LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Traffic Accident’s Severity Prediction: A Deep-Learning Approach-Based CNN Network

Photo from wikipedia

In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation… Click to show full abstract

In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation in a timely manner. The existing traffic accident’s severity prediction methods mainly use shallow severity prediction models and statistical models. To promote the prediction accuracy, a novel traffic accident’s severity prediction-convolutional neural network (TASP-CNN) model for traffic accident’s severity prediction is proposed that considers combination relationships among traffic accident’s features. Based on the weights of traffic accident’s features, the feature matrix to gray image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident’s data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident’s severity prediction has a better performance.

Keywords: severity prediction; traffic accident; traffic

Journal Title: IEEE Access
Year Published: 2019

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.