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.

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.

This study rigorously examines the nexus between relationship marketing and customer loyalty within an ice cream and beverage company in Ayacucho, Peru. Employing a quantitative, correlational, and non-experimental design, the research draws on a purposive non-probabilistic sample of 386 consumers. Data were gathered through a structured questionnaire based on a five-point Likert scale, whose content validity was established through expert judgment and reliability confirmed via (Cronbach’s alpha coefficients exceeding 0.80). The empirical findings demonstrate robust, positive, and statistically significant associations between the dimensions of relationship marketing (trust, satisfaction, and commitment) and the components of customer loyalty (retention, perceived value, and loyalty). More specifically, the evidence underscores that trust and satisfaction act as pivotal determinants in cultivating enduring customer relationships, whereas commitment performs a reinforcing function by consolidating retention and perceived value. Consequently, the study enriches the literature by providing context-specific empirical evidence from Latin America, thereby advancing the debate on relationship marketing as a strategic lever for strengthening loyalty in small- and medium-sized enterprises (SMEs). In practical terms, the results underscore the imperative for managers to formulate customer-centric strategies that strategically enhance trust, satisfaction, and commitment as essential drivers of sustainable competitiveness.

Inventory and condition assessment of vertical traffic signs constitutes an essential activity for road safety management, as these signs serve as the primary mechanism for regulating vehicular flow and alerting drivers to prevailing road conditions. However, current inspection procedures in Peru rely on field crews that evaluate each sign manually, thereby constraining the frequency, objectivity, and scalability of the process. This paper presents SISEVIR (Sistema de Supervisión de Señales Verticales en Infraestructura Vial), a three-stage deep learning pipeline for the automated diagnosis of vertical traffic sign condition. The first stage employs YOLO26s-seg for instance segmentation of 31 sign classes, achieving a test mAP50 of 0.9305 (box) and 0.9224 (mask). The second stage classifies each detected sign into seven deterioration states using EfficientNet-B0, optimized through a five-experiment ablation study that identified progressive offline augmentation as the most effective strategy for handling a 147:1 class imbalance (macro F1 = 0.8252; pairwise McNemar’s tests with Holm–Bonferroni correction did not confirm significance at the family-wise $\alpha =0.05$ level). The third stage integrates Qwen2-VL-2B-Instruct, a vision–language model, to generate natural-language descriptions of sign condition aligned with the MTC Manual of Traffic Control Devices for Streets and Highways. A structured evaluation by two independent raters on 35 descriptions yielded a correctness rate of 93.5% among valid responses (95% CI: 79.3–98.2%, Cohen’s $\kappa =1.00$). The system was trained and validated on a proprietary dataset of 5935 images and 6412 labeled crops collected along three routes in the Ayacucho Region (246.6 km total), with an inter-rater reliability of $\kappa =0.802$ (95% CI: 0.676–0.928). SISEVIR processes vehicular video at 30.7 FPS on an NVIDIA RTX 5080 GPU and assigns each sign a level within a four-tier condition scale (Optimal through Critical) linked to specific maintenance interventions, significantly reducing the time, cost, and personnel required compared with the manual inspection method established in the MSV-2016 Road Safety Manual.