PhD defense and TRAIL seminar

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 blockagesTransportation 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 BlockagesTransportation 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 DirectionsPublic 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”

ITS Berkeley seminar

Monday, April 16, 2018 – , 1:00pm to 2:00pm, 212 O’Brien Hall

ITS Berkeley seminar announcement

Seminar abstract: The metropolitan passenger transport landscape is shifting due to a combination of technological and social developments which enable both service providers and service users to become increasingly adaptive. Service providers can manage their resources to better cater for prevailing demand patterns, while service users can adjust their behaviour in response to real-time information. In this seminar, I will present our work on modelling system dynamics and the interaction between supply and demand under uncertainty in relation to tactical planning (e.g. fleet size and composition, frequency setting) and real-time management (e.g. trip dispatcher, disruption management) of fixed line-based as well as flexible on-demand services.

Method for the reliable determination of service frequencies

Service reliability is often considered only at the operational management phase, while services are assumed to be perfectly reliable at the strategic and tactical planning phases. However, service (un)reliability has consequences on the effective frequency and thus on deficiencies in capacity allocation and passenger waiting times and on-board comfort.

Determining the dispatching headways of bus services in a city network is a multi-criteria problem that typically involves balancing between passenger demand coverage and operational costs.

Together with Costas Gkiotsalitis from NEC Labratories Europe, we develop and apply a reliability-based optimization framework for setting the dispatching headways of bus lines that considers historical operational data and is aware of the passenger waiting time variability at each stop and how it is affected when changing the planned dispatching headways.

Check out the full paper published on Transportation Research Part C – Emerging Technologies following this link

We hope that this work will contribute to a new generation of tactical planning methods that account for service uncertainty.

TRB 2018

Looking forward to meeting many colleagues and friends at the Transportation Research Board (TRB) 97th Annual Meeting in Washington DC next week (January 7-11)!

The following studies which I have been involved in together with students and colleagues will be presented at TRB this year:

  1. The Potential of Demand Responsive Transport as a Complement to Public Transport: An Assessment Framework and an Empirical Evaluation. (Session 293, Monday 10:15 AM- 12:00 PM Convention Center, 147A) Alonso-Gonzalez M., Liu T., Cats O., van Oort N. and Hoogendoorn S.
  2. Individual, Travel and Bus Stop Characteristics Influencing Traverlers’ Safety Perceptions. (Session 556, Tuesday 10:15 AM- 12:00 PM Convention Center, 143B) Abenoza R.F., Ceccato V., Susilo Y. and Cats O.
  3. Constructing Spatiotemporal Load Profiles of Transit Vehicles with Multiple Data Sources. (Session 649, Tuesday 1:30 PM- 3:15 PM Convention Center, Hall E) Lou D., Bonnetain L., Cats O. and van Lint H.
  4. Strategic Planning and Prospects of Rail-bound Demand Responsive Transit. (Session 660, Tuesday 1:30 PM- 3:15 PM Convention Center, Hall E) Cats O. and Haverkamp J.
  5. Demand-anticipatory Flexible Public Transport Service. (Session 784, Tuesday 8:00 AM- 9:45 PM Convention Center, Hall E) van Engelen M., Cats O., Post H. and Aardal K.

In addition, will be presiding:

  • Poster session 650 on Transit Service Disruptions: Impacts and Mitigation Measures (Tuesday 1:30 PM- 3:15 PM, Convention Center, Hall E)
  • Poster session 651 on Economic and Optimization Models for Integrated Service Planning (Tuesday 1:30 PM- 3:15 PM, Convention Center, Hall E)

In conjunction with the TRB conference, Jaime Soza Parra and I meet with Washington Metropolitan Area Transit Authority on Jan 11 to present and discuss the preliminary results of our evaluation of their headway-control experiment.

Demand anticipatory operations

On-demand (known also as flexible or demand responsive) services rely on algorithms that determine which vehicle to assign to which passenger travel request. This becomes especially relevant as developments in vehicle automation and the shared economy call for new developments in routing flexible transport services.

Together with colleagues from Applied Mathematics and Transdev (Connexxion) in the Netherlands, we propose a new type of insertion algorithm: an online dynamic insertion algorithm with demand forecasts. Hence, this algorithm beyond responsiveness by incorporating demand anticipatory capabilities. The performance of this algorithm is tested in a simulation model for a case study network located in vicinity to Amsterdam.

See the full paper in Transportation Research Part E, by following this link.

When combining the new insertion algorithm with empty vehicle rerouting, 98% of passenger rejections are eliminated and travel and waiting times are reduced by up to 10 and 46% respectively, compared to traditional insertion algorithms. A sensitivity analysis tested performance robustness to variations in operational and demand conditions including different fleet compositions.