: decision tree, Naïve Bayes, and self-induction to identify factors affecting accidents in Los Baños Laguna, Philippines.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Dia

Faty

Sall³

et al. 2022

2022 the 5th International Conference on Data Storage and Data Engineering (DSDE)

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Road accidents are a major problem, the consequences of which have negative impacts on the economy, health and development of a country in general. The recrudescence of road accidents in Senegal during these last years has aroused a great debate in which certain responsibilities have been pointed out. A serious road traffic accident constitutes in most cases an emotional and economic disaster, hence the need to find sustainable solutions for the improvement of road safety. The objective of this work is to identify the major risk factors of road accidents in Senegal in order to set up a training corpus for the predictive study of the gravity of these accidents. According to our research on the bibliography, there is no study on the implementation of a corpus adapted to the data of accidents in Senegal. In this research, we used an exploratory study on the collected data to determine the factors most likely to cause accidents and supervised learning for predictive modeling of accident severity. The results showed that modelling with supervised learning can be used as an accident severity prediction system with promising accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Dia

Faty

Sall³

et al. 2022

2022 the 5th International Conference on Data Storage and Data Engineering (DSDE)

View full text Add to dashboard Cite

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“…Other classification algorithms such as K-nearest neighbors (KNN) are also used to classify accidents [30], [31].…”

Section: Related Workmentioning

confidence: 99%

Denoising Recurrent Neural Networks for Classifying Crash-Related Events

Park

Seonwoo

Kim

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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With detailed sensor and visual data from automobiles, a data-driven model can learn to classify crash-related events during a drive. We propose a neural network model accepting time-series vehicle sensor data and forward-facing videos as input for learning classification of crash-related events and varying types of such events. To elaborate, a novel recurrent neural network structure is introduced, namely, denoising gated recurrent unit with decay, in order to deal with time-series automobile sensor data with missing value and noises. Our model detects crash and near-crash events based on a large set of time-series data collected from naturalistic driving behavior. Furthermore, the model classifies those events involving pedestrians, a vehicle in front, or a vehicle on either side. The effectiveness of our model is evaluated with more than two thousand 30-s clips from naturalistic driving behavior data. The results show that the model, including sensory encoder with denoising gated recurrent unit with decay, visual encoder, and attention mechanism, outperforms gated recurrent unit with decay, gated CNN, and other baselines not only in event classification and but also in event-type classification.

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“…Injuries due to road accidents cost around 518 billion USD every year globally [6]. A person's social contacts are also affected by an accident, in addition to his or her own injuries [7]. Every day, six Indians were killed by potholes in 2016.…”

Section: Introductionmentioning

confidence: 99%

Pothole recognition using convolution neural networks and transfer learning

Senigalakuruba¹,

Pabba²

2023

IJ-AI

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Potholes have been and still are a huge problem for every walk of life. There are many deaths and accidents reported daily due to that very problem. For that reason, pothole recognition comes into the picture. To maintain and preserve a road, it is vital to detect potholes. It also helps in the prevention of accidents. Roads play an important part in day-to-day transportation for every person around the world. But the quality of roads decreases drastically due to the way of usage and aging. The existing methods take much time and manpower to repair the damaged areas. The entire process is slowing down just because an expert team is checking whether there is a pothole at the reported location or not. So, if we automate the process of detection of potholes from a set of images reported from a particular location and appropriately alerting the authorities with the amount of damage, the process speeds up exponentially. We must solve the major problem of pothole recognition by using machine learning algorithms. This paper will discuss a convolution neural network-based and a transfer learning-based solution for pothole recognition.

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A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines

Cited by 10 publications

References 5 publications

Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Predicting the Severity of the Road Accidents in Senegal: An Empirical Study

Denoising Recurrent Neural Networks for Classifying Crash-Related Events

Pothole recognition using convolution neural networks and transfer learning

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