KAUST: Autonomous Connected Clean Electric Shared Services

Project: Research project

Project Details


In collaboration with colleagues at KAUST, the Northwestern University Transportation Center investigators will develop algorithms for real-time shared autonomous fleet operations under different business models, intended to run at scale. These consider various possible scenarios for providing seamless Mobility as a Service (MaaS) with automated vehicles in a connected environment, allowing for high levels of service with minimum environmental impact, e.g. through electric powered vehicles. The team will (1) formulate the various scenarios of interest, with regard to the type of MaaS models, e.g. on-demand only vs. allowing reservations, use of heterogeneous vehicle fleets, degree and conditions for sharing rides, among others; (2) develop mechanisms to integrate personalized information from connected travelers to improve the quality of service delivered to customers; (3) investigate use of machine learning algorithms for predicting demand and performance of the system; (4) develop algorithms for solving the resulting formulations in light of the highly dynamic, stochastic and combinatorial character of the problem; (5) test the approaches developed in a representative problem setting; and (6) provide recommendations on most promising MaaS service concepts along with directions for robust solution methods for at-scale deployment. The work will take place over a two-year period, with the first year focused on conceptualization, formulation and small example testing, and the second focused on algorithm development and large-scale development and application. The team will communicate periodically through electronic means with the KAUST and other partners. In-person meetings and presentations will take place at least once a year. The results will be disseminated through international refereed journals and conferences.
Effective start/end date9/15/191/31/22


  • King Abdullah University of Science and Technology (OSR-2019-KAUST CoE NEOM-4178.6)


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