Link criticality analysis (LCA) in transportation networks plays a pivotal role in assessing the systemic impact of link failures on overall traffic performance. Traditional LCA approaches often rely on exhaustive link-removal simulations or graph-theoretic metrics, which become computationally prohibitive and behaviorally simplistic when addressing multiple link failures. This study proposes a novel, scalable framework that integrates stochastic user equilibrium traffic assignment, deep-learning-based flow estimation using stacked autoencoders (SAEs), and multi-method global sensitivity analysis (GSA) to evaluate network-wide link importance. The framework generates synthetic demand scenarios using Monte Carlo simulations, applies a stochastic assignment model to estimate flow distributions, and trains an SAE model to predict average user delay. The trained model then enables efficient GSA to quantify the influence of each link. The methodology is applied to a real-world case study in Egypt’s New Capital. The proposed framework demonstrates high predictive accuracy (mean standard error = 0.66, R² = 0.98) and computational efficiency, making it suitable for large-scale, data-sparse, or developing urban contexts. GSA results reveal critical links with both direct and nonlinear effects on delay, guiding planners toward strategic investments and resiliency planning. This integrated approach advances LCA by offering interpretable, scalable, and data-driven insights into transportation network vulnerabilities.

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.