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Services for Connected, Cooperated, and Automated Mobility based on Big Data and Artificial Intelligence: The SHOW project paradigm

  • Georgios Spanos*
  • , Alexandros Siomos
  • , Carolin Schmidt
  • , Mathias Tygesen
  • , Josep Maria Salanova
  • , Filipe Rodrigues
  • , Alexandros Papadopoulos
  • , Evangelos Antypas
  • , Athanasios Sersemis
  • , Maria Gkemou
  • , Antonios Lalas
  • , Konstantinos Votis
  • , Georgia Ayfantopoulou
  • , Evangelos Bekiaris
  • , Dimitrios Tzovaras
  • *Corresponding author for this work
  • Center for Research and Technology - Hellas

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Cooperative, Connected, and Automated Mobility (CCAM) constitutes a viable solution toward sustainable future mobility in order to achieve the target of decarbonization. Artificial Intelligence (AI) and Big Data (BD) have altered several industrial sectors providing novel and affordable solutions that facilitate and improve existing operations in these sectors. Hence, the combination of the CCAM paradigm with AI methodologies based on BD could ever increase the potential benefits of CCAM in the contemporary society. For this reason, three CCAM services, which are based on AI and BD, are introduced in the current research work in order to tackle three well-known issues of mobility such as i) the estimated time of arrival, ii) the passenger demand prediction and iii) the mobility patterns identification. The proposed CCAM services were tested on various pilot sites of the EU-funded SHOW project, thus demonstrating the potential of BD and AI in future mobility services.

Original languageEnglish
Article number24
JournalOpen Research Europe
Volume5
ISSN2732-5121
DOIs
Publication statusPublished - 2025

Keywords

  • Artificial Intelligence
  • Automated Mobility
  • Big Data
  • Estimated Time of Arrival
  • Machine Learning
  • Mobility Patterns Identification
  • Passenger Demand Prediction

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