Optimal congestion control strategies for near-capacity urban metros: Informing intervention via fundamental diagrams

Anupriya,, Daniel J. Graham, Prateek Bansal, Daniel Hörcher and Richard Anderson

Physica A: Statistical Mechanics and its Applications, 2023, vol. 609, issue C

Abstract: Congestion; operational delays due to a vicious circle of passenger congestion and train-queuing; is an escalating problem for metro systems because it has negative consequences from passenger discomfort to eventual mode shifts. Congestion arises due to large volumes of passenger boardings and alightings at bottleneck stations, which may lead to increased stopping times at stations and consequent queuing of trains upstream. Such queuing further reduces line throughput and implies an even greater accumulation of passengers at stations. Alleviating congestion requires control strategies such as regulating the inflow of passengers entering bottleneck stations. The availability of large-scale smartcard and train movement data from day-to-day operations facilitates the development of models that can inform such strategies in a data-driven way. In this paper, we propose to model station-level passenger congestion via empirical passenger boarding–alightings and train flow relationships, henceforth, fundamental diagrams (FDs). We emphasise that estimating FDs using station-level data is empirically challenging due to confounding biases arising from the interdependence of operations at different stations, which obscures the true sources of congestion in the network. We thus adopt a causal statistical modelling approach to produce FDs that are robust to confounding and as such suitable to inform control strategies. The closest antecedent to the proposed model is the FD for road sections, which informs traffic management strategies, for instance, via locating the optimum operation point. Our analysis of data from the Mass Transit Railway, Hong Kong indicates the existence of concave FDs at identified bottleneck stations, and an associated critical level of boarding–alightings above which congestion sets in unless there is an intervention.

Keywords: Urban rail; Congestion; Causal statistical modelling; Bayesian machine learning; Nonparametric statistics (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122009487

DOI: 10.1016/j.physa.2022.128390

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