Ensuring Safety and Resilience in Electric Bus Systems via an Integrated Framework for Comprehensive Risk Analysis

Nowadays, many bus transit organizations worldwide aspire to remove carbon emissions entirely by progressively introducing electrified fleets rather than lowering these hazardous air pollutants with alternative fuel vehicles. In tandem with the growth of the electric bus market, bus transportation systems have seen the rise of new technologies, notably battery-powered electric buses (BEBs). The advancement of such technology has brought about the need for bus fleet operators to ensure their overall safety and security performance throughout their daily operations. To this end, the primary goal of this study is to identify and prioritize risk factors for public bus transit agencies to confirm that BEBs and all related systems are secure for zero-emission transport operations. In this regard, a novel risk assessment framework composed of COmparisons Between RAnked Criteria (COBRAC) and Alternative Ranking Technique based on Adaptive Standardized Interval (ARTASI) methods based on Safety and Critical Effect Analysis (SCEA) in the spherical fuzzy (SF) context was proposed for BEB fleets risk analysis. SF-COBRAC was employed to provide the weights of risk parameters, including the probability of occurrence (P), the severity of occurrence consequence (S), the undetectability of occurrence (U), and the frequency of occurrence (F) obtained from the SCEA method. In contrast, the ranking of the related risk factors was determined via the SF-ARTASI approach. A case study for Istanbul with four risk parameters and six main and 26 sub-risk factors organized in a three-level hierarchy was executed to illustrate the efficacy and applicability of the suggested risk assessment technique. The primary study outcomes indicated that the method was substantially viable in a rigorous appraisal and could direct urban fleet providers in the BEB systems risk analysis. Finally, the designed generic approach may be easily modified to handle sophisticated multiple-criteria decision-making scenarios in a spherical fuzzy domain.