Delasalles, E., Ziat, A., Denoyer, L. and Gallinari, P. (2019) Spatio-Temporal Neural Networks for Space-Time Data Modeling and Relation Discovery. Knowledge and Information Systems, 61, 1241-1267.
https://doi.org/10.1007/s10115-018-1291-x

has been cited by the following article:

• TITLE: A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

  AUTHORS: Ed Pearson III, Aschalew Kassu, Louisa Tembo, Oluwatodimu Adegoke

  KEYWORDS: Artificial Neural Network, Multilayer Perceptron, Fatal Crash, Traffic Safety

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.12 No.3, July 31, 2024

  ABSTRACT: This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.