Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

Mineral fillers provide a significant role in the Marshall properties of hot mix asphalt for paving applications. The article's goal is to assess the suitability and effectiveness of two minerals (coal dust and wood powder ash) used as fillers in asphalt concrete. Chemical composition test using X-ray fluorescence indicated a high content of SiO2, Fe2O3, and Al2O3, which encouraged us to select the coal dust and wood powder ash as mineral fillers for further investigation. A total of 90 cylindrical Marshall Specimens, made with different percentages (i.e., 4%-8%) of coal dust, wood powder ash, and conventional stone dust filler were prepared to assess the performance of individual filler within the asphalt concrete mix. And after that, volumetric characteristics such as density, stability-flow test, air void, and voids in mineral aggregates have been analyzed to evaluate the effectiveness of every sample and, afterward, to find out the optimum asphalt content. Finally, the optimum asphalt content for every filler material was ascertained, and subsequently, Marshall properties were checked again to assess the optimum filler content in the mix that satisfy all the standard criteria. The overall Marshall properties for both fillers were within the acceptable limits. Though the optimum asphalt content was higher for coal dust than wood powder ash and stone dust, the wood powder ash showed better durability than coal dust. All mixtures have been found to have better resistance to deformation, fatigue, and moisture-induced damages; however, 4% coal dust and 6% wood powder ash satisfied most of the Marshall criteria than other percentages.

The disposal of massive amounts of waste, particularly nondecaying waste, is now a major issue for both developed and developing countries. One of the most sustainable solutions to this problem is to recycle garbage into valuable items. Expanded polystyrene, which is manufactured in large quantities, is one of these waste materials. This is being used globally as packaging material, construction material, and household appliances. The waste expanded polystyrene (EPS) is necessary due to its biodegradability, and aesthetically, it has a great negative impact on the environment. The goal of this research would be to see how shredded waste EPS affects the characteristics of asphalt and asphalt concrete. For this, four separate serial asphalt concrete samples were made with varying amounts of shredded expanded polystyrene waste (0.25, 0.50, 0.75, and 1% by the weight of the total aggregate) and five different percentages of asphalt (4.0, 4.5, 5.0, 5.5, and 6.0% by the weight of the total aggregate). In this study, 60/70 grade bitumen was used. Modified asphalt concrete properties were examined and compared with those of the standard specimens. The penetration, ductility, softening point, flash point, and fire point of the asphalt will all be affected by the addition of EPS plastic waste. The optimal asphalt content of the conventional specimen was 5.1 percent, and different percentages of EPS with OAC were applied. The mechanical properties of all specimens were studied in terms of Marshall properties after Marshall specimens were created with regard to OAC by adding EPS. It has been discovered that applying a 0.5% addition produces improved results than other methods. Simultaneously, as the EPS percentage increased, the stability value increased by approximately 82.61% compared to the traditional mix.

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.