Abuzwidah, Muamer
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Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles Abuzwidah, Muamer; Elawady, Ahmed; Wang, Ling; Zeiada, Waleed
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-019

Abstract

Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility. Doi: 10.28991/CEJ-2024-010-09-019 Full Text: PDF
Machine Learning Modelling of IRI in Continuously Reinforced Concrete Pavements Using LTPP Data: Comparative Evaluation of Advanced Algorithms Alnaqbi, Ali; Zeiada, Waleed; Al-Khateeb, Ghazi; Abuzwidah, Muamer
Civil Engineering Dimension Vol. 28 No. 1 (2026): MARCH 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.28.1.130-144

Abstract

Accurately predicting International Roughness Index (IRI) is essential for effective pavement maintenance and long-term network sustainability. This study evaluates several advanced machine learning models for IRI prediction in Continuously Reinforced Concrete Pavement (CRCP) using a comprehensive dataset from Long-Term Pavement Performance (LTPP) program. Support Vector Machines, Artificial Neural Networks, Regression Trees, Ensemble Trees, and Gaussian Process Regression (GPR) were developed and assessed using Root Mean Square Error (RMSE) and R-squared (R²). The Matern 5/2 GPR model achieved the best performance, with R² = 0.97 and RMSE = 0.0776. Feature importance analysis using Random Forest identified initial IRI, construction number, layer thicknesses and temperature as the strongest predictors. Sensitivity analysis confirmed the influence of age, climate, and traffic on IRI. Using only the top ten variables produced nearly identical accuracy, improving computational efficiency. Overall, the study demonstrates the strong potential of ML for reliable and sustainable IRI prediction in rigid pavements.