Important Dates

Journal track paper:
submit now, max Apr 1, 2017

Paper submission (TT):
Apr 15, 2017 May 1, 2017

Paper acceptance (TT):
May 31, 2017

Jun 15, 2017

Doctoral Symposium & BAAI session:
Paper submission: Jun 15, 2017
Paper acceptance: Jul 15, 2017
Camera-ready: Jul 25, 2017

Discovery Challenge:
Predictions: Jun 1, 2017
Papers: Jul 8, 2017 Jul 31, 2017

Geometry Friends:
Aug 8, 2017

Sep 5-8, 2017



The Thematic Track on Artificial Intelligence in Transportation Systems (AITS@EPIA2017) 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. This Thematic Track follows up the first edition of the AIASTS Workshop, held at EPIA2007, the second edition of the AITUM Thematic Track, held at EPIA2009, and the third, fourth, and fifth editions of the AITS Thematic Track, held at EPIA2011, EPIA2013, and EPIA2015, respectively. It will serve as a unique platform gathering the AI community, transportation and other social sciences’ practitioners to discuss how cutting-edge AI technologies can be effectively developed and applied to improve transportation performance towards sustainable mobility systems. 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.


As in many multidisciplinary knowledge fields, much advance in AI is fostered through challenges imposed by issues that scientists address when applying theory to solve practical problems. Thus, the AITS Thematic Track serves as a working platform to discuss current developments and advances of AI techniques in a rather practical perspective. It will stimulate a debate emphasizing on how theory and practice are effectively coupled to tackle problems in the specific domain of transportation.

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 use it. Therefore, contemporary transportation systems are a natural ground to conceive, develop, test and apply AI techniques.

Topics of Interest

The AITS Thematic Track welcomes and encourages contributions reporting on original research, work under development and experiments of different AI techniques, such as, supervised/unsupervised learning approaches (e.g. neural networks for classification problems), 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 (air, road, rail and water transports);
  • 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;
  • 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);

Paper Submission

Submissions must follow the guidelines specified on the EPIA 2017 Conference Web site (/call-for-papers/).

Submissions must be original and can be of two types: full papers should not exceed twelve (12) pages in length, whereas short papers should not exceed six (6) pages.

Organizing Committee

Alberto Fernandez, CETINIA, Universidad Rey Juan Carlos, Madrid, Spain
Luis Moreira-Matias, NEC Labs Europe, Germany
Rosaldo Rossetti, FEUP / LIACC, Porto, Portugal

International Program Committee

  • Ana Bazzan, UFRGS, Brazil
  • Carlos Lisboa Bento, University of Coimbra, Portugal
  • Constantinos Antoniou, MIT, USA
  • Eduardo Camponogara, UFSC, Brazil
  • Eugénio Oliveira, University of Porto, Portugal
  • Franziska Klügl, Örebo University, Sweden
  • Giuseppe Vizzari, University of Milan-Bicocca, Italy
  • Gonçalo Homem De Almeida Correia, TU Delft, NL
  • Harry Timmermans, Eindhoven University of Technology, The Netherlands
  • Hussein Dia, Connell Wagner, Australia
  • Javier Sanchez Medina, Universidad de Las Palmas de Gran Canaria, Spain
  • Jihed Khiari, NEC Laboratories Europe, DE
  • João Mendes-Moreira, U. Porto, PT
  • José Telhada, University of Minho, Portugal
  • Josep Salanova, CERTH-HIT, GR
  • Kai Nagel, Technical University of Berlin, Germany
  • Luís Nunes, ISCTE, Portugal
  • Marcela Munizaga, U. Santiago, Chile
  • Oded Cats, TU Delft, NL
  • Sascha Ossowski, Rey Juan Carlos University, Spain
  • Soora Rasouli, Eindhoven University of Technology, The Netherlands
  • Thahn Lam Hoang, IBM Research Dublin