Transportation

Artificial Intelligence in Transportation Systems aims to promote an interdisciplinary debate on current developments and advances of AI techniques in a rather practical perspective, focusing on transportation and mobility systems. Research and development in cutting-edge AI technologies that could be effectively developed and applied to improve transportation performance towards sustainable mobility systems is going to be a high priority. This forum is an opportunity for the technical and scientific community to present progresses made so far, and as a means to generate new ideas towards building innovative applications of AI technologies into smarter, greener and safer transportation systems, stimulating contributions that emphasize on how theory and practice are effectively coupled to solve real-life problems in contemporary transportation.
The areas of focused research, work under development and experiments of different AI techniques, such as neural networks, biologically inspired approaches, evolutionary algorithms, knowledge-based and expert systems, case-based reasoning, fuzzy logics, intelligent agents and multi-agent systems, support vector regression, data mining and other pattern-recognition and optimization techniques, as well as concepts such as ambient intelligence and ubiquitous computing, service-oriented architectures, and ontology, to address specific issues in contemporary transportation, which would include (but are not limited to):
• Different modes of transport and their interactions.
• Intelligent and real-time traffic management and control.
• Design, operation, time-tabling and management of logistics systems and freight transport.
• Transport policy, planning, design and management.
• Environmental issues, road pricing, security and safety.
• Transport systems operation.
• Application and management of new technologies in transport.
• Travel demand analysis, prediction and transport marketing.
• Traveler information systems and services.
• Ubiquitous transport technologies and ambient intelligence.
• Pedestrian and crowd simulation and analysis.
• Urban planning toward sustainable mobility.
• Service oriented architectures for vehicle-to-vehicle and vehicle-to-infrastructure communications.
• Assessment and evaluation of intelligent transportation technologies.
• Human factors in intelligent vehicles.
• Autonomous driving.
• Artificial transportation systems and simulation.
• Surveillance and monitoring systems for transportation and pedestrians.