Colonial historic city centers represent a paradigmatic challenge for modern road safety, as they are characterized by narrow streets originally designed for carriage and pedestrian traffic. This research presents ADAS-TSR, a deep learning-based advanced driver assistance system for vertical traffic sign detection with voice alerts, specifically designed for the Historic Center of Ayacucho, Peru, which is located at 2761 m a.s.l. An original dataset comprising 2250 images with 2450 instances corresponding to 14 sign classes according to Peruvian regulations was constructed. The dataset was captured under real operational conditions, including deteriorated, partially occluded, and vehicle impact-deformed signage. A comprehensive multi-model benchmark experiment was conducted, comparing four CNN-based detectors (YOLOv8m, YOLO11n, YOLO26n, YOLO26s) and one transformer-based detector (RT-DETR-l) spanning both classical and state-of-the-art architectures released through January 2026. YOLO26s achieved the best overall performance, with an mAP@0.5 of 0.994 and mAP@0.5:0.95 of 0.989 while using only 9.5 M parameters. YOLO11n matched the performance of YOLOv8m with 10× fewer parameters (2.6 M vs. 25.9 M). Uncertainty analysis revealed that modern architectures exhibit significantly higher prediction confidence (mean > 0.90) compared to YOLOv8m (0.82), and fairness analysis confirmed equitable detection across all 14 classes (Gini < 0.002). A voice alert system with five priority levels and rule-based temporal filtering for detection stabilization was implemented. Validation across five urban circuits spanning 14.11 km demonstrated a detection rate of 94.7% with a 73% reduction in redundant alerts.

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects.

The urban heat island (UHI) effect in Andean cities is a critical yet understudied phenomenon, where complex topography and rapid urbanization uniquely alter local climates. This research analyzes the spatiotemporal evolution of the surface UHI and its linkage to urban morphology in Ayacucho, Peru, through a 40-year multi-temporal analysis (1986–2016) using Landsat images. We developed a synthetic Urban Heat Island Index (UHII) through Principal Component Analysis (PCA), integrating land surface temperature (LST), spectral indices, and urban morphological parameters. Our results identify a critical transition in 2006, with the emergence of persistent heat spots driven by unplanned expansion. The surface UHI intensity reached urban-rural differences of 4.31 °C (day) and 5.82 °C (night), showing a positive trend. Urban morphology was a key determinant, with high-density blocks exhibiting a minimum nocturnal LST 3.53 °C higher than low-density areas. Statistical trend tests confirmed a significant intensification, while a strong negative correlation with vegetation indices (R² = 0.97) underscored the vital mitigation role of green infrastructure. This study provides academics with a robust methodological framework for UHI analysis in complex terrains. For public and private urban managers, it offers spatially explicit evidence to prioritize actionable strategies, such as integrating green infrastructure and regulating urban form, to enhance climate resilience in Andean cities.

Traffic congestion in urban commercial districts presents a critical challenge to sustainable mobility, particularly in developing cities. This study addresses this issue by developing and calibrating a mesoscopic simulation model to optimize traffic performance parameters in the commercial district of Ayacucho, Peru. The methodology was based on extensive fieldwork to gather traffic volume, travel time, and parking data. Using this data, a PTV Vissim model was developed and rigorously calibrated, with its accuracy validated through the Geoffrey E. Havers (GEH) statistic. Various traffic management strategies, including signal timing adjustments and parking supply regulation, were simulated and evaluated. The results indicate a substantial improvement in network performance: Average intersection delay was reduced from 10.72 seconds to 7.40 seconds, and a significant decrease in queue lengths was observed. The findings confirm that calibrated mesoscopic simulation serves as a robust and effective tool for quantitatively assessing traffic interventions, thereby providing municipal authorities with reliable data for evidence-based urban planning.

La presente investigación analiza la influencia de la temperatura de mezclado en el comportamiento mecánico de mezclas asfálticas utilizadas en pavimentos flexibles, con el objetivo de identificar el rango térmico óptimo que garantice su resistencia estructural y durabilidad. Se formularon mezclas a temperaturas de 125 °C, 145 °C y 185 °C, evaluadas mediante el ensayo Marshall (ASTM D6927) para determinar la estabilidad y el flujo, y mediante el ensayo de retención de adhesividad (ASTM D3625) para analizar su comportamiento frente a la humedad.

En la Ingeniería Civil, cada proyecto ejecutado, cada material analizado y cada estructura diseñada encierra un proceso de observación, análisis y toma de decisiones que, al sistematizarse, se convierte en conocimiento científico. En este sentido, investigar no es una actividad ajena al quehacer del ingeniero, sino una extensión natural de su rol como profesional que resuelve problemas concretos con fundamento técnico y responsabilidad social.