Modelling virus spreading in ride-pooling networks

Will a random infected passenger spread a virus across a large number of travellers using ride-pooling services? or will it be encapsulated and thus confined to a distinct community? How many other travellers will get infected and how will the epidemiological process evolve? Finally, can we mitigate it by effective control and design measures and thus introduce it to policymakers as a safe alternative?

See our open-access work published in Scientific Reports with Rafał Kucharski and Julian Sienkiewicz.

We combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. 

Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks without sacrificing the efficiency achieved by pooling.

PhD graduations – Ding, Menno and Panchamy

Had an eventful end of last month with three PhD graduations within a week span:

On February 21, Ding Luo defended his thesis entitled “Data-driven Analytics and Modeling of Passenger Flows and Networks for Public Transport Systems“.

Then, on February 26, Menno Yap defended his thesis on “Measuring, Predicting and Controlling Disruption Impacts for Urban Public Transport”. Menno was awarded for his work with a Cum-laude designation.

And, on February 27, Panchamy Krishnakumari defended her dissertation on “Multiscale Pattern Recognition of Transport Network Dynamics and its Applications“. Panchamy was awarded for her work with a Cum-laude designation.

Congratulations to the young doctors for their wonderful achievements!


Presenting at the Transit Data 2019 – the 5th International Workshop and Symposium on research and applications on the use of massive passive data for public transport on 8th -10th July in Paris, France. Contributions include the following studies:

– Generating network-wide travel diaries using smartcard data
– Enhanced complex network representation of public transport for accessibility assessment based on General Transit Feed Specification data
– Impact analysis of a new metro line in Amsterdam using automated data sources
– Predicting and clustering station vulnerability in urban networks
– Investigating the effects of real-time crowding information (RTCI) systems in urban public transport under different demand conditions

You may find the sessions by browsing the program.

Recovery time and propagation effects of passenger transport disruptions

How do disruptions propagate in public transport networks? for how long and how far away from the source are passengers affected?

In a new paper on Physica A: Statistical Mechanics and its Applications, we propose a method for quantifying the extent to which the network exhibits spillover effect.

Model results are evaluated for the Stockholm network using a dynamic non-equilibrium assignment model.

[News headline: Bomb threat against train travel; Travel chaos following two false bomb alarms; photo from Stockholm Central station]