The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions (${CO}_2$ and ${NO}_x$). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of ${CO}_2$ emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities.

Many epidemiological studies have evaluated the accuracy of machine learning models in predicting levels of particulate number (PN) and black carbon (BC) pollutant concentrations. However, few studies have investigated the ability of machine learning to predict the pollutant concentration with using unrefined mobile measurement data and explore the reliability of the prediction models. Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. This study compared the differences between long-term (daily average) and short-term (hourly average and 1 s unrefined data) model performance in three different classes of cross validation: randomly, spatially, and spatially temporally. This study used secondary data describing BC and PN pollutant levels in the rural location of Blacksburg (VA). Our results show that the model based on unrefined data was able to detect the pollutant hot spot areas with similar accuracy compared to the aggregated model. Moreover, the performance was found to improve when temporal data added to the model: the 10-fold MAE for the BC and PN were 0.44 μg/m³ and 3391 pt/cm³, respectively, for the unrefined data (one second data) model. The findings detailed here will add to the literature on the correlation between data (pre)processing and the efficacy of machine learning models in predicting pollution levels while also enhancing our understanding of more reliable validation strategies.

A confined plunging liquid jet reactor (CPLJR) is an unconventional efficient and feasible aerator, mixer and brine dispenser that operates under many operating conditions. Such operating conditions could be challenging, and hence, utilizing prediction models built on machine learning (ML) approaches could be very helpful in giving reliable tools to manage highly non-linear problems related to experimental hydrodynamics such as CPLJRs. CPLJRs are vital in protecting the environment through preserving and sustaining the quality of water resources. In the current study, the effects of the main parameters on the air entrainment rate, Q_a, were investigated experimentally in a confined plunging liquid jet reactor (CPLJR). Various downcomer diameters (D_c), jet lengths (L_j), liquid volumetric flow rates (Qj), nozzle diameters (d_n), and jet velocities (V_j) were used to measure the air entrainment rate, Q_a. The non-linear relationship between the air entrainment ratio and confined plunging jet reactor parameters suggests that applying unconventional regression algorithms to predict the air entrainment ratio is appropriate. In addition to the experimental work, machine learning (ML) algorithms were applied to the confined plunging jet reactor parameters to determine the parameter that predicts Q_a the best. The results obtained from ML showed that K-Nearest Neighbour (KNN) gave the best prediction abilities, the proportion of variance in the Q_a that can be explained by the CPLJR parameter was 90%, the root mean square error (RMSE) = 0.069, and the mean absolute error (MAE) = 0.052. Sensitivity analysis was applied to determine the most effective predictor in predicting Q_a. The Qj and V_j were the most influential among all the input variables. The sensitivity analysis shows that the lasso algorithm can create an effective air entrainment rate model with just two of the most crucial variables, Qj and V_j. The coefficient of determination (R²) was 82%. The present findings support using machine learning algorithms to accurately forecast the CPLJR system’s experimental results.

The sustainable management of road infrastructure has become a critical challenge for transportation agencies due to growing safety requirements, environmental pressures, and financial constraints. While several sustainability assessment frameworks exist, there is still a lack of an integrated and quantitative index that systematically prioritizes key sustainability criteria for roadway infrastructure while accounting for uncertainty in expert judgment. This study addresses this gap by developing a Green Road Index (GRI) to support informed decision-making in sustainable transportation planning and policy. The proposed GRI is developed using the Fuzzy Analytic Hierarchy Process (FAHP) , which enables the integration of qualitative and quantitative indicators and effectively manages the uncertainty inherent in subjective evaluations. A structured questionnaire was administered to 30 experts from academia, industry, and public agencies to evaluate four main sustainability criteria include safety, geometric design, cost, and environmental impact , along with their associated sub-criteria through fuzzy pairwise comparisons. The results reveal that safety is the most influential criterion in sustainable roadway development, with a global weight of 0.343 , followed by geometric design (0.315) , environmental impact (0.182) , and cost (0.160) . High-priority sub-criteria include enforcement and regulations, pedestrian facilities, traffic control devices, slope reduction, renewable energy use, and promotion of public transportation. These findings emphasize the dominant role of safety and geometric design while highlighting the need to balance economic and environmental considerations. The proposed GRI offers a transparent and adaptable decision-support tool for evaluating and prioritizing roadway projects. By consolidating multiple sustainability dimensions into a single index, the study provides practical insights for planners and policymakers and contributes to advancing sustainable transportation infrastructure assessment.

: The effects of the main parameters on the air entrainment rate, Qa, were investigated experimentally in a confined plunging liquid jet reactor CPLJR. Various downcomer diameters (Dc), jet lengths (Lj), liquid volumetric flow rates (Qj), nozzle diameters (dn), and jet velocity (Vj) were used to measure air entrainment, Qa. The non-linear relationship between the air entrainment ratio and confined plunging jet reactor parameters suggests that applying unconventional regression algorithms to predict the air entrainment ratio is appropriate. This study applied machine learning algorithms to the confined plunging jet reactor parameters to predict Qa. The obtained results showed that K-Nearest Neighbour (KNN) gave the best prediction abilities, R2 = 0.900, RMSE = 0.069, and MAE = 0.052. The sensitivity analysis was applied to determine the most effective predictor. The liquid volumetric flow rate (Qj) and jet velocity (Vj) were the most influential among all the input variables. Our findings support using machine learning algorithms to accurately forecast the CPLJR system’s experimental results.