Modelling public transport on-board congestion: comparing schedule-based and agent-based assignment approaches and their implications
2016 (English)In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, Vol. 50, no 6, 1209-1224 p.Article in journal (Refereed) Published
Transit systems are subject to congestion that influences system performance and level of service. The evaluation of measures to relieve congestion requires models that can capture their network effects and passengers' adaptation. In particular, on-board congestion leads to an increase of crowding discomfort and denied boarding and a decrease in service reliability. This study performs a systematic comparison of alternative approaches to modelling on-board congestion in transit networks. In particular, the congestion-related functionalities of a schedule-based model and an agent-based transit assignment model are investigated, by comparing VISUM and BusMezzo, respectively. The theoretical background, modelling principles and implementation details of the alternative models are examined and demonstrated by testing various operational scenarios for an example network. The results suggest that differences in modelling passenger arrival process, choice-set generation and route choice model yield systematically different passenger loads. The schedule-based model is insensitive to a uniform increase in demand or decrease in capacity when caused by either vehicle capacity or service frequency reduction. In contrast, nominal travel times increase in the agent-based model as demand increases or capacity decreases. The marginal increase in travel time increases as the network becomes more saturated. Whilst none of the existing models capture the full range of congestion effects and related behavioural responses, existing models can support different planning decisions.
Place, publisher, year, edition, pages
Wiley-Blackwell, 2016. Vol. 50, no 6, 1209-1224 p.
capacity, congestion, model comparison, network assignment, public transport, simulation model, transit networks, Autonomous agents, Computational methods, Mass transportation, Traffic control, Transportation, Travel time, Traffic congestion
Transport Systems and Logistics
IdentifiersURN: urn:nbn:se:kth:diva-195286DOI: 10.1002/atr.1398ISI: 000386040200016ScopusID: 2-s2.0-84978967672OAI: oai:DiVA.org:kth-195286DiVA: diva2:1045977
QC 201611112016-11-112016-11-022016-11-17Bibliographically approved