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.

The role of mobility in spreading COVID-19

At the Smart Public Transport Lab we are currently undertaking a series of studies related to the corona crisis. Given the urgency and the gravity of what is at stake, we share results as soon as we are confident that we can contribute to the policy and scientific debate with sound models and empirical findings.

By now we are ready to share findings from two studies focusing on the role of mass transit and ride-sharing in spreading the virus. Welcome to check out our short articles following these links:
Virus spreading in public transport networks: the alarming consequences of the business as usual scenario [A slightly modified version is available on ResearchGate]

When sharing is not always caring: On the spreading processes in ride-sharing networks [A slightly modified version is available on ResearchGate]

And stay tuned for upcoming results from on-going studies.

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!

TransitData2019

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.

Generating multiscale graphs

Zooming in and out on maps gives you the impression that a different version of the same network is displayed. However, important topological properties of the network might be distorted in the process.

We develop a method for automatically generating multiscale graph representations without significantly compromising their topological properties.

Our results show that the method is able to successfully reduce the Amsterdam road network by up to 96% of its original size at a computation time of no more than 15 min with a limited loss of information.

This is part of Panchamy Krishnankumari’s PhD thesis which I co-supervise together with Hans Van Lint.

Heuristic Coarsening for Generating Multiscale Transport Networks