Modeling the severity of road accidents at intersections

Allouch, Mariem; Ouni, Fedy

doi:10.1109/logistiqua49782.2020.9353914

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimized models can be efficiently used by the government to reduce road accidents and to implement policies for road safety. Mariem ALLOUCH and Fedy OUNI (2020, [2]), their work aimed to highlight the way and extent through which the explanatory variables such as a human, vehicle, and environment affect the severity of a traffic accident. They chose a multinomial logit model to analyze the severity levels of road accidents at intersections.…”

Section: IIImentioning

confidence: 99%

Road Structure Quality Classification using Reinforcement Learning

Shilpasree¹

2022

IJRASET

View full text Add to dashboard Cite

Owing to the exponentially growing amount of transportation on the road, the number of mishaps on daily basis is also growing at a shocking rate. There is one death every 4 minutes due to road mishaps in India. One of the foremost causes of these road mishaps to occur is the poor road environment. In the present work, Reinforcement learning is used to classify road quality. Reinforcement learning is a part of machine learning. Suitable actions are taken to maximize the reward. Reinforcement learning is different from supervised learning, in reinforcement learning, there is no trained dataset but the agent learns from its experience and decides what actions should be performed for the given task in order to achieve the maximum reward. We use Qlearning a Reinforcement learning algorithm that will find the best action, in the given current state of an environment. It chooses the action randomly and aims to maximize the reward. An environment is created in which policy, action, and rewards to classify road structure quality are defined. The dataset has 15 features and 5 classes based on which policy is defined that defines the next action to be taken based on the state. Training and Testing are done with 80:20 ratios. Accuracy for q-learning is 93.62% and enhanced q-learning is 93.74% obtained. A confusion matrix with precision, F1 score, recall, and support is calculated. The accuracy obtained for different training and testing model ratios 60:40, 70:30, 80:20, and 90:10 is 93.19, 91.06, 91.51, and 91.86 respectively. The average accuracy for the training and testing model is 91.86.

show abstract

Section: IIImentioning

confidence: 99%

Road Structure Quality Classification using Reinforcement Learning

Shilpasree¹

2022

IJRASET

View full text Add to dashboard Cite

show abstract

“…Currently, the severity of road traffic accidents is primarily studied using data mining and machine learning techniques. Yan, Zhangcun et al [1] employed the heteroscedastic ordered logit (HORL) model. Casado-Sanz [2] utilized latent cluster analysis as an initial tool to analyze the contributing factors that increase the likelihood of fatal outcomes.…”

mentioning

confidence: 99%