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.

Virus Transmission Risk in Urban Rail Systems: Microscopic Simulation-Based Analysis of Spatio-Temporal Characteristics

“…To apply the framework described in Figure 2, the model derived in the previous section is combined with an urban rail simulation model (SimMETRO) to assess the risk of transmission. SimMETRO is a microscopic, agent-based stochastic simulation model ( 25 , 26 ). It is detailed and designed for heavy rail system performance analysis, operations planning, signaling system evaluation, real-time control strategies evaluation and refinement, and operator-in-the-loop training.…”

“…A detailed dwell time model is used to determine a train’s dwell times at stations as a function of the number of boarding and alighting passengers. SimMETRO has been calibrated and validated in previous studies ( 26 , 27 ) using calibration and validation methods reported in the literature ( 26 – 29 ).…”

“…Detailed data at the train and passenger levels are generated by the simulation model, such as train runtimes, headways, dwell times, train load at each segment, individual passenger journey times, wait times, and denied boarding due to capacity constraints. The simulation model has been validated in previous studies [24].…”

Prioritizing Subway Station Entrance Attributes to Optimize Passenger Satisfaction in Cold Climate Zones: Integrating Gradient Boosting Decision Trees with Asymmetric Impact-Performance Analysis

“…The orientation of the entrances affects passenger flow and station accessibility, as well as lighting and ventilation [19]. The size of the portal, or entrance and exit dimensions, directly impacts congestion levels and passenger throughput [20]. Navigational clarity is crucial in subway environments [21].…”