Insufficient data availability and suboptimal monitoring systems notably reduced the lifespan of flexible pavements. This study addressed these challenges by introducing an innovative tool to enhance control over pavement conditions. Initial field observations identified various types of cracking, forming the basis for a comprehensive photogrammetric data survey. This dataset was then employed to train a Deep Learning model for object detection. The results showcased the model’s exceptional reliability in identifying pavement cracks, achieving an impressive accuracy rate of 83.33%. The study emphasizes the practical viability of the proposed tool as an effective means of monitoring roadway conditions. By overcoming data limitations and monitoring deficiencies, this research not only contributes to the progression of pavement maintenance practices but also establishes a solid foundation...

This article addresses the issue of user congestion at a high-demand public transportation stop in Lima, caused by prolonged waiting times and the perceived low quality of service. Micro simulation was conducted using VISSIM software to model ideal scenarios based on empirical data. The analysis considered key indicators such as service demand, congestion levels, and operational frequency of transportation lines. The study simulated the anticipated behavior of users who, upon having access to real-time bus arrival information, arrive at the stop just in time, thereby reducing waiting times. The results showed a 12.22% reduction in user congestion during peak hours and a more uniform redistribution of service demand during the same period. This optimization improved passenger flow and user experience without the need to alter the current bus frequencies, validating the economic and operat...