The boundary between random and non-random passenger arrivals: Robust empirical evidence and economic implications

Singh, Ramandeep; Graham, Daniel J.; Hörcher, Dániel; Anderson, R. J.

doi:10.1016/j.trc.2021.103267

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…From the initialisation of the simulation time, passenger arrivals begin at station platforms. Passengers are assumed to follow a random Poisson arrival process as is conventional for high frequency transit networks (refer to the authors' previous work 27 and references therein). When trains stop at stations, passengers board and alight during the specified dwell time subject to train capacity and boarding rate constraints.…”

Section: Methodsmentioning

confidence: 99%

An evaluation framework for operational interventions on urban mass public transport during a pandemic

Singh

Hörcher

Graham

2023

Sci Rep

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Decision making in a rapidly changing context, such as the development and progression of a pandemic, requires a dynamic assessment of multiple variable and competing factors. Seemingly beneficial courses of action can rapidly fail to deliver a positive outcome as the context changes. In this paper, we present a flexible data-driven agent-based simulation framework that considers multiple outcome criteria to increase opportunities for safe mobility and economic interactions on urban transit networks while reducing the potential for Covid-19 contagion in a dynamic setting. Using a case study of the Victoria line on the London Underground, we model a number of operational interventions with varied demand levels and social distancing constraints including: alterations to train headways, dwell times, signalling schemes, and train paths. Our model demonstrates that substantial performance gains ranging from 12.3–195.7% can be achieved in metro service provision when comparing the best performing operational scheme and headway with those realised on the Victoria line during the pandemic.

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Section: Methodsmentioning

confidence: 99%