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.

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 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 cost-effectiveness 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.

New land-use planning configurations can have wide-ranging crowding effects on the public transportation system, given the ongoing increase in urban agglomerations worldwide. In this study, we propose a method for quantifying the network-wide crowding implications of new developments accounting for their socioeconomic and planning characteristics. Size and proximity to a high-capacity connection are highly influential factors in determining crowding implications’ extent and geographical spread. Interestingly, the income level can have a twofold effect on crowding contributions (increase or decrease). The proposed method can serve as a tool for the ex-post quantification of the crowding impacts using automated data sources.

Crowding exposure in public transport vehicles has a serious negative impact on passengers’ travel experience. Especially during the COVID-19 pandemic, exposure to crowded conditions may also increase the risk of virus transmission among passengers. To mitigate the negative impact of crowding on public transport systems, there is an increasing need to understand how crowding exposure is distributed across the service area and passenger groups, and how it changes over time. This paper provides a methodology for monitoring the equity of crowding exposure over time using longitudinal smart card data. An objective measure is proposed to quantify crowding exposure: relative crowded travel time (rCTT). We apply Lorenz curves, Gini and Suits coefficients to assess horizontal equity (across the population) and vertical equity (considering income). In our case study of the Stockholm Region, we demonstrate our method by assessing the equity of crowding exposure during different stages of the COVID-19 pandemic: pre-COVID, COVID, and post-COVID. Our findings show that the pandemic negatively impacted both horizontal and vertical equity. During the pandemic, crowding exposure became increasingly uneven across the service area. While overall ridership and crowding exposure declined during the pandemic, reductions were not uniform across social groups. Lower-income riders showed smaller decreases in travel compared to higher-income riders, resulting in greater crowding exposure among disadvantaged groups and a shift from a progressive to a regressive distribution. These findings reinforce the importance of continued monitoring of crowding exposure, especially as travel behavior and policy contexts continue to evolve. The proposed framework can help identify and target the most critical equity gaps, enabling more focused and effective interventions.with lower-income travelers experiencing more crowding than their higher-income counterparts. However, by the post-COVID stage, the equity of crowding exposure has nearly returned to pre-COVID levels.

Providing relevant information to passengers is essential for the functioning of the public transport system. With the digitalisation of passenger information systems (PIS), passengers currently have access to large amounts of information. To avoid cognitive overload among passengers, public transport systems experiment with applying personalisation to PIS, allowing for the provision of tailored information according to the needs and desires of passengers. Notwithstanding, systematic definitions and guidelines for designing personalised PIS in public transport are currently lacking. We, therefore, introduce a framework for assessing the personalisation levels of PIS, to close the gap between theoretical conceptualisations and practical implementations of PIS. Our framework defines five levels of personalisation, which are substantiated by a review of 40 papers focusing on personalisation in PIS.

Providing relevant information to passengers is essential for the functioning of the public transport system.With the digitalization of passenger information systems (PIS), passengers currently have access to largeamounts of information. To avoid cognitive overload among passengers, public transport systems experimentwith applying personalization to PIS, allowing for the provision of tailored information according tothe needs and desires of passengers. Notwithstanding, systematic definitions and guidelines for designingpersonalized PIS in public transport are currently lacking. We, therefore, introduce a framework for assessingthe personalization levels of PIS, to close the gap between theoretical conceptualizations and practicalimplementations of PIS. Our framework defines five levels of personalization, which are substantiated by areview of 40 papers focusing on personalization in PIS.