Evaluation of Subway Bottleneck Mitigation Strategies using Microscopic, Agent-Based Simulation

Abstract

Many subway systems operating near capacity face challenges in meeting reliability and level of service targets. This paper examines the effectiveness of various strategies to relieve congestion and increase capacity using a microscopic, agent-based, urban heavy rail simulation model. The Massachusetts Bay Transportation Authority’s (MBTA) Red Line serves as the testbed for the analysis. The Red Line operates very close to its capacity. Bottlenecks on the Red Line and possible strategies to mitigate them are discussed, including skip-stop, station consolidation, and dwell time control. The results show that, compared with the no strategy case, skip-stop and consolidation are effective in reducing runtimes and passenger journey times, increasing train throughput, and maintaining headway regularity during peak periods. Performance under these two strategies is also robust to dispatching irregularity and increases in passenger demand. The dwell time control strategy mitigates congestion and disturbances in operations to some extent, but is less effective and robust.