Increasing the operating speed in public transport systems can increase the system capacity, reduce the overall passenger travel time and improve experienced comfort. Skip-stop operation, where subsets of the trains operating on the same tracks skip certain intermediate stops, can accelerate the service and improve passengers’ overall travel experience. This paper considers the problem of deciding whether skip-stop operation is beneficial for a given line and which stopping scheme is the most effective. In particular, we investigate whether a simple decision rule for determining the stopping pattern under a skip-stop strategy, derived from the expected weighted time benefits to the passengers, can reliably determine the most suitable skip-stop scheme. To evaluate the impact of alternative stop-skipping strategies, we adopt the existing public transit assignment model BusMezzo, which allows for a realistic representation of passengers’ experienced waiting and in-vehicle travel times and the resulting trade-offs between passenger costs and benefits. The decision rule is applied to a set of high-frequency urban rail lines in Stockholm, Sweden. We show that a simple decision rule may not be a robust way of determining a beneficial skip-stop scheme. The results from the case study reveal that the skip-stop operation can have an overall positive impact on passenger generalized travel time but only under certain conditions at the stops along the line.

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Overcrowding has become a big challenge for public transport systems, affecting passengers’ travel experience. At the same time, service supply is often underutilized due to large variations in crowding across services, vehicle trips on the same service and different compartments of the same vehicle. Real-time operational measures, such as information provision, can potentially reduce on-board crowding unevenness and its negative effects. In this study, we extend a dynamic public transport simulation model to provide passengers with predictive real-time crowding information (RTCI) concerning individual train cars. Passengers utilize this information when choosing a specific train car to board. It is demonstrated through a case study for the Stockholm metro network area that in the presence of car-specific crowding information, passengers alter their car boarding choices to avoid on-board crowding, leading to a more even passenger distribution inside trains. We find that passengers’ travel experience improves with the provisioning of RTCI, which is a result of the lower on-board crowding unevenness. Moreover, this improvement increases with increased demand levels but only up to a certain point beyond which passengers do not gain from switching train cars.

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As travel demand grows in many cities around the world, overcrowding in public transport systems has become a major issue and has many negative effects for both users and operators. Measures to address on-board congestion span from large-scale strategic investments (e.g. increasing infrastructure capacity), through tactical planning (e.g. stopping pattern) to real-time operational measures (e.g. information provision, gate and escalator control). Thus there is a need to evaluate the impact of these measures prior to their implementation. To this end, this study aims at capturing the effective capacity utilization of the train, by considering passengers' distribution among individual train cars into an agent-based simulation model. The developed model is validated and applied to a case study for the Stockholm metro network. The findings suggest that an increase in peak hour demand leads to a more even passenger distribution among individual train cars, which partially counteracts the increased disutility caused by the higher passenger volumes. Interestingly, the closure of the most popular entrance point at one of the stations leads to lower train crowding unevenness at the downstream stops and consequently reduces passengers' experienced discomfort. We find that the user cost is significantly underestimated when passenger distribution among cars is not accounted for.

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We propose a methodology based on multiple automated data sources for evaluating the effects of station layout, arriving traveler flows, and platform and on-board crowding on the distribution of boarding passengers among individual cars of metro trains. The methodology is applied to a case study for a sequence of stations in the Stockholm metro network. The findings suggest that passengers opt for less crowded train cars in crowded situations, trading-off walking and in-vehicle crowding while waiting and riding. We find that the boarding car distribution is also affected by the locations of platform access points and the distribution of entering traveler flows. These insights may be used by transit planners and operators to increase the understanding of how passengers behave under varying crowding conditions, identify the factors that affect travelers' choice of metro car and eventually reduce experienced on-board crowding and increase the capacity utilization of the trains through investments in infrastructure or operational interventions.

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The paper proposes a methodology based on multiple automated data sources for evaluating the eects of station layout, arriving traveller flows, and platform and on-board crowding on the distribution of boarding passengers in individual cars of a metro train. The methodology is applied to a case study for a sequence of stations in the Stockholm metro network. While train car loads are generally skewed towards the leading cars, results indicate that a crowded arriving train is associated with increasing boarding shares in the middle and rear cars. Moreover, higher platform crowding is found to have a positive signicant eect on the boarding share in the middle car. We nd that the boarding car distribution is also aected by the locations of entrances and the distribution of entering traveller ows. The insights may be used by transit planners and operators to increase the understanding of how passengers behave under crowding conditions and identify the factors that aect travelers' metro car choice.@en