The energy impact of the transportation sector is consistently increasing. The European Union experienced a growth rate of 100% between 1990 and 2018, and current projections indicate that this growth trend is expected to persist over the next decade. In particular, the railway sector impacts a significant portion (up to 280 TWhel per year worldwide), and the energy consumption of HVAC systems has been found to be significant among various services provided on board, such as traction and auxiliaries, accounting for as much as 30% of the total energy requirements. This phenomenon can be attributed to the growing focus of stakeholders on ensuring a satisfactory thermal comfort experience while on board. In this context, the current manuscript suggests an innovative method for increasing the passengers' indoor thermal comfort while also taking the associated energy, economic, and environmental considerations into account. Specifically, it consists of an advanced comfort-optimal HVAC system control logic that prioritizes comfort, utilizing comfort-optimized indoor air setpoints. This innovative methodology surpasses the rule-based control logic proposed by the existing standards. To analyse the described control logic, a novel mathematical model for the energy, economic, and environmental performance analyses of modern train-HVAC systems has been developed. The simulation tool, validate through a code-to-code procedure, has the capability to incorporate weather data associated with actual railway paths, based on their location and orientation. To prove the potential of the proposed method and the capabilities of the developed dynamic simulation tool, a suitable proof-of-concept analysis has been conducted. In particular, for an existing railway coach operating in Italy, the standard and the innovative HVAC system control logics have been tested and compared in terms of energy, economic, environmental, and thermal comfort performance. The results of the analysis show that interesting thermal comfort hours increase (≃+1000 hours) and energy savings (-27%) can be achieved on yearly basis, proving the potential benefits achievable by the proposed method, and the capabilities of the developed tool.