Modelling the effects of real-time crowding information in urban public transport systems

Abstract

Public transport (PT) overcrowding is a notorious problem in urban transport networks. Its negative effects upon travel experience can be potentially addressed by disseminating real-time crowding information (RTCI) to passengers. However, impacts of RTCI provision in urban PT networks remain largely unknown. This study aims to contribute by developing an extended dynamic PT simulation model that enables a thorough analysis of instantaneous RTCI consequences. In the model, RTCI is generated and disseminated across the network, and then utilised in passengers' sequential en-route choices. A case-study demonstration of the RTCI algorithm on urban PT network model of Kraków (Poland) shows that instantaneous RTCI has the potential to improve passengers' travel experience, although it is also susceptible to inaccuracy. RTCI provision can yield total travel utility improvements of 3% in typical PM peak-hour, with reduced impacts of the worst overcrowding effects (in terms of denied-boarding and in-vehicle travel disutility in overcrowded conditions) of 30%. Highlights: • Real-time crowding information (RTCI) is an increasingly feasible solution in public transport. • We introduce a novel framework for modelling the network effects of instantaneous RTCI. • Instantaneous RTCI can result in improved travel experience but also substantial inaccuracy risk. • Reduced impacts of the worst overcrowding experience amount to up to 30%.

Sustainable mobility in auto-dominated Metro Boston: Challenges and opportunities post-COVID-19

“…Real-time crowding information is being provided to riders on busy routes through the MBTA website, e-ink digital signs at bus stops, and on the MBTA-endorsed Transit app. 21 This is expected to provide a much needed confidence boost to riders who can choose to adjust their travel plans based on the available information and enjoy an improved travel experience, as demonstrated by Drabicki et al (2020) . Contactless payments are already possible through Charlie Cards, which are reusable cards that can be loaded with cash value or passes at the MBTA website or station kiosks to pay transit fares.…”

Pandemic impacts on public transport safety and stress perceptions in Nordic cities

“…Part of this is managing the potentially heightened discomfort that people may feel with regard to crowdedness after the pandemic. Real time crowding information (RTCI) at stations and in travel apps may help PT travellers to prepare for and anticipate to this ( Drabicki et al, 2021 ), for example by guiding them how to choose or skip departures or strategically position themselves on platforms.

Figure 3 Long-term post-pandemic mobility scenarios and the challenges and tasks for PT

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Mitigating bus bunching with real-time crowding information

“…WTW rate induced by predictive RTCI) eventually leads to much lower overcrowding experience and improved on-board comfort. Drabicki et al (2021b) develop a modelling framework of instantaneous RTCI effects upon route (path) choices. The model, embedded within BusMezzo, a dynamic transit assignment simulation model, assumes that RTCI is generated based on latest reported crowding levels of downstream line segments, and then utilized by passengers to evaluate route choice probabilities.…”

“…The BusMezzo model assumes an explicit, disaggregate representation of PT system, which enables in-depth analysis of the PT overcrowding (Cats et al 2016) and PT capacity reductions' (Cats and Jenelius 2018) effects on passengers' travel experience and network performance. It has been also applied for analysing the impacts of real-time information (RTI) concerning subsequent vehicle arrivals on travel times (Cats et al 2011a;Cats and Jenelius 2014) and real-time crowding information (RTCI) on instantaneous route choices (Drabicki et al 2021b). Furthermore, BusMezzo is a dynamic assignment model, where PT network performance is an outcome of sequential and mutual interactions between supply side (individual vehicle movements) and demand side components (passengers' travel actions).…”

“…We build on the modelling framework presented in Drabicki et al (2021b) and proof-ofconcept analysis in Drabicki et al (2021a), extending them to cover the broad spectrum of corridor-specific dynamics of boarding decisions. We model the RTCI effects on boarding choices as part of a dynamic PT assignment model with a three-step framework (Fig.…”