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.

MT-ITS 2017

Will be attending MT-ITS 2017, the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, together with colleagues with whom I collaborated on public transport -related studies.

I am involved in the following contributions that will be presented in the upcoming conference:

  • Analysis of Network-wide Transit Passenger Flows based on Principal Component Analysis. (Presenter: Ding Luo)
  • Simulating the Effects of Real-time Crowding Information in Public Transport Networks (Presenter: Arek Drabicki)
  • Impact of Relocation Strategies for a Fleet of Shared Automated Vehicles on Service Efficiency, Effectiveness and Externalities (Presenter: Konstanze Winter)
  • Real-time Short-turning in High Frequency Bus Services based on Passenger Cost (Presenter: David Leffler)
  • Measuring Spill-over Effects of Disruptions in Public Transport Networks (I will present work performed with Sanmay Shelat)

Looking forward to my first MT-ITS experience!

Added on 16-8-2017: links to all conference papers are available on the Publications page.

Prediction for proactive mitigation of bus bunching


A new paper proposes a data driven method to predict Bus Bunching in real-time followed by the selection and deployment of a corrective action based on the assessment of bunching likelihoods. The method was validated using one-year data of 18 real-world bus routes. This combined prediction-control approach can contribute to more proactive bus operations and improved service reliability.


Link to the full paper









Two public transport proposals granted

Two public transport projects will be soon launched in the Department of Transport and Planning at TU Delft:

(1) SCRIPTS – on flexible demand-anticipatory services. Granted in the Smart Urban Regions in the Future (SURF) program by NWO [2016-2018; total of 1,800,000€, of which 500,000€ in TU Delft]. ‘Smart Cities’ Responsive Intelligent Public Transport Systems’ will develop advanced models for the optimal design of hybrid public transport systems, involving demand responsive transport services that are flexible in route and schedule and (self-)organized through ICT platforms, and the simulation of their performance, including a series of pilots and showcases.

(2) TRANS-FORM – on real-time transfer and congestion management. Granted in the Co-fund Smart Cities and Communities (ENSCC) call [2016-2018; total of 1,800,000€, of which 315,000€ in TU Delft]. A consortium of universities, industrial partners, public authorities and private operators from Switzerland, Sweden, Spain and the Netherlands, led by TU Delft. ‘Smart Transfers through Unravelling Urban Form and Travel Flow Dynamics’ will develop a multi-level approach for monitoring, mapping, analyzing and managing urban dynamics in relation to interchanging travel flows. Analysis of pedestrian and traveler flows at the hub, urban and regional networks.

Three new PhD positions in the area of public transport modelling will be soon available to work in these projects. Relevant background and skills include simulation modelling, network analysis and optimization.

UPDATE (28-01-2016):

Interested? See the job ad here. Applications are due by February 10.



How reliable is real-time information?

RTI Stockholm

A lot of effort is invested in developing algorithms to better predict traffic conditions in general and bus arrival times in particular, but how reliable is the real-time information currently provisioned? This was the topic of a study that I embarked on together with Gerasimos Lotous.  One of the triggers for this study was the “SL minute”, named after the public transport agency, which is notoriously known and used by the public and popular media in Stockholm as a particularly “long” minute because the bus fails to arrive within the projected time window. The design of this study enables us to examine whether the coined term is empirically justified for the current system. We find that on average “SL minute” lasts in fact 63.7 seconds. However, the average excess waiting time disguises substantial variations, in particular depending on the prediction/waiting horizon. Notwithstanding, the provision of real-time information yields a waiting time estimate that is more than twice as close to the actual waiting times than the timetable. This difference in waiting time expectations is equivalent to 30% of the average waiting time. The results of this study can be used as a benchmark in the development of more elaborated prediction schemes.

The paper is now available from Journal of Intelligent Transportation Systems using this link.