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

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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.

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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.

 

 

What can history tell us about the future? About predicting public transport travel times

It is common wisdom that while man should learn from history. At the same time, another prominent wisdom is that history never repeats itself. Both assertions are also true for public transport operations. When predicting future states of the system, it is advisable to account for historical records whilst bearing in mind that it will not evolve in the exact same fashion. Instead, the underlying dynamics of the current system as well as inherent stochastic will result with different performance. This makes the task of predicting vehicle trajectories and their arrival times at downstream stops a non-trivial task. Such predictions are useful both for operators for better managing the fleet in real-time as well as for passengers that wish to reduce their travel uncertainty and enable them to take more informed decisions.

Today and yesterday I presented on IEEE Intelligent Transport System Conference (ITSC) in Qingdao, China, two studies on prediction schemes for public transport and the generation of real-time travel information.One study together with Masoud Fadaei Oshyani, who is doing his PhD under my supervision, proposes a hybrid model to integrate several sources of information on downstream travel conditions for buses. The contribution of each information source (within-day instantaneous data, day-to-day historical data and static schedule data) and related parameters are estimated using a genetic algorithm optimization. Predictions improve considerably compared with the currently-used scheme and there are good indications on the transferability of the scheme. For the presentation slides click here: Real-time Bus Departure Time Predictions

The second study investigated real-time predictions for light rail trains. Light rail trains are often characterized by mixed-operations: running partially in mixed traffic (in some cases even in pedestrian street) and in segregated right-of-way, alternately as well as with different levels of priority in crossings. A prediction scheme that is based on constructing link-specific speed profile and then computing for a given current position and speed the remaining travel time was applied for a case study in Bergen, Norway (thanks to Edouard Naye). The scheme elevates the prediction accuracy to the same level  that was reported by previous studies for metro systems. For the presentation slides click here: Real-time Predictions for Light Rail Train Systems