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.



What can passenger counts tell us about urban form and dyanmics?

Apparently quite some interesting insights!


Using a simple clustering method, transport data such as passenger counts can be used to identify the main activity centers. Activity clusters are then classified based on their time-dependent flow profile including magnitude, directness and the distribution of incoming and outgoing flows. It is postulated that urban structure and the spatial distribution of activities are manifested through time-dependent flow profile because activity centers with distinguished functions will yield distinctive travel patterns throughout the day. The method developed in this paper is directly transferable to different data sources, networks and scales.

The method can be used by policy makers and planners to provide insights on the discrepancy between the planning policy and the prevailing urban structure. This technique might be most valuable for anlayzing urban forms in mega-cities in emerging economies which undergo rapid changes.

Together with Qian Wang and Yu Zhao, we applied this method to the Stockholm metropolitan area. Stockholm is famous for its long-term monocentric planning with a dominant central core. Since the turn of the century there has been a noticeable shift towards developing sub-centres but to what extent has it been realized insofar?

See the results of our analysis in the full paper – available here. 

Figure 9 Poly



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.



How can we keep you satisfied?

The public transport industry has along tradition of measuring passenger satisfaction for a number of purposes including monitoring and market analysis. Changes in passenger satisfaction over time are typically conceived in terms of absolute of perceived changes concerning service quality. However, we are all admittedly inclined to shift the importance that we attach to service attributes as time evolves.

The Swedish Public Transport Association (Svensk Kollektivtrafik) kindly granted me and Yusak Susilo from KTH access to a large rolling survey that they conduct since 2001. The results of 13 years of evolution in passenger satisfaction were presented last week on TRB in Washington DC. The survey data sums up to more than half a million records collected in 2001-2013. This made one of the committee members that took part in the session to comment that “we in the US can only drool from the possibilities made by it'”.

The work was performed together with Roberto Abenoza and Chengxi Liu. We constructed dynamic priority maps to visualize the trajectory of various service attributes in terms of their relative importance to overall satisfaction and their relative performance. This will support stakeholders such as agencies and operators to identify priority areas and benchmark it against past performance and other service providers.

The presentation is available here: TRB2015 OdedCats SKT