This study presents the most extensive and temporally grounded review of the transit network design problem (TNDP) to date, covering five decades of research and offering a unified perspective on its two core subcomponents: the transit route network design problem (TRNDP) and the frequency setting problem (FSP). As cities face mounting pressures from urbanization, climate change, and equity demands, the strategic design of public transit networks has become increasingly critical. Despite the problem’s centrality to transportation planning, the field remains fragmented and methodologically saturated, lacking integrated approaches that reflect real-world complexity. This review addresses that gap by analyzing over 170 studies from 1970 to 2024, systematically categorizing them by methodology, objective function, network scale, and system application. It is the first study to employ decade-resolved visual analytics, including heatmaps and taxonomies, to illustrate methodological trends, such as the rise of metaheuristics in the 2010s, the emerging—but still limited—role of AI/ML post-2020, and the declining prominence of classical optimization models. The study also introduces a novel scalability–performance matrix, comparing 10 solution approaches across multiple dimensions, and highlights the integration of TRNDP and FSP as a pivotal frontier in transit research. In doing so, it reveals critical research gaps, particularly the lack of resilience, equity, and adaptability in existing models, and proposes a forward-looking agenda rooted in unified, real-time, and data-driven frameworks. The review offers both a historical map and a strategic roadmap for scholars and practitioners seeking to advance sustainable and inclusive urban transit systems. The scientific value of this work lies in its combination of historical depth and methodological synthesis, introducing a novel scalability–performance matrix, decade-resolved visual analytics, and an integration-focused framework that has not been attempted in prior reviews.

With the growing urgency to address climate change, reducing greenhouse gas emissions from urban transportation systems has become a critical global challenge. Mass rapid transit systems, including rail and bus rapid transit, offer promising solutions by replacing less efficient transport modes and reducing urban air pollution. However, accurately quantifying the emission reductions achieved by MRTS projects is complex, requiring detailed consideration of baseline emissions, direct and indirect project emissions, and leakage effects such as changes in vehicle occupancy and induced traffic. This article presents a comprehensive methodology for calculating these emission reductions, grounded in established frameworks like the clean development mechanism methodology. By combining theoretical background with practical application to a Bus Rapid Transit project in México City, the article highlights key calculation steps and challenges, providing a valuable resource for researchers and policymakers aiming to promote sustainable urban mobility.

Monitoring traffic flow across large-scale transportation networks is essential for effective traffic management, yet comprehensive sensor deployment is often infeasible due to financial and practical constraints. The traffic sensor location problem (TSLP) aims to determine the minimal set of sensor placements needed to achieve full link flow observability. Existing solutions primarily rely on algebraic or optimization-based approaches, but often neglect the impact of sensor measurement errors and struggle with scalability in large, complex networks. This study proposes a new scalable and robust methodology for solving the TSLP under uncertainty, incorporating a formulation that explicitly models the propagation of measurement errors in sensor data. Two nonlinear integer optimization models, Min-Max and Min-Sum, are developed to minimize the inference error across the network. To solve these models efficiently, we introduce the BBA Algorithm (BBA) as an adaptive metaheuristic optimizer, not as a subject of comparative study, but as an enabler of scalability within the proposed framework. The methodology integrates LU decomposition for efficient matrix inversion and employs a node-based flow inference technique that ensures observability without requiring full path enumeration. Tested on benchmark and real-world networks (e.g., fishbone, Sioux Falls, Barcelona), the proposed framework demonstrates strong performance in minimizing error and maintaining scalability, highlighting its practical applicability for resilient traffic monitoring system design.

The cement industry is a significant contributor to CO₂ emissions worldwide, which demands new measures to reduce its environmental impacts. Therefore, finding solutions to reduce the CO₂ emissions in cement production became necessary. Pozzolanic materials offer an optimum solution approach with both environmental and functional advantages. For the investigation of pozzolan effects on the concrete mixture, the modeling part becomes a challenging task. This study models and predicts the compressive strength of pozzolanic cement-based concrete using deep residual neural networks (DRNNs) and variance-based sensitivity analysis (VBSA). The designed DRNNs architecture uses shortcuts (i.e., residual connections) that bypass some layers in the deep network structure in order to alleviate the problem of training with high accuracy. The research also examines crucial aspects such as pozzolan type, substitution ratio, component proportions, and grinding processes, using data developed by the authors and from different pozzolanic concrete compositions from various studies. The proposed model showed a high accuracy of R² = 0.94 for testing data that outperformed traditional literature models, enabling the generation of a large sample of synthetic experimental data for further analysis. The VBSA improves knowledge by prioritizing the importance of input factors, resulting in a complete method for designing concrete mixes. The analysis revealed that silica fume and volcanic ash were the most effective pozzolans in enhancing compressive strength, followed by scoria and metakaolin, with optimal substitution ratios ranging from 10 to 15% for most natural pozzolans and up to 20–30% for metakaolin and pumicite. Hence, this newly presented analysis framework offers an optimizing tool for pozzolanic concrete mix design that could investigate several pozzolana types/proportions, their efficiency, and the structural performance of the final concrete mixture.

The seismic pounding between adjacent, dynamically incompatible reinforced concrete structures poses a significant collapse risk. This study presents a numerical investigation to evaluate a novel coupling system (NCS) designed to enforce response synchronization and eliminate pounding, comparing its performance against a conventional individual building retrofit (IBR) strategy. Three-dimensional nonlinear finite element models were developed for two adjacent reinforced concrete buildings, representing a seismically deficient building inventory. The performance of the bare, IBR, and NCS configurations was assessed through a suite of nonlinear response history analyses and subsequent probabilistic fragility analyses. The results demonstrate the enhanced performance of the enforced synchronization approach. While the IBR strategy reduced the total number of impacts from 97 to 31 across all analyses, the NCS completely eliminated pounding by transforming the two structures into a single, coupled dynamic system. This elimination of impact-induced shock loading resulted in a 50 % reduction in peak roof acceleration relative to the bare case. Furthermore, the NCS channeled 65 % of the total input energy into its designated ductile links, compared to only 45 % for the IBR system. Probabilistic analysis confirms this performance enhancement; the median collapse capacity of the more vulnerable structure was increased from a peak ground acceleration (PGA) of 0.32 g to 1.32 g, a four-fold improvement that substantially outperforms the IBR. The findings confirm that a design philosophy based on enforced coupling is a more effective mechanism for mitigating seismic pounding than conventional, independent-building strengthening.

Abstract Accurate estimation of daily reference evapotranspiration (ETo) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ETo models to predict daily ETo across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R2), the normalized root-mean-squared error (RMSEn), mean absolute error (MAE), and simulation error (Se), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ETo-developed models showed an excellent to fair performance in predicting daily ETo over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R2 ≤ 1.0, 0.729 ≤ RMSEn ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day−1, and 0.833 ≤ Se ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (Tmax, Tmin, and Tave) predicted ETo well across the three stations during testing (17.655 ≤ RMSEn ≤ 13.469 and Se ≤ 15.45%). Results highlighted that Tmax, solar radiation (Rs), and wind speed at 2 m height (U2) are the most influential factors affecting ETo at the central Arizona sites, followed by extraterrestrial solar radiation (Ra) and Tave. In contrast, humidity-related variables (RHmin, RHmax, and RHave), along with Tmin and precipitation (Pr), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona.

The intricate dynamics of odorants in the indoor environment and human respiratory system remain poorly understood. In the present study, we integrate odor sensory tests (OSTs) and computational fluid dynamics coupled with a physiologically based pharmacokinetic (CFD-PBPK) model to elucidate various aspects of odorant transport and olfaction dynamics. Safe yogurt-derived substances were incorporated into OSTs to prevent harmful exposure. Acetaldehyde was identified as a key active component in determining odor intensity, prompting further analysis of acetone and other four constituents. Logarithmic correlations were established between the perceived odor intensity from the OSTs and both time-averaged absorption flux and equilibrium concentration within the olfactory mucus layer. These parameters were numerically captured, enabling the logarithmic approximation of odor intensity for different breathing profiles and developing reliable prediction models for odor sensation in the indoor environment based on quantifiable physiological parameters. Location-specific analysis revealed the nostrils and olfactory regions as the most accurate indicators of perceived odor intensity, proving the limitations of rough sensory assessments in the indoor/breathing zone scales. This study offers insights for potential safe and sustainable applications, such as smart odor displays, e-noses, and sensors/control systems in the indoor environment, particularly for long-term exposure in industries that emit harmful compounds.

Traditional K-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an optimal number of ellipsoidal clusters, a capability absent in traditional methods. SAELLC transforms each identified cluster into a hyperspherical cluster, where the diameter of the hypersphere equals the minor axis of the original ellipsoid, and the center is encoded to represent the entire cluster. During the assignment of points to clusters, local ellipsoidal properties are independently considered. For objective function evaluation, the method adaptively transforms these ellipsoidal clusters into a variable number of global clusters. Two objective functions are simultaneously optimized: one reflecting partition compactness using the silhouette function (SF) and Euclidean distance, and another addressing cluster connectedness through a nearest-neighbor algorithm. This optimization is achieved using a newly-developed multiobjective simulated annealing approach. SAELLC is designed to automatically determine the optimal number of clusters, achieve precise partitioning, and accommodate a wide range of cluster shapes, including spherical, ellipsoidal, and non-symmetric forms. Extensive experiments conducted on UCI datasets demonstrated SAELLC’s superior performance compared to six well-known clustering algorithms. The results highlight its remarkable ability to handle diverse data distributions and automatically …