Simulation-Based Method of Capacity Utilization Evaluation to Account for Uncertainty in Recovery Time

Abstract

A typical objective of a railway agency is to design an efficient and reliable service plan. Although enhancing the level of capacity utilization may increase efficiency, the stability of the service plan may also decrease due to stochastic railway disruptions. Past studies used recovery time to evaluate the stability of a service plan; however, the variation of recovery time was not fully examined. Therefore, we developed a Monte Carlo simulation framework to address this issue and proposed four capacity-based indices for the evaluation of service efficiency and stability. These indices are capacity utilization efficiency, mean recovery time, dispersion of recovery time, and probability of unacceptable recovery time. The latter two new indices explored the variation of recovery time and probability of unacceptable long recovery time, which is undesired for railway operation. We conducted a real-world case study on the Taiwan Railways Administration before and after the service plan revision. Consequently, the northbound service plan needed a deliberate review and revision before implementation, whereas the southbound plan successfully increased capacity usage while not necessarily worsening stability. This result demonstrated that our study introduced uncertainty analysis into the evaluation framework and provided flexible information on the service plan. These developments can provide improved support in the decision-making of railway agencies to strike a balance between service level and asset usage.

Extending UIC 406-based capacity analysis – New approaches for railway nodes and network effects

“…Additional complexity enters as the number of additional trains in saturation methods also depends on whether trains are inserted randomly or in a heuristically optimized manner. Another approach is presented by Lai et al (2018), where capacity utilization in the presence of disruptions is assessed and capacity utilization efficiency and recovery times are introduced as additional capacity metrics.…”