This paper develops a method for analysing the elasticity of travel demand to public transport fares. The methodology utilizes public transport smartcard data for collecting disaggregate full population data about passengers’ travel behaviour. The study extends previous work by deriving specific fare elasticities for distinct socioeconomic (e.g., car ownership and income) groups and public transport modes (metro, trains and buses), and by considering the directionality of the fare change. The case study involves a public transport fare policy introduced by the regional administration of Stockholm County in January 2017, where the zonal fare system for single-trip tickets was replaced by a flat-fare policy. The overall fare elasticity of travel funds is found to be −0.46. User sensitivity grows along with the journey distance. Metro users demonstrate the lowest sensitivity, followed by bus and commuter train riders. Low socioeconomic groups, in particular with respect to car ownership, tend to be less sensitive than the high-factor groups. In addition to the direct effect of changed fares, simplification and unification of the fare scheme appears to have substantially contributed to its attractiveness. The flat fare may allow the geographic disparity of public transport travel to be reduced and new users to be attracted from remote areas who are more prone to own cars.@en

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.@en

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

The maturing of autonomous driving technology in recent years has led to several pilot projects and the initial integration of autonomous pods and buses into the public transport (PT) system. An emerging field of interest is the design of public transport networks operating autonomous buses and the potential to attract higher levels of travel demand. In this work a multi-objective optimization and multi-agent simulation framework is developed to study potential changes in the network design and frequency settings compared to conventional PT systems when autonomous vehicles (AV) systems are deployed on fixed-route networks. During the optimization process multiple deployment scenarios (network configurations and service frequency) are evaluated and optimized considering the operator cost, user cost and infrastructure preparation costs of the system. User-focused network design and operator-focused network design are studied for a real-world urban area in Sweden. The results provide insights into the network design and level of service implications brought about by the deployment of autonomous bus (AB) when those are integrated in route-based PT systems. We show that the deployment of autonomous buses result with a network design that increases service ridership. In the context of our case study this increase is likely to primarily substitute walking.@en

This study investigates the potential of modular vehicle concepts and consolidation to increase the efficiency of urban freight and passenger transport. Modularity is achieved by connecting multiple vehicles together to form a platoon. Consolidation is realized by integrating passenger and freight demand in the routing problem. Vehicles are specific for each demand type but can be connected freely, allowing the transport of multiple demand types in the same platoon. The routing problem formulation considers travel time costs, travel distance costs, fleet size costs, and unserved requests costs. The operations are modeled in a novel modular multi-purpose pickup and delivery problem (MMP-PDP) which is solved using CPLEX and Adaptive Large Neighborhood Search (ALNS). In an extensive scenario study, the potential of the modular vehicle type is explored for different spatial and temporal demand distributions. A parameter study on vehicle capacity, vehicle range and platoon cost saving is performed to assess their influence on efficiency. The experiments indicate a cost saving of 48% due to modularity and an additional 9% due to consolidation. The reduction mainly stems from reduced operating costs and reduced trip duration, while the same number of requests can be served in all cases. Empty vehicle kilometers are reduced by more than 60% by consolidation and modularity. A large-scale case study in Stockholm highlights the practical applicability of the modular transport system. The proposed model and optimization framework can be used by companies and policy makers to identify required fleet sizes, optimal vehicle routes and cost savings due to different types of operation and vehicle technology.@en

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.@en

Holding has been extensively used as control strategy to regulate public transport operations, especially to maintain even headways and prevent buses of the same line to bunch up. Applying holding to multiple lines requires however to deal with the transition between corridor and branching segments. In this study, we introduce a holding criterion for network configurations with lines that operate jointly along a common corridor and then diverge to individual branches serving different urban areas. The proposed holding decision rule accounts for all different passenger groups in the overlapping segment and considers the transition to individual line operation. The holding rule is evaluated using simulation for different demand levels and compositions and is compared with state-of-the-art control schemes for a real-world network. Results show that the proposed multi-line control yields performance improvements along the shared transit corridor as well as at the line level. The performance of the control scheme is affected by the demand composition and we provide indications regarding the conditions under which multi-line control is advisable.@en

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.@en

While social segregation is often assessed using static data concerning residential areas, the extent to which people with diverse background travel to the same destinations may offer an additional perspective on the extent of urban segregation. This study further contributes to the measurement of activity-based social segregation between multiple groups using public transport smart card data. In particular, social segregation is quantified using the ordinal information theory index to measure the income group mix at public transport journey destination zones. The method is applied to the public transport smart card data of Stockholm County, Sweden. Applying the index on 2017–2020 data sets for a selected week, shows significant differences between income groups’ segregation along the radial public transport corridors following the opening of a major rail project in the summer of 2017. The overall slight decrease in segregation over the years can be linked to declining segregation in the city center as a travel destination and its public transport hubs. Increasing zonal segregation is observed in suburban and rural zones with commuter train stations. This method helps to quantify social segregation, enriching the analysis of urban segregation and can aid in evaluating policies based on the dynamics of social life.@en

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.@en

This paper presents a comparative analysis of demand-responsive and fixed-schedule, fixed route operations for a simplified station-based feeder to mass transit scenario. Traffic dynamics, demand-responsive fleet coordination, and the behaviour of individual transit users are represented using a public transit simulation framework. Each operational strategy is simulated for varying levels of demand and two fleet compositions with respect to vehicle capacities and fleet size are compared. The services are evaluated based on resulting passenger waiting times, in-vehicles times and additional waiting time if one is denied boarding a fully occupied vehicle. Results indicate that dividing planned service capacity into larger fleets of smaller vehicles can provide a higher level-of-service to passengers. On an aggregate level, utilizing a fixed operational policy results in shorter and more reliable waiting times for levels of demand where there is slack in service capacity. In scenarios where planned service capacity is sometimes exceeded, the on-demand service provides a more even spatial distribution of passenger waiting times, relative to a fixed service.@en

In transit corridors, multiple lines share a sequence of consecutive stops to provide higher joint frequency in higher demand areas. A key challenge is to coordinate the transition from single line to joint operation. A holding control strategy aimed at minimizing passenger travel times is introduced for lines merging into a shared corridor, accounting for the coordination of vehicle arrivals from the merging lines as well as the regularity of each line. The criterion is tested using an artificial network and a real-world network to analyze the impact of demand distribution and compare cooperative versus single line control. We illustrate how the real-time strategy yields overall passenger gains, depending on the composition of different user groups. Results are assessed based on operation and passenger performance indicators and show that coordination is achieved. When combined with joint control in the common part, the proposed approach achieves consistent network-wide travel time benefits.@en

The paper develops a simulation model and evaluates fixed versus on-demand operational designs of a station-based automated feeder service. The evaluation considers the operational cost and average passenger level-of-service trade-offs as well as distributional differences in waiting times. Two case studies are used to evaluate such trade-offs under different fleet compositions; (1) a simple circular network feeder service; (2) a case based on a real-world coordinated branched service in Stockholm, combining fixed-line services on the trunk portion with a flexible feeder service on the branches. Results for the circular network indicate that there are benefits in utilizing an on-demand operational policy for the lowest and highest demand levels tested. When fixed service capacity is exceeded, it is found that there are potential benefits in on-demand operations with respect to average level-of-service, as well as delivering a more even distribution of passenger waiting times. Results for the real-world case show that combining DRT on branches with fixed services on the trunk improves the overall median waiting times for all DRT scenarios and provides substantial improvements for passengers on the trunk, at the cost of more variable, and less equitable waiting times on the branches. For larger fleet sizes, generalized travel costs are reduced with and without rebalancing and level-of-service provided to branch-to-branch passengers is improved considerably by rebalancing idling vehicles to branch end-stops. The case studies demonstrate the usefulness of the simulation framework in evaluating trade-offs between fixed and on-demand service design variables and their effects on disaggregate level-of-service provided for stop-based feeder services.@en

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.@en

In this paper, we deal with the problem of determining when and where a bus should short-turn on a single bi-directional line in real-time. We formulate a decision rule for when to short-turn among candidate short-turning locations that is based on the objective of minimizing total generalized passenger travel cost including waiting times and forced transfer. Computational results and analysis are provided via a simulation study in BusMezzo, a dynamic, agent-based transit operations and assignment model that represents both vehicle as well as passenger progression. The simulation framework allows us to evaluate the resulting trade-off between passenger costs and transit performance that occur when a decision to short-turn is made. The proposed short-turning strategy is applied to a real-world high-frequency transit line in Stockholm, Sweden.@en