One of the most common motivations for public transport investments is to reduce congestion and increase capacity. Public transport congestion leads to crowding discomfort, denied boardings and lower service reliability. However, transit assignment models and appraisal methodologies usually do not account for the dynamics of public transport congestion and crowding and thus potentially underestimate the related benefits. This study develops a method to capture the benefits of increased capacity by using a dynamic and stochastic transit assignment model. Using an agent-based public transport simulation model, we dynamically model the evolution of network reliability and on-board crowding. The model is embedded in a comprehensive framework for project appraisal. A case study of a metro extension that partially replaces an overloaded bus network in Stockholm demonstrates that congestion effects may account for a substantial share of the expected benefits. A cost-benefit analysis based on a conventional static model will miss more than a third of the benefits. This suggests that failure to represent dynamic congestion effects may substantially underestimate the benefits of projects, especially if they are primarily intended to increase capacity rather than to reduce travel times.

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This paper performs an ex-post cost-benefit and distribution analysis of the Gothenburg congestion charges introduced in 2013, based on observed effects and an ex-post evaluated transport model. Although Gothenburg is a small city with congestion limited to the highway junctions, the congestion charge scheme is socially beneficial, generating a net surplus of €20 million per year. From a financial perspective, the investment cost was repaid in slightly more than a year and, from a social surplus perspective, is repaid in\ 4 years. Still, the sums that are redistributed in Gothenburg are substantially larger than the net benefit. In the distribution analysis we develop an alternative welfare rule, where the utility is translated to money by dividing the utility by the average marginal utility of money, thereby avoiding putting a higher weight on high-income people. The alternative welfare rule shows larger redistribution effects, because paying charges is more painful for low-income classes due to the higher marginal utility of money. Low-income citizens pay a larger share of their income because all income classes are highly car dependent in Gothenburg and workers in the highest income class have considerably higher access to company cars for private trips. No correlation was found between voting pattern and gains, losses or net gain.

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The distribution of passenger demand over the transit network is forecasted using transit assignment models which conventionally assume that passenger loads satisfy network equilibrium conditions. The approach taken in this study is to model transit path choice as a within-day dynamic process influenced by network state variation and real-time information. The iterative network loading process leading to steady-state conditions is performed by means of day-to-day learning implemented in an agent-based simulation model. We explicitly account for adaptation and learning in relation to service uncertainty, on-board crowding and information provision in the context of congested transit networks. This study thus combines the underlying assignment principles that govern transit assignment models and the disaggregate demand modeling enabled by agent-based simulation modeling. The model is applied to a toy network for illustration purposes, followed by a demonstration for the rapid transit network of Stockholm, Sweden. A full-scale application of the proposed model shows the day-to-day travel time and crowding development for different levels of network saturation and when deploying different levels of information availability.