Nabeel Saleem Saad Al-Bdairi

TL;DR: The study outcomes reveal that data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance, and both methodologies are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy.

TL;DR: In this article, the determinants of driver injury severity in animal-vehicle collisions while systematically accounting for unobserved heterogeneity in the data by using three methodological approaches: mixed logit model with heterogeneity in means and variances.

TL;DR: In this paper, the authors investigated the transferability of the large-truck crash injury-severity determinants across weekdays and weekends using random parameters logit models while considering three categories of injury severity levels.

TL;DR: The modeling framework presented in this work offers a flexible methodology to analyze ROR crashes involving large trucks while accounting for unobserved heterogeneity, and shows that large-truck drivers who are not licensed in Oregon have a higher probability of experiencing no injury ROR crash outcomes, and human related factor, fatigue, increases the probability of minor injury.

TL;DR: This study provides a novel methodology to predict monthly water demand based on several weather variables scenarios by using combined techniques including discrete wavelet transform, principal component analysis, and particle swarm optimisation.