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Fernando da Costa Pfitscher, Joyce Azevedo Caetano, Cintia Machado de Oliveira, Glaydston Mattos Ribeiro, Marina Leite de Barros Baltar
Abstract: The high number of deaths and serious injuries in traffic crashes can be considered a silent global epidemic, as it is still understood by part of society as an inherent consequence of road traffic. There are several risk factors that can increase the occurrence or severity of crashes on roads, acting alone or in combination. Road safety diagnoses based on facts and evidence are essential for improving public policies to reduce victims. With the aim of assisting in these diagnoses and since the official database on these victims is not made available in detail to the public, this work investigates the relationship between seven indicators, collected in field research and in public databases, and the occurrence and fatality of traffic victims in the City of Rio de Janeiro. Linear regression models are developed for each approach and the one with the best statistical parameters is chosen. The model with greater robustness demonstrated that helmet non-use, the density of traffic enforcement cameras, and illiteracy together explain a significant portion of the variation in the fatality rate. The results are considered satisfactory, since a limited number of existing risk factors for road safety were used.
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Joyce Azevedo Caetano, Larissa Rodrigues Turini, Cintia Machado de Oliveira, Eunice Horácio de Souza de Barros Teixeira Rodrigues, Nicholas Miralhes de Freitas, Ana Carolina de Barros Pamplona, Marina Leite de Barros Baltar, Glaydston Mattos Ribeiro
Abstract: This article investigates the impacts of commuting time in transportation, especially by bus, on the occupational health of the economically active population in the Rio de Janeiro Metropolitan Area (RJMA), highlighting the importance of understanding how prolonged daily travel affects well-being and productivity.
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Conrado Vidotte Plaza, Okan Arslan, Gilbert Laporte, Glaydston Mattos Ribeiro, Laura Bahiense, Glaubos Clímaco
Abstract:The limited network of chargers for electric heavy-duty trucks (eHDTs) hinders a widespread adoption due to their shorter operating range compared to diesel engines. The charger location problem seeks to determine the optimal number and location of chargers in a transportation network to support efficient eHDT logistics operations. Moreover, commercial drivers’ hours of service (HOS) are regulated by law, requiring careful planning of driving, breaks, and rest periods. In this context, effective vehicle scheduling and routing are crucial to increasing punctuality and safety in road freight transport. Neglecting these operational aspects in the charger location problem can lead to suboptimal or even infeasible decisions. The aim of this paper is to develop, model and solve the charger location problem with routing and driver’s working hours. The decisions include the long-haul electric vehicle routing, and the scheduling of drivers respecting HOS requirements by determining the locations, types and number of chargers. The problem is modeled by transforming the road network into a communication-time-expanded network through a temporal discretization process. We also present two mechanisms to reduce the model size. We conduct extensive numerical experiments in order to demonstrate the efficiency of the proposed optimization methods and to evaluate the impact of several features on the routing and scheduling of the eHDTs.
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Bruno Salezze Vieira, Glaydston Mattos Ribeiro, Antônio Augusto Chaves
Abstract: Strategic placement of alternative-fuel refuelling stations is a critical challenge for energy and transportation planners, who must navigate conflicting objectives, such as minimising capital costs and maximising service coverage. This paper presents a decision support system for the bi-objective Flow Refuelling Location Problem (FRLP) built upon the Sub-path Flow Refuelling Location Model (SPFRLM). This formulation distinguishes itself by enabling continuous facility siting along edges, managed through a dynamic separation procedure for sub-path constraints. To generate solutions efficiently, the system incorporates the Smoothest Descent Algorithm (SDA), a bi-objective method that approximates the Pareto front by dynamically switching between minimization and maximization strategies. The SDA relies on a Two-Phase Hybrid (TPH) algorithm that integrates Cut-and-Solve and Branch-and-Cut to solve the underlying sub-problems. We validate the system on a newly introduced library of 26 real-world test instances. The results demonstrate that the proposed approach captures approximately 97% of the optimal hyper-volume while requiring less than 10% of the computational time of exact methods. These findings confirm that the system is a powerful tool for stakeholders, providing rapid and accurate guidance for the strategic deployment of future energy infrastructure.
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