The transportation community (and industry) often bases traffic predictions on complex computer-based simulations that can model many elements of a real transportation system but not always resolve their properties. On the other hand, the theory of dynamic traffic assignments in terms of equilibrium existence, computability and efficiency, has not matured to the point of matching the model complexity inherent in simulations. This Dagstuhl Seminar is the third in a series that brings together leading scientists in the areas of traffic simulations (SIM), algorithmic game theory (AGT) and dynamic traffic assignment (DTA) with the goal to further close the gap between theory and simulations. We will particularly address the following topics.

Queuing representation in dynamic models. The method of representing queuing dynamics is perhaps the central decision affecting the subsequent properties of a dynamic network model. Discrete simulation models are good for reflecting complex intersections and queue spillback behaviour, yet their theoretical properties are less well understood. Somewhat at the other extreme, the bottleneck or fluid queue model gives a very simple characterisation that allows many theoretical results to be established, yet the appealing theoretical properties this gives rise to are typically lost when spatial queue spillback is included. We will explore how the theoretical boundaries of the discrete and continuous models may be further advanced.

Risk-averse traveller behaviour in dynamic models. Travellers making repeated journeys typically face unexpected traffic conditions, and experienced travellers take appropriate risk-averse strategies in their choice of route and departure time. While quite a range of models have been proposed in the transportation literature for representing such behaviour, relatively little is known about the theoretical properties of these models (relative to their risk-neutral counterparts), such as uniqueness, complexity, computation and efficiency of equilibria. We will classify and analyse the existing models and theory for such phenomena, and identify key open research questions and promising theoretical frames.

Autonomously guided vehicles in dynamic models. It is widely anticipated that future transport systems will comprise significant numbers of autonomously guided vehicles, which provides great potential for considerable benefits to both guided and unguided vehicles. However, the development of techniques for the modelling of dynamic traffic flow in such mixed conditions is still in its early stages. We will assess the state-of-the-art for such dynamic link/intersection modelling, identify desirable properties of any such mixed model, and set out new open research questions. This will include, for example, consideration of (atomic splittable) oligopolistic routing models, which seem of strong relevance but have been largely unexplored in a dynamic network setting.

Coordinated/shared routing. It is anticipated that most future mobility will be coordinated by shared use of resources, such as dynamic ride-sharing or car-sharing. This requires elements of cooperative behaviour, embedded within a non-cooperative framework. In addition, there are many time-dependent elements, such as the coordination of 'matched' journey in space and time, and the use of dynamic pricing to influence travellers to be flexible (e.g., in their desired departure time). Studying large-scale problems of this kind is a new challenge, and the potential of game theory to address this challenge has been little studied.

Creative Commons BY 4.0

Martin Gairing, Carolina Osorio, Britta Peis, and David Watling

• Joseph Chow (New York University, US)
• Richard Connors (University of Luxembourg, LU) [dblp]
• Katharina Eickhoff (RWTH Aachen, DE) [dblp]
• Miriam Fischer (Imperial College London, GB)
• Gunnar Flötteröd (Linköping University, SE & Swedish National Road and Transport Research Institute (VTI) - Stockholm SE) [dblp]
• Martin Gairing (University of Liverpool, GB) [dblp]
• Yiannis Giannakopoulos (Universität Erlangen-Nürnberg, DE)
• Lukas Graf (Universität Augsburg, DE) [dblp]
• Tobias Harks (Universität Augsburg, DE) [dblp]
• Martin Hoefer (Goethe-Universität - Frankfurt am Main, DE) [dblp]
• Takamasa Iryo (Tohoku University - Sendai, JP) [dblp]
• Max Klimm (TU Berlin, DE) [dblp]
• Ekkehard Köhler (BTU Cottbus-Senftenberg, DE) [dblp]
• Jannik Matuschke (KU Leuven, BE) [dblp]
• Haruko Nakao (University of Luxembourg, LU) [dblp]
• Neil Olver (London School of Economics, GB) [dblp]
• Carolina Osorio (HEC Montréal, CA & Google - Mountain View, US) [dblp]
• Dario Paccagnan (Imperial College London, GB) [dblp]
• Britta Peis (RWTH Aachen, DE) [dblp]
• Rahul Savani (University of Liverpool, GB) [dblp]
• Guido Schäfer (CWI - Amsterdam, NL) [dblp]
• Daniel Schmand (Universität Bremen, DE) [dblp]
• Alexander Skopalik (University of Twente - Enschede, NL) [dblp]
• Martin Skutella (TU Berlin, DE) [dblp]
• Martin Strehler (Westsächsische Hochschule Zwickau, DE) [dblp]
• Bernhard von Stengel (London School of Economics, GB) [dblp]
• David Watling (University of Leeds, GB) [dblp]