We are developing models that provide insights into the performance of on-demand transportation systems and use them to estimate the impact of their large-scale deployment. Further, we are looking in the potential of advanced optimization techniques, automation, ride-sharing and market-based mechanisms to achieve affordable, efficient, and congestion-free urban traffic.
We have developed a model that allows making principled decisions about the fleet size and the optimal fleet distribution in vehicle sharing system. As a case study, we used the model to compute what would be the minimum fleet size and minimum station capacities that suffice to ensure that the customer will not experience unavailability of bikes to rent or unavailability of free bike docks.
The large fleets of AMoD systems might play a major role in future traffic flows. We develop routing algorithms to efficiently control the fleet with respect to the limited road infrastructure capacities. We present a new reformulation of congestion-free fleet routing problem that enables to solve instances of realistic sizes that were previously considered intractable.
We studied the impact of the mobility on demand systems (MoD) deployment on traffic density. We tested both the system with and without ridesharing on the Prague metropolitan area, where we replaced all vehicles by MoD. Now we are working on the comparison of different ridesharing algorithms. You can explore our open source projects at https://github.com/aicenter.
We are based in the beautiful city of Prague. Our offices are located in a historical building of one of the oldest technical universities in Europe.
We are looking for colleagues at all levels of seniority (PhDs, postdocs and assistant/associate professors) to join our team. If you find our work interesting, consider joining us!