Seat allocation optimization for railways considering social distancing during the post-pandemic period

Abstract

IntroductionEnsuring social distance is crucial for protecting public health and reducing the risk of COVID-19 transmission, particularly in enclosed spaces like public transport. However, it implies a reduction in seat occupancy and thereby cuts the revenue of railway operators. In the post-pandemic period, increase revenue while ensuring passenger safety is becoming increasingly important for railway operators. MethodsThis study proposes a group seat allocation method (GSAM) to ensure different social distancing requirements between passenger groups from different risk-level areas. An integer linear programming model is established to maximize the operator’s revenue, and an efficient adaptive large neighborhood search (ALNS) algorithm is developed to solve the large-scale problem of interest. ResultsA set of numerical experiments is conducted to validate the proposed model and assess the computational efficiency of the algorithm. The results obtained from a real-word example demonstrate that the GSAM can improve revenue by a considerable 62.64%, while maintaining public health and safety standards, relative to current seat allocation methods. Furthermore, railway operators can adopt varying levels of social distancing measures in response to different stages of the COVID-19 pandemic, such as implementing more rigorous social distancing requirements during the pre-pandemic period to ensure stringent public health measures. ConclusionsThe findings of this study demonstrate that railway operators can increase revenue while also ensuring passenger safety during an epidemic. These results have practical implications for the railway industry, and the study aspires to provide new insights for operators on how to adapt to the COVID-19 pandemic and similar respiratory virus transmissions in the future.