We introduce and demonstrate a simple and efficient method for simulating mixtures of legacy and autonomous trains. The method generalises an earlier simulation that we developed for legacy-only operations, in which trains run according to fixed-block signalling rules. Autonomous trains, which use moving-block signalling rules, are incorporated into this framework by employing an overlapping set of short virtual fixed-blocks. Safe occupancy is then maintained by using shadowing rules that link the two sets of blocks. The paper gives relevant rail background, details of the proposed simulation rules, and demonstrates exemplar solution trajectories. The simulation technique is validated both in terms of maintaining safe occupancy and providing a close approximation of the true continuous-space dynamics of autonomous leader-follower pairs. At the Dresden meeting, a variety of interesting mixed-fleet capacity results will also be presented.

Connected and automated vehicles (CAVs) will behave fundamentally differently than human drivers. In mixed traffic, this could lead to inefficiencies and safety-critical situations since neither human drivers nor CAVs will be able to fully anticipate or predict surrounding traffic dynamics. Thus, some researchers proposed to separate CAVs from conventional vehicles by dedicating exclusive lanes to them. However, the separation of road infrastructure can negatively impact the system’s capacity. While the effects of CAV lanes were addressed for freeways, their deployment in urban settings is not yet fully understood. This paper systematically analyzes the effects of CAV-lanes in an urban setting accounting for the corresponding complexities. We employ microscopic traffic simulation to model traffic flow dynamics in a detailed manner and to be able to consider a wide array of supply-related characteristics. These concern intersection geometry, public transport operation, traffic signal control, and traffic management. Our study contributes to the existing literature by revealing the potential of CAV lanes in an urban setting while accounting for the behavioral and topological complexities. The results of this study can support decision-makers in the design of future urban transportation systems and to prepare cities for the upcoming era of automation in traffic.

A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real-time and is therefore uncertain in advance. However, there was an assumption that the traffic signal is initially red and turns to green, which means that only half traffic light cycle was considered. In this work, the aforementioned problem is extended considering a full traffic light cycle, consisting of four phases: a certain green phase, during which the vehicle can freely pass; an uncertain green phase, in which there is a probability that the traffic light will extend its duration or turn to red at any time; a certain red phase during which the vehicle cannot pass; and an uncertain red phase, in which there is a probability that the red signal may be extended or turn to green at any time. It is demonstrated, based on preliminary results, that the proposed SDP (Stochastic Dynamic Programming) approach achieves better average performance, in terms of fuel consumption, compared to the IDM (Intelligent Driver Model), which emulates human-driving behavior.

This paper presents results from a workshop focusing on human demands of mobility that was conducted during the MFTS conference 2022. It shows, how the international participants, most of them male researchers with a background in engineering, dealt with concepts and findings from mobility research conducted by scholars of social sciences, humanities and cultural studies that focus on human mobility demands including gender and diversity aspects.

Introduction: With the beginning of the COVID-19 outbreak and the restrictions put in place to prevent an uncontrolled spread of the virus, the circumstances for daily activities changed. A remarkable shift in the modal split distribution was observed [Ank21]. Moreover, the changes in mobility during the COVID-19 pandemic had multiple impacts on road traffic [Yas21]. By now, several researchers have looked at the impact of COVID-19 as a disruptive event on mobility behaviour. This workshop within the 4th Symposium on Management of Future Motorway and Urban Traffic Systems aimed to discuss insights from these research projects and how they enable experts to transfer this newfound knowledge to future disruptive events such as climate change, rising energy costs and events related to a possible energy transition. Thus, the research question this workshop investigated reads as follows: What can we learn from the pandemic to be able to predict how different future disruptive events can shape the mobility of tomorrow?

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Den NSU als Komplex (#NSU-Komplex) zu begreifen, bedeutet, dessen gesellschaftliche Entstehungsbedingungen zu verstehen und aus den Erkenntnissen Schlüsse für gelingende Präventionsarbeit zu ziehen. Das zentrale Ziel, das wir in diesem Methodenhandbuch für die Bildungsarbeit verfolgen, ist das Kennenlernen und die Sichtbarkeit der Betroffenenperspektiven nach dem Ansatz „Zuhören, Vertiefen, Aktiv werden“ von Ayşe Güleç und Fritz Laszlo Weber.1 In den Fokus gerückt werden die Lebensgeschichten und die Perspektiven der Angehörigen von Enver Şimşek, Abdurrahim Özüdoğru, Süleyman Taşköprü, Habil Kılıç, Mehmet Turgut, İsmail Yaşar, Theodoros Boulgarides, Mehmet Kubaşık, Halit Yozgat und Michèle Kiesewetter. Lehrkräfte und Multiplikator:innen sollen sich mit dem hier zur Verfügung gestellten Material ermutigt fühlen, mit jungen Menschen die Geschichten der Opfer des NSU zu erarbeiten, Migrationsgeschichte und -gegenwart in Deutschland kennenzulernen und sich mit Rassismus und seiner Wirkungsweise anhand des NSU-Komplexes auseinanderzusetzen.

The paper proposes the management of bus disruption (e.g. fleet failure) and maintain a resilient transportation system through a synergy between shared autonomous vehicles and the existing public transport system based on the organizational structure and demand characteristics. The methodology is applied to the region of Rennes (France) and its surroundings.

This paper describes a general approach to the specification the stable regime speed-flow function, for motorways, as a part of the stable regime Stochastic Fundamental Diagram consistent with main assumptions of Transportation Systems Theory. Main original elements are: • Specification of speed-flow functions consistent with travel time function, such as BPR-like functions; • Calibration from disaggregate data, say data from single vehicle trajectories; • Specification of the speed r. v. distribution consistent with those used in RUT for route choice behavior modelling, such as Gamma, Inv-Gamma.