What is the impact of travel distance and travel purpose on travelers’ mode preferences in relation to automated driving transport service?
Our findings suggest that users adopting automated driving transport services are likely to prefer this mode for long-distance leisure trips rather than short-distance commuting trips.
Joint work with Peyman Ashkrof, Gonçalo Homem de Almeida Rodriguez Correia and Bart van Arem. Open Access.
Impact of Automated Vehicles on Travel Mode Preference for Different Trip Purposes and Distances
Zooming in and out on maps gives you the impression that a different version of the same network is displayed. However, important topological properties of the network might be distorted in the process.
We develop a method for automatically generating multiscale graph representations without significantly compromising their topological properties.
Our results show that the method is able to successfully reduce the Amsterdam road network by up to 96% of its original size at a computation time of no more than 15 min with a limited loss of information.
This is part of Panchamy Krishnankumari’s PhD thesis which I co-supervise together with Hans Van Lint.
Heuristic Coarsening for Generating Multiscale Transport Networks
How to design real-time public transport control strategies so that the waiting time savings are not offset by slowing down the service?
Kostas Gkiotsalitis and I propose and test a time-window-based bus holding method which performs better than a rule-based control (including in terms of waiting time savings) albeit more complex. Open access.
Multi-constrained bus holding control in time windows with branch and bound and alternating minimization
Gave on January 18 a seminar at C2Smart together with Panchamy Krishnakumari, a colleague in our Smart Public Transport Lab. We were hosted by Prof. Joseph Chow.
You can watch the seminar by following this link.
Our seminar was entitled Capacity Allocation for On-demand Services, Demand-anticipatory Operations and Analyzing Demand Patterns. On-demand transit has become a common mode of transport with ride-sourcing companies like Uber, Lyft, Didi transforming the way we move. With the increase in popularity for such services, the fleet needs to adapt according to the demand and passenger demand needs to be predicted. In the seminar, we presented our work on capacity allocation for on-demand services, demand-anticipatory operations and analyzing demand patterns using spatial-temporal clustering.
Panchamy and I gave the seminar following the TRB conference in Washington DC and a project meeting with WMATA (Washington Metropolitan Area Transit Authority).