Aims of the workshop

The field of traffic and transportation is both geographically and functionally distributed, with subsystems exhibiting a high degree of autonomy while remaining interdependent. These settings often involve various dynamics, including real-time constraints and conflicting goals. Intelligent algorithms are essential for addressing the complexity inherent in traffic coordination and transportation management. Simultaneously, there exists a substantial amount of relevant data concerning different aspects of traffic and transportation systems, providing a foundation for innovative intelligent approaches. 

Consequently, numerous applications in this domain can be effectively modeled using intelligent, autonomous agents and multi-agent systems. The growing interest of the Artificial Intelligence community in the traffic and transportation domain aligns with the increasing demand within the traffic community for more secure, efficient, and sustainable (i.e. resource-efficient and ecological) transportation solutions. 

Considering the recent trends and achievements in deep learning approaches from a scientific standpoint, it is crucial to explore the extent to which these methods can make significant contributions to addressing existing challenges in the traffic and transportation sector. Thus, we particularly encourage contributions on machine learning, deep learning, and data-driven approaches that target problems specific to this application area. 

On the application front, researchers face a continuous stream of original and novel challenges that are formulated at an ever growing rate. These include but are not limited to autonomous vehicles, human driver assistance systems (which often necessitate accurate modeling of drivers’ and pedestrians’ behaviors for rigorous testing), and innovative cooperative systems utilizing V2X communication in connected vehicles and smart city infrastructures. These topics, in addition to more traditional ones such as, for example, agent-based traffic simulation or traffic control, contribute to the Workshop’s diverse range of interests.  

In essence, this workshop aims to foster collaboration between researchers and practitioners, providing a platform to exchange perspectives on modeling, simulation, control, and management of large-scale complex transportation systems. This encompasses both  micro and macro levels, leveraging AI-based techniques, models, and approaches within the framework of autonomous agents and multiagent systems. 

Topics of interest

• Applications of AI technology in traffic, transportation, and transport logistics

• Intelligent Optimization (e.g., traffic assignment, routing, route choice)

• Autonomic and autonomous transportation systems

• Coordination in intelligent transportation systems

• Intelligent, adaptive traffic control

• Distributed decision making in traffic, transportation and transport logistics

• Intelligent vehicles, intelligent assistance systems and human involvement

• Coordination of intelligent vehicle fleets

• Agent based intervehicular communication and V2I communication

• Mobile devices in smart transportation systems

• Intelligent monitoring of transportation systems, data collection, filtering, prediction and distribution of traffic information and transportation data

• Autonomous and connected vehicles, collaborative driving

• Self-* properties and theory of intelligent traffic and transportation systems

• Agent-based simulation of traffic and transportation systems and their behavior, including detailed models of traffic participant behavior

• Agent-based pedestrian and crowd simulation

• Data driven approaches in the domain of traffic and transportation

• Deep learning architectures for temporal and spatial traffic data

• Verification and validation of intelligent transportation systems

• Shared mobility models, e.g., car-sharing, ride-sharing, bike and e-scooter sharing

• Multi-modal journey planning

• Mobility-as-a-Service

• Other future mobility technologies