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.

Introduktion

Delade elsparkcyklar är nu en vanlig syn i många städer i Sverige, men färdmedlet betraktas fortfarande som ett nytt inslag i gatubilden. Förespråkare för elsparkcyklar lyfter ofta fram färdmedlet för dess potential att lösa kollektivtrafikens problem med first/last mile. Det finns dock fortfarande betydande kunskapsluckor kring multimodala resor som kombinerar delade elsparkcyklar och kollektivtrafik. Den här studien undersöker vilka elsparkcykelanvändare i Stockholm, Göteborg och Malmö som gör multimodala resor.

Metod

En webbaserad enkät distribuerades till elsparkcykelanvändare i Stockholm, Göteborg och Malmö under hösten 2022. Den ursprungliga studien genomfördes för att undersöka attityder och uppfattningar om det nationella parkeringsförbud som infördes den 1 september 2022. Förutom frågor om utformningen av parkering för elsparkcyklar och förändringar i resebeteende, fick respondenterna också frågor om kombinerade resor med delade elsparkcyklar och kollektivtrafik under de senaste sex månaderna. Dessutom samlade vi in information om demografiska och socioekonomiska faktorer hos elsparkcykelanvändarna. Totalt samlades in 965 svar för Stockholm, 145 för Göteborg och 159 för Malmö. Genom att uppskatta logistiska och ordinala regressionsmodeller för varje stad visade vi både vilka användargrupper som har gjort first- och last-mile-resor med delade elsparkcyklar samt vilka som gjort det i störst utsträckning sett till andel av resor.

Resultat

I Stockholm hade användare med högre användningsgrad av elsparkcyklar en högre sannolikhet att ha gjort en first- eller last-mile-resa under de senaste sex månaderna. Användare med högre inkomster och de som bor i stadens centrum hade en lägre sannolikhet att både ha gjort en multimodal resa samt att göra det i stor utsträckning sett till deras totala antal resor. Även i Göteborg hade användare som bodde utanför centrum en högre sannolikhet för att göra en kombinerad resa med kollektivtrafik. I Malmö hade användare som använde delade elsparkcyklar varje vecka en högre sannolikhet att ha använt delade elsparkcyklar för att ansluta till eller från kollektivtrafik, medan användare över 30 års ålder hade en lägre sannolikhet. Vi kan dra slutsatsen att resultaten inte kan generaliseras, eftersom olika grupper gör multimodala resor i alla tre städer. Vi kan också dra denna slutsats när vi jämför våra resultat med andra studier.

Slutsats

Våra resultat visar att delade elsparkcyklar kan spela olika roller för olika användargrupper. För yngre användare, med lägre inkomst och som bor utanför stadens centrum, kan delade elsparkcyklar ha en större kompletterande roll med kollektivtrafik. Detta belyser den roll som delade elsparkcyklar har i det större transportsystemet och den potential som färdmedlet har. I takt med att behovet av hållbara alternativ till privatbilismen växer finns det ett behov av att ytterligare förbättra och främja kollektivtrafiken, och resultaten från denna studie blir alltmer relevanta.

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.

As shared e-scooter systems have been introduced globally, many cities encounter issues with clutter caused by improperly parked vehicles. In response, cities have started implementing parking regulations. Based on a user survey, this study utilizes binary and ordinal logistic regression to analyze user attitudes towards regulated parking in designated zones, changes in ridership following policy implementation, and the key factors influencing these two aspects in Stockholm and Malmö, Sweden. Users in Stockholm and Malmö showed similarly mixed attitudes towards the parking regulations, despite Stockholm having many more designated parking zones. The majority of users in both cities reported using shared e-scooters less frequently after the introduction of the parking policy. The density of parking zones was the most consistent factor across all models, influencing user attitudes and decline in ridership in both Stockholm and Malmö. Other factors influencing attitudes and declines in ridership include the ability to park close to destinations, perceived longer walking distances, multimodal trips, and ease of finding parking. Notably, users in Stockholm who combined shared e-scooters with public transportation were more negative towards the parking policy, possibly due to insufficient parking facilities near transit stops. To increase user acceptance and encourage multi-modal use, planners should ensure availability not only near public transport but also near the trip’s final destination, emphasizing the need for high overall parking density.

Accurately predicting engine-out nitrogen oxides (NOx) emissions on-board is crucial for effective emission control in heavy-duty engines. Real-world engine operating conditions, especially in non-road applications with frequent dynamic changes, can significantly affect NOx emission characteristics. However, these engine emission characteristics are conventionally measured on steady-state or regulated driving cycles, which may not fully reflect the emission levels under real-world operational dynamics. This highlights the necessity of integrating engine performance during transient operation into the NOx prediction model to enhance the accuracy of on-board predictions. This paper introduces a novel data-driven model to predict engine-out NOx emissions during the construction activities of a wheel loader. This paper begins by addressing discrepancies between steady-state map predictions and on-board NOx measurements. To bridge these gaps, the model identifies engine transient operating conditions by analyzing the time derivatives of engine speed and torque. The model structure integrates steady-state and transient emission maps, with the transient map being iteratively refined using the Kalman filter principle, thereby improving its accuracy and robustness in response to engine dynamics. The proposed method maintains a model structure that is easily implemented and similar to conventional steady-state emission maps, while also enabling online self-learning for model parameter updates. Model validation shows that the model has high prediction accuracy and the ability to differentiate between steady-state and transient engine working conditions during construction activities.

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.

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.