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Project

Bus transport of the future: algorithms for fully on-demand public transport

Local public transport, defined as the collective scheduled transport of passengers, is one of the backbones of urban mobility within the EU. The traditional line-based bus system used in virtually every city worldwide, however, is inherently inefficient and lacks flexibility. Even though many obstacles still need to be overcome, the ubiquity of mobile devices would for the first time in human history allow for a large-scale shift to on-demand public transport. However: the operational and tactical foundations that are required to implement an on-demand transportation system are still lacking. Even though a large majority of people now has the technological means to determine exactly where they are and transmit exactly where they want to go, the tools to plan all of these transportation requests have not been developed. Such a system would demand carrying out optimization on a scale never seen before, and will require not only modeling all the intricacies of an on-demand scheduling of passengers, but to develop new optimization algorithms and software components that can coordinate such a massive fleet of vehicles and vast number of passengers. The main objective of this project is to develop the Operations Research foundations that will allow to design and deploy a fully on-demand public transportation system. The outcome of this project will provide an optimization framework for the scheduling of passengers and buses, a simulation environment to evaluate the impact of several design/operational decisions on the performance of the transportation system, and a set of tactical guidelines that will assist the progressive transition to an on-demand operation. Achieving these goals will position the University of Antwerp as one of the first research centers to tackle this paramount challenge.
Date:1 Jan 2018 →  31 Dec 2021
Keywords:HEURISTIC METHODS, LOGISTICS OPTIMIZATION, OPTIMISATION
Disciplines:Applied mathematics in specific fields, Statistics and numerical methods