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).
On-demand public transport is expected to become an increasingly important component of public transport systems, facilitated by vehicle automation. The potential of rail-bound on-demand services has been largely overlooked. Together with Jesper Haverkmap, who did his master thesis in the Dutch Railways, we determine the capacity requirements of an envisaged automated on-demand rail-bound transit system which offers a direct non-stop service.
The full paper published on Transportation Research Part B: Methodological is available here.
How many vehicles would such a system require? What are the costs associated with such a system? What are the track and station capacity required? What level of service will it deliver? What are the network saturation patterns given that vehicles can now perform route choice and choose the shortest path to passengers’ destination? What are the consequences for equity in service provision?
An optimization model is formulated for determining the optimal track and station platform capacities for an on-demand rail transit system so that passenger, infrastructure and operational costs are minimized. The macroscopic model allows for studying the underlying relations between technological, operational and demand parameters, optimal capacity settings and the obtained cost components.A series of sensitivity analyses are performed to test the consequences of a range of network structures, technological capabilities, operational settings, cost functions and demand scenarios for future automated on-demand rail-bound systems.
The model is applied to a series of numerical experiments followed by its application to part of the Dutch railway network. The performance is benchmarked against the existing service, suggesting that in-vehicle times can be reduced by 10% in the case study network while the optimal link and station capacity allocation is comparable to those currently available in the case study network. While network geometry and demand distribution are always the underlying determinants of both service frequencies and in-vehicle times, line configuration is only a determinant in the conventional system, whereas the automated on-demand rail service better caters for the prevailing demand relations, resulting in greater variations in service provision.
Here is a clip made by the Dutch Railways (NS) on the concept of on-demand services, denominated as Swarming transport.
I am honored and delighted to be awarded with an ERC (European Research Council) Starting Grant. My project CriticalMaaS will develop and test concepts, theories and models for planning, operating and evaluating the dynamics of Mobility as a Service.
The project will run for 5 years and will be performed by a team of PhD students, post-doc researchers and in collaboration with colleagues within the Department of Transport and Planning at TU Delft and beyond.
See the announcement on the ERC website
and on the Faculty news.
Congratulations Dr. Nadjla Ghaemi for successfully defending your PhD dissertation on “Short-turning Trains during Full Blockage in Railway Disruption Management” today!
Nadjla’s PhD work has been published in the following journal publications:
Ghaemi N., Cats O. and Goverde R.M.P. (2018). Macroscopic multiple-station short-turning model in case of complete railway blockages. Transportation Research Part C, 89, 113-132.
Ghaemi N., Cats O. and Goverde R.M.P. (2017). A Microscopic Model for Optimal Train Short-Turnings during Complete Blockages. Transportation Research Part B, 105, 423-437.
Ghaemi N., Zilko A., Yan F., Cats O., Kurowicka D. and Goverde R.M.P. (2018). Impact of Railway Disruption Predictions and Rescheduling on Passenger Delays. Journal of Rail Transport Planning & Management. Accepted.
Ghaemi N., Cats O. and Goverde R.M.P. (2017). Railway Disruption Management Challenges and Possible Solution Directions. Public Transport, 9 (1-2), 343-364.
The defense was followed by a TRAIL seminar where I gave a talk on “Robust passenger transport systems: Network, operations and user adaptations”