Traffic Prediction for Intelligent Transportation System Using Machine Learning

EasyChair Preprint no. 9911

Abstract

Automobile manufacturers have developed various safety features to mitigate the risk of traffic accidents but accidents continue to occur frequently in both urban and rural areas.To prevent accidents and improve ​​​​​​ safety measures,it is necessary to develop accurate prediction models that can identify patterns associated with        different scenarios.By using these models,we can cluster accident scenarios and develop effective safety measures.we aim to achieve the maximum possible reduction in accidents using low-budget resources through scientific measures.To achieve this goal,we need tocollect and analyze a vast amount of data related to traffic accidents,such as accident location,time,weather condition,and road features.machine learning algorithms can be used to automatically identify patterns in the data and predict accident scenarios based on these patterns these models can then be used to cluster accidents into different categories and develop safety measures tailored to each category.By using this approach,we can develop cost-effective safety measures that can be implemented in a variety of settings.We believe that this approach has the potential to significantly reduce the number of traffic accidents and improve safety for drivers,passengers,and pedestrains alike.

Keyphrases: Decision Tree, logistic regression, machine learning, Random Forest, Support Vector Machine

@Booklet{EasyChair:9911,
  author = {N Akhila and M Kavya and M Soumith Reddy and Prasanta Kumar Pradhan},
  title = {Traffic Prediction for Intelligent Transportation System Using Machine Learning},
  howpublished = {EasyChair Preprint no. 9911},

  year = {EasyChair, 2023}}