hEART2017 conference

I participate in the hEART conference which take place this week in the Technion – Israel Institute of Technology, Haifa.

After hosting it last year in Delft, it is great to attend it in the campus where I have studied for four years and excited to share the experience gained in the following studies:

  1. “Coordinating Merging Public Transport Operations Using Holding Control Strategies” presented by Georgios Laskaris
  2. “Tactical Service Design and Vehicle Allocation Optimization“, which I present
  3. “An Integrated Trip Assignment Model for Passenger Rail Systems” presented by Flurin Hänseler
  4. “Traveler’s Perceived Safety at Bus Stops in Stockholm, Sweden”, presented by Roberto Fernandez Abenoza

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.

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.

 

 

MIT transit group seminar

On Thursday, November 20, I had the privilage to present highlights from my research to MIT transit group led by Prof. Nigel Wilson and thereafter had the opportunity to disucss ongoing research activities with group members.

The seminar was entitled: “Unraveling and modeling the dynamics of public transport systems: Theory and applications”, where I briefly presented the transit operations and assignment model, BusMezzo, and its applications to service reliability and control, congestion and evaluation of increased capacity as well as service disruptions and the value of real-time information provision.

For a reduced version of the presentation, click here: MIT seminar 20112014 v1

IMG_2012

 

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