Quantitative Availability Analysis of Fog-Edge Monitoring Architectures in Bus Rapid Transit Station

• Raquel F. Trajano UFPE
• Carlos Melo UPE
• Jamilson Ramalho UFPE

Resumo

Improving the quality and reliability of public transportation services is a key requirement for addressing mobility challenges in large urban centers. Bus Rapid Transit (BRT) systems play a significant role in achieving this goal. However, their effectiveness relies on the combination of several elements, among them the monitoring of stations, which is essential for managing passenger flow and ensuring safety. Evaluating the availability of such monitoring systems at scale across an entire corridor with multiple station types is a complex challenge, as individual station analyses fail to capture the systemic risks associated with the network’s topology. To address this issue, this paper proposes a hierarchical approach for modeling and evaluating the availability of monitoring systems in BRT corridors. The proposed solution employs a Fog-Edge architecture, where Fog units are placed at critical integration stations that act as hubs connecting different transit lines, and Edge units are deployed at regular stations. Availability analysis is conducted through Reliability Block Diagrams (RBDs) to represent the corridor topology and incorporate different operational requirements, such as defining the minimum number of stations that must be active for the corridor to be considered functional. We apply the proposed models to the East/West Corridor in the city of Recife, Brazil, as a set of case studies. Results show that even under a permissive operational scenario, where only the integration stations are required to function, the corridor’s availability is limited to a ceiling of 91.3%. This value translates to more than 30 days of expected annual downtime, indicating that to enhance corridor resilience, investments should focus on improving the individual availability of integration stations. The series’ dependency on these nodes is the dominant factor governing the reliability of the entire monitoring system. The proposed models serve as a quantitative decision-support tool for planners and stakeholders in the design and development of more reliable public transportation systems.