Recent advances in battery technology and the global shift toward sustainable transport have accelerated the adoption of electrified public transit systems. However, the implementation of such systems is often constrained by the need for large battery capacities and the high costs associated with stationary charging infrastructure. This study investigates the potential of Mobile Autonomous Charging Pods (MAPs) which are autonomous mobile charging vehicles as an innovative and cost-effective strategy to support the electrification of high-frequency urban bus lines. Using microscopic simulation for inner-city trunk lines in Stockholm, three charging configurations are evaluated: (i) depot-only charging, (ii) depot charging combined with end-station charging, and (iii) depot charging supported by MAPs. Results show that the MAP-based approach enables a reduction in total battery capacity by up to 67% compared to the depot-only strategy and yields total cost savings of over 7 million USD in total cost of ownership across an 11-year horizon. In addition to reducing capital and grid connection costs, MAPs offer greater operational flexibility and resilience by decentralizing energy delivery and enabling dynamic in-motion or stationary charging. The findings highlight MAPs as a scalable and economically viable solution that complements traditional depot infrastructure, offering a path toward more adaptable and efficient electric public transport networks.

The rise of autonomous electric vehicles (AEVs) presents new challenges and opportunities for an efficient and flexible charging infrastructure. This study proposes a reinforcement learning (RL) based framework for optimizing the control and operation of mobile autonomous charging pods (MAPs) for maintaining the operation of AEVs through dynamic charging. We formulate a time and energy aware Markov Decision Process (MDP) to maximize the energy delivered, and the number of AEVs serviced, while also minimizing energy consumed and increasing efficiency. We integrate this framework with SUMO to enable realistic MAP-AEV interactions. A Proximal Policy Optimization (PPO) algorithm was used to train this MDP and identify the optimal control strategies for initiating, terminating, and balancing the network. The results show that the PPO agent can service around 175 AEVs, with an efficiency of 91.5%, representing a 25% improvement over baseline greedy heuristics. Moreover, the battery capacities of AEVs can also be reduced by up to 26%, without compromising the performance. The simulation results show the potential of the proposed method in providing a flexible, and scalable charging for future transport.

Recent advances in battery technologies and a global push for greener transport have accelerated the development of electrified public transportation systems. Such systems often face challenges due to the need for large battery capacities and the high costs associated with conventional charging infrastructure. This study examines the potential of Mobile Autonomous Charging Pods (MAPs), which are autonomous charging vehicles, as an innovative solution to enhance both the efficiency and costeffectiveness of electric bus operations in urban environments. Using the case of inner-city trunk bus lines in Stockholm and employing a microscopic simulation-based study, three charging scenarios are evaluated: depot charging only, depot combined with end-station charging, and depot plus MAP charging. The results indicate that the integration of MAPs can significantly reduce the required battery capacities and associated infrastructure costs while enhancing the reliability of the service. By facilitating dynamic, on-the-go charging, MAPs offer a sustainable and economically viable alternative for urban electric bus networks.

On-board crowding in public transportation has significant impact on passengers' travel experience. New land-use planning configurations can have wide-ranging crowding effects in the public transportation system. Nevertheless, there is a lack of knowledge on the crowding implications caused by new urban developments. In this study, we propose a method for quantifying the network-wide crowding implications of a new urban development. We apply the method to different kinds of urban developments in terms of type, size, location, proximity to high-capacity public transportation connections as well as socioeconomic characteristics. Size and proximity to a high-capacity connection are highly influential factors in determining the value and the geographical extent of the crowding implications. The analysis proposed in this paper can serve as a tool for the ex-post quantification of the on-board crowding impacts using automated data sources. The insights gained can be utilized in more efficient dimensioning of the supply (service) for newly developed areas as well as for placement of future urban developments accounting for the resulting crowding effects.

The electrification of transportation has emerged as a key focus area over the past decade, driven by the rise of electric vehicles (EVs) and supportive governmental policies. Conventional EV charging solutions, while foundational, face notable challenges such as high infrastructure costs, low flexibility, and underutilization. Simultaneously, emerging transportation modes such as autonomous vehicles, shared mobility, modular systems, and aerial vehicles, introduce additional complexities, demanding more innovative charging solutions. This review emphasizes the potential of charge-on-the-move systems referred to as dynamic charging, as a transformative approach to address these challenges. Dynamic charging enables EVs to recharge while in motion, presenting opportunities to minimize battery sizes, reduce emissions, and optimize operational efficiency. The study critically evaluates state-of-the-art dynamic charging technologies, including their benefits, limitations, and applicability to future mobility systems, while also comparing these solutions based on infrastructure costs, readiness, and scalability. The findings suggest that the future of EV charging will likely involve a hybrid approach, integrating both conventional and dynamic solutions. Key priorities for advancing dynamic charging include developing optimization models for infrastructure deployment, finding the balance between battery size and battery life, establishing interoperability standards, and enhancing energy transfer efficiency while ensuring safety and sustainability. By addressing these research challenges, dynamic charging systems have the potential to redefine EV infrastructure and support the broader transition to sustainable and efficient mobility ecosystems. This review serves as a guide for researchers and planners seeking to align charging technologies with evolving transportation needs.

Recent advances in automation have accelerated the development of autonomous electric vehicles (AEVs), which offer the potential for continuous operation, constrained primarily by the need for recharging. We propose a dynamic charging strategy based on Mobile Autonomous Charging Pods (MAPs), which are battery-equipped electric vehicles capable of transferring energy to AEVs while in motion. We introduce a dedicated simulation framework within the microscopic traffic simulator SUMO, incorporating MAP-specific modules for assignment, navigation, and real-time energy transfer under realistic traffic constraints. We model the behavior of both MAPs and AEVs in a stylized looped network and evaluate system-level performance under various demand and fleet configurations. Key performance indicators include energy consumption, charging efficiency, battery utilization, and reductions in AEV battery capacity requirements. Simulation results demonstrate that MAPs can effectively support continuous AEV operation, achieving up to 14% battery downsizing with minimal infrastructure investment, while also reducing travel time by 7%, relative to fixed charging solutions. This study lays the foundation for simulation-based evaluation of MAP-based dynamic charging as a scalable, flexible, and efficient alternative to fixed charging solutions.

On-board crowding in public transportation has a significant impact on passengers' travel experience. However, there is little knowledge of how different passenger groups contribute to on-board crowding. Empirical knowledge of specific passenger groups' impact on the system facilitates more effective tuning of policy instruments such as new fare structures, dedicated public transportation services, infrastructure investments, and capacity provision. We propose a method to capture the crowding contributions from selected passenger groups by means of smart card data analytics. Two crowding contribution metrics at the passenger journey level are proposed: (1) time-weighted contribution to load factor and (2) maximum contribution to load factor. We apply the proposed method to the multimodal public transportation system of Region Stockholm, Sweden. We demonstrate the method for two groups: school students, and passengers traversing Stockholm's inner city. Our findings indicate that school students and passengers traversing the inner city have similar crowding contributions, utilizing 15 % and 11 % of the seating capacity across all modes during the AM and the PM peak, respectively. The commuter rail network, as well as some of the areas neighboring it, experience on average more than 70 % and 90 % utilization of their seating capacity during the AM peak, by school students and passengers traversing the inner city, respectively.

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.

Recent advances in the development of modular transport vehicles allow deploying multi-purpose vehicles, which enable alternate transport of different demand types. In this study, we propose a novel variant of the pickup and delivery problem, the multi-purpose pickup and delivery problem, where multi-purpose vehicles are assigned to serve a multi-commodity flow. We solve a series of use case scenarios using an exact optimization algorithm and an adaptive large neighborhood search algorithm. We compare the performance of a multi-purpose vehicle fleet to a mixed fleet of single-purpose vehicles. Depending on cost parameters, our findings suggest that in certain scenarios, the total costs can be reduced by an average of 13% when multi-purpose vehicles are deployed, while at the same time reducing total vehicle trip duration and total distance traveled by on average 33% and 16%, respectively. The required fleet size can be reduced by 35% on average when operating multi-purpose vehicles. The results can be used by practitioners and policymakers to determine if the combined service of passenger and freight demand flows with multi-purpose vehicles in a given system will yield benefits compared to existing transport operations.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.