Public transit shared mobility - connected and safe solutions
We propose to use adaptive learning algorithms in order to (i) estimate the travel demand, (ii) define and estimate the risks of crashes/conflicts and (iii) minimize transit delays (primary and secondary). The project will initially focus on designing intelligent algorithms for the public transport in Gothenburg, for which large amount of data on city bus driving has already been recorded.
One of the tasks is to investigate and propose an appropriate level of model abstractions and control decomposition into multiple layers that allow a real-time implementable solution.
This project is supported by Chalmers AoA Transport Foundation, with a total budget of 2.4 million SEK including matchup funding.
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