Projekt

The EC-RIDER project will study and create novel, advanced and explainable AI methods for problems in the realm of shared urban mobility and validate them on selected ridesharing scenarios. While working towards these objectives, EC-RIDER will address four research challenges (RCs): 1. RC1: Understand and model human motivation and behavior (user satisfaction, trust, choice of mobility mode) in ridesharing applications while maintaining user privacy. 2. RC2: Develop and evaluate novel business and operations models, pricing and incentive schemes, taking the models of RC1 into account, in order to achieve a satisfactory degree of social welfare. 3. RC3: Develop and evaluate innovative algorithmic AI methods based on the models provided by RC1 and RC2 to find human-centered, socially sustainable, fair and efficient traffic assignments. 4. RC4: Develop new methodologies for successfully creating and applying the AI methods of RC3. The critical success factor we will address is explainability of AI methods and algorithms to the human user.

• Prof. Dr. Jörg P. Müller

  Technische Universität Clausthal
  Institut für Informatik
  Clausthal-Zellerfeld
  

• Dr. Noam Hazon

  Ariel University
  Department of Computer Science
  Ariel
  Israel
  

• Prof. Dr. Lutz Kolbe

  Universität Göttingen
  Fakultät Wirtschaftswissenschaften
  Professur für Informationsmanagement
  Wirtschaftsinformatik
  Smart Mobility Research Group
  Göttingen
  

• Prof. Sarit Kraus

  Bar Ilan University
  Department of Computer Science
  Ramat Gan
  Israel
  

• Prof. Dr. Mark Vollrath

  Technische Universität Braunschweig
  Fakultät für Lebenswissenschaften
  Lehrstuhl für Ingenieur- und Verkehrspsychologie
  Braunschweig
  

• AI for Explaining Decisions in Multi-Agent Environments

• How did you like this ride? An analysis of user preferences in ridesharing assignments.

• Cost Allocation for Prioritized Ridesharing

• Levinger, C.; Hazon, N.; Azaria, A