Abstract:
Understanding the resilience of the railway network in Bangladesh in response to natural disasters is a major concern in the face of increasing calamities caused by climate change, as it plays a crucial role in mobilizing this densely populated country. This study aimed to assess the vulnerability of the railway network to extreme weather events like floods, storm surges, and sea level rise, while also identifying optimal recovery strategies. The study evaluated the resilience of Bangladesh Railway utilizing a network science-based approach. To construct the network, stations where inter-city trains stop are considered as nodes and the connections between these stations are defined as links. Network topological parameters such as degree, centralities, shortest path, and global efficiency, which is an indicator of network connectivity as well, were obtained by numerically analyzing the network. The resilience is further obtained by integrating the changes in global efficiency during recovery following a disruption. Here, disruption refers to the inundation of a component when the water level rises over 3.5m above ground. Twelve future disruption scenarios covering flood, storm surge, and sea level rise, are simulated using projected climate data. Assuming all disrupted components are equally available for recovery after the receding of water, the effectiveness of various recovery sequences based on network and station attributes is compared. Furthermore, the study investigated how recent network extensions might affect resilience and recovery.
The resiliency of the Bangladesh railway network against the increasing natural disasters due to climate change has been found inadequate from the analysis. The central region is highly vulnerable to floods in Brahmaputra basin over the next 50 to 100 years. Additionally, while less imminent than floods, storm surges of 100 years return period pose a significant threat to the southern portion of the network. However, the network's strategic avoidance of coastal areas might benefit its resilience to long-term sea level rise. The findings also highlight that for large-scale disruptions like nationwide floods, centrality measures, particularly closeness centrality, provide a strong foundation for recovery strategies. Nevertheless, the optimal approach to recovery sequences can vary depending on the scale of the disruption and the network characteristics of the affected region. Overall, the outcomes of this study may help to plan a recovery strategy for future disasters even before their occurrence.