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The 7th AITS Track at EPIA Conferences aims to promote a debate on current developments and advancements of AI techniques in a rather practical perspective. It will gather both the AI community and transportation practitioners to discuss how cutting-edge AI technologies can be effectively applied to improve the performance of transportation systems and mobility in general on a sustainable basis, according to three important dimensions, namely economic, environmental, and social. This forum also aims to generate new ideas towards building innovative applications of AI technologies into smarter, greener and safer transportation systems, stimulating contributions that emphasise on how theory and practice are effectively coupled to solve real-life problems in contemporary transportation, naturally including all sorts of mobility systems. Indeed, today’s transportation systems are being devised on a more intelligent basis, and the concept of Intelligent Transportation Systems (ITSs) has become already a reality among us. More recently, ITS has evolved into the basis giving support to the development of the so-called Smart Mobility solutions, within the framework of Smart Cities, in which social issues increase the complexity of transportation systems and bring about new performance measures such as equity, security, while sustainability is strongly emphasised.
Therefore, this proposed workshop is within the application-oriented, integrative, and multi-disciplinary perspectives of the EPIA Conference series. It is intended to leverage the cross-fertilisation synergetic relationship between AI and ITSs. As a matter of fact, in most fields of Science, advancements in theory are also inspired by problems identified by practitioners in their field of expertise. That is also true in AI! As in many multidisciplinary knowledge areas, many advances in AI are fostered through challenges found by scientists when applying theory to solve practical problems. The AITS Workshop series at EPIA have then served also as a networking platform to discuss current developments and advances of AI, as well ashow such findings might be practically applied to this challenging and inspiring domain.
Besides its economic, social, and environmental importance, Transportation is a very challenging domain - especially due to its inherent complexity. It is formed up by geographically and functionally distributed heterogeneous elements, both artificial and human, which have different decision-making abilities and/or goals, thus turning its dynamics rather uncertain. Moreover, mobility plays a major role towards citizen’s quality of life. The scarceness of resources and the raising number of constraints to urban mobility, contemporary transportation have been experienced a great revolution. Consequently, the rational use of transportation infrastructures, as well as the way it interacts with the environment must be managed on a sustainable basis-independently on the dimension of transportation systems that we are working in. In fact, this scenario motivates much research in different and diverse fields, from distributed computing to social sciences, and has been used as a natural ground for AI to devise, test and leverage novel and new theories contributing to substantial advances to the so-called Empirical AI!
During the last decades, we have been witnessing the advent of ITSs taking place in our daily lives. Rather than increasing service capacity, one underlying approach of ITS-based solutions is to ensure productivity and mobility by making better use of existing transportation infrastructure, featuring them with smarter, greener, safer, and more efficient technologies, linking rail, ground, air and urban transport towards a truly seamless multimodal future. Much advance verified in this field is due to AI – turning it a key ingredient to ITS. The relationship between these two areas is certainly mutually beneficial, suggesting a wide range of cross-fertilisation opportunities and potential synergism between the AI community that devises theory and transport practitioners that practically apply it.
TOPICS OF INTEREST
The Workshop welcomes and encourages contributions reporting on original research, work under development and experiments of different AI techniques, such as intelligent agents and multi-agent systems, supervised/unsupervised learning as well as statistical learning approaches (e.g. neural networks for classification problems, logistic regression, decision trees/rules induction, and so forth), biologically inspired approaches, evolutionary algorithms, knowledge-based and expert systems, case-based reasoning, fuzzy logics, data mining and/or fusion techniques, big-data analytics, and other pattern-recognition and optimization techniques, as well as ambient intelligence and ontologies, to address specific issues in contemporary transportation and mobility systems, which would include (but are not limited to):
• different modes of transport and their interactions (air, road, rail and water transports);
• intelligent and real-time traffic management and control;
• design, operation, timetabling and real-time control 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;
• advanced traveller 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;
• serious games and gamification in transportation;
• behaviour modelling and social simulation of transportation systems;
• electric mobility and its relationship with smart grids and the electricity market;
• computer vision in autonomous driving;
• surveillance and monitoring systems for transportation and pedestrians;
• data-driven preventive maintenance policies;
• Anomalous Trajectory Mining and Fraud Detection;
• smart architectures for vehicle-to-vehicle/vehicle-to-infrastructure communications;
• automatic assessment and/or evaluation on the transport reliability (planning, control and other related policies);
• Intelligent transportation infrastructure management and maintenance.
PAPER SUBMISSION
All accepted papers will be published by Springer in a volume of Springer’s Lecture Notes in Artificial Intelligence (LNAI) corresponding to the proceedings of the 19th EPIA Conference on Artificial Intelligence, EPIA 2019.
Submissions must be original and not published elsewhere. Papers should not exceed twelve (12) pages in length and must adhere to the formatting instructions of the conference. Each submission will be peer reviewed by at least three members of the Program Committee. The reviewing process is double blind, so authors should remove names and affiliations from the submitted papers, and must take reasonable care to assure anonymity during the review process. References to own work may be included in the paper, as long as referred to in the third person. Acceptance will be based on the paper’s significance, technical quality, clarity, relevance and originality. All accepted papers must be presented orally the conference by one of the authors and at least one author of each accepted paper must register for the conference.
ORGANIZING COMMITTEE
Rosaldo Rossetti, Universidade do Porto, Portugal
Alberto Fernandez, Universidad Rey Juan Carlos, Spain
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