This study investigates the combined use of waste glass cullet (WGC) and snail shell powder (SSP) as a sustainable binary cementitious system to enhance the mechanical performance and durability of concrete, particularly for rigid pavement applications. Nine concrete mixes were formulated: a control mix, four mixes with 5%, 10%, 15%, and 20% WGC as partial cement replacement, and four corresponding mixes with 1% SSP addition. Slump, compressive strength, and flexural strength were evaluated at various curing ages. Results showed that while WGC reduced workability due to its angular morphology (slump decreased from 30 mm to 20 mm at 20% WGC), the inclusion of SSP slightly mitigated this reduction (21 mm at 20% WGC + 1% SSP). At 28 days, compressive strength increased from 40.0 MPa (control) to 45.0 MPa with 20% WGC and further to 48.0 MPa with the addition of SSP. Flexural strength also improved from 7.0 MPa (control) to 7.8 MPa with both WGC and SSP. These improvements were statistically significant (p < 0.05) and supported by correlation analysis, which revealed a strong inverse relationship between WGC content and slump (r = −0.97) and strong positive correlations between early and later-age strength. Microstructural analyses (SEM/EDX) confirmed enhanced matrix densification and pozzolanic activity. The findings demonstrate that up to 20% WGC with 1% SSP not only enhances strength development but also provides a viable, low-cost, and eco-friendly alternative for producing durable, load-bearing, and sustainable concrete for rigid pavements and infrastructure applications. This approach supports circular economic principles by valorizing industrial and biogenic waste streams in civil construction.

Traffic counts are essential for assessing road capacity to provide efficient, effective, and safe mobility. However, current methods for generating models for traffic count studies are often limited in their accuracy and applicability, which can lead to incorrect or imprecise estimates of traffic volume. This study focused on analyzing and predicting traffic conditions on Ikorodu Road in Lagos State. The analysis involved an examination of historical traffic data, specifically focusing on daily and hourly traffic volumes. The prediction involved the use of machine learning models, including decision trees, gradient boosting, and random forest classifiers. The results of this study revealed significant variations in traffic volume across different days of the week and times of the day, indicating peak and off-peak periods. The study also highlighted the need for a more comprehensive approach that includes additional factors, such as weather conditions, road work, and special events, which could significantly impact traffic volume.