José Manuel Lozano Domínguez

The safety of roads in urban areas is a major concern for governments, demanding innovative solutions to enhance pedestrian safety. This paper introduces a novel approach to crosswalks by integrating resin with photoluminescent additives, offering a significant boost to road safety. A thorough methodology was employed to assess its effectiveness, covering mechanical, lighting, and vibroacoustic aspects, alongside a photogrammetric analysis of real-world experiments. The material exhibited noteworthy mechanical properties, displaying consistent tensile strength, load capacity, and strain values with a remarkable Shore A hardness. After 20 min, luminance values peaked at 68 mcd/m2, surpassing standard vehicle headlights at 100 m. Additionally, vibroacoustic analysis highlighted a noticeable relationship between vehicle speed and sound bandwidth, indicating the system’s potential to alert pedestrians. Tests revealed that the proposed system significantly decreased the average vehicle speed by 36.96% compared to conventional crosswalks, with a 27.80% reduction when drivers yielded to pedestrians. Furthermore, a survey involving 35 participants, focusing on the knowledge of road safety regulations, behavior, signage, and visibility, found positive results regarding accident reduction. The estimations indicate potential decreases of 26.26% in injuries and 35.4% in fatalities due to improved road conditions, 26.58% in injuries and 53.16% in fatalities resulting from reduced average speeds, and 52.56% in injuries and 79.91% in fatalities through enhanced road education. This underscores the multifaceted impact of the system on urban road safety.

Pedestrian safety is a major concern in urban areas, and crosswalks are one of the most critical locations where accidents can occur. This research introduces an intelligent crosswalk, employing sensor fusion and machine learning techniques to distinguish the presence of pedestrians and drivers. Upon detecting a pedestrian, the system proactively activates a warning light signal. This approach aims to quickly alert nearby people and mitigate potential dangers, thereby strengthening pedestrian safety. The system integrates data from radio detection and ranging sensors and a magnetic field sensor, using a hierarchical classifier. The One-Class support vector machine algorithm is used to classify objects in the radio detection and ranging data, while fuzzy logic is used to filter out targets from the magnetic field sensor. Additionally, this work presents a novel method for the manufacture of the road signaling system, using mixtures of resins, aggregates, and reinforcing fibers that are cold-injected into an aluminum mold. The mechanical, optical, and electrical characteristics were subjected to standardized tests, validating its autonomous operation in real-world conditions. The results revealed the system’s effectiveness in detecting pedestrians with a 99.11% accuracy and a 0.0% false-positive rate, marking a substantial improvement over the previous fuzzy logic-based system with an 81.33% accuracy. Attitude testing revealed a significant 33.33% reduction in pedestrian erratic behavior and a substantial decrease in driver speed (32.83% during the day and 70.6% during the night) compared to conventional crossings. Consequently, this comprehensive work offers a unique solution to pedestrian safety at crosswalks by showcasing the potential of machine learning techniques, particularly the One-Class support vector machine algorithm, in advancing road safety through precise and reliable pattern recognition.