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Author: search.filters.author.César CorralesStart date: 2022

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    Intelligent route planning for effective police patrolling in a Peruvian district
    (Springer, 2022-01-01)
    Citizen security directly influences life quality and is a great source of concern in Latin America. A scheduling and vehicle routing model is proposed for the allocation and routing of police resources, increasing visits in locations with a higher crime rate, and strategic standby locations based on real data.
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    DISCRETE SIMULATION OF TRAFFIC ACCIDENTS ON THE PERUVIAN HIGHWAY USING ARENA
    (Universidad Nacional de Colombia, 2025-02-12)
    This paper developed a simulation model in ARENA, taking into account its versatility and power, with the of studying possible crash scenarios on a Peruvian highway in the next five years. Based on the available data and the assumptions used in the probability distribution for each factor, crashes that may occur in the future, without changes in the actual road conditions, were simulated. The results, validated with real data from 2022 and 2023, showed the areas, periods of the year, days, hours, potential hazard vehicles that need more signage or human or technological control, to decrease the exponential generation rate which is currently 5.08 days. In conclusion, it is an important contribution of science to academia and safety practice to address the high vehicle accident rate, which is showing significant increases and needs to raise awareness among institutions and road users. Keywords: Simulation; Bus crashes; ARENA; Road Accidents, Safety
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    Analysis of the Severity of Road Accidents Using Combined Data Mining Techniques
    (Multidisciplinary Digital Publishing Institute (MDPI), 2026-06-01)
    Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility.