Welcome to my web page 👋

My name is Clément Sebastiao. I am currently a PhD Student at the NERDS group at the IT University of Copenhagen , funded by the EU Horizon Project Just Streets .

You can also find my publications , my CV , and all the random things I dare to say on the Internet on my blog !

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If you want to get in touch with me about something or just to say hi, reach out on social media or send me an email. I would (most likely) love to hear from you !

Research Interests

Urban Data Science
Applied Network Science
Sustainable and Just Mobility
... and many more !

Recent research

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Right-wing Votes Relate to Delays in Bicycle Network Development

Clément S, Michael S

Paper

In
Transport Findings

Paris has implemented only 43% of its 2021-2026 bicycle network plan by its 2026 deadline, but the delays are not equally distributed. Correlating political voting and bicycle infrastructure data, we find that boroughs with a higher share of right-wing votes are also boroughs with the largest delays in bicycle infrastructure development. Although the number of voting units is only 17, this correlation is remarkably strong and significant. At the city scale, for each additional 1% of right-wing votes, we find 0.86% less protected bicycle lanes built.
The Trade-off Between Directness and Coverage in Transport Network Growththe Trade-off Between Directness and Coverage in Transport Network Growth

Clément S, Anastassia V, Ane Rahbek V, Luca Maria A, Michael S,

Paper

In
Applied Network Science

Designing spatial networks, such as transport networks, commonly deals with the problem of how to best connect a set of locations through a set of links. In practice, it can be crucial to order the implementation of the links in a way that facilitates early functioning of the network during growth, like in bicycle networks. However, it is unclear how this early functional structure can be achieved by different growth processes. Here, we systematically study the growth of connected planar networks, quantifying functionality of the growing network structure. We compare random growth with various greedy and human-designed, manual growth strategies. We evaluate our results via the fundamental performance metrics of directness and coverage, finding non-trivial trade-offs between them. Manual strategies fare better than greedy strategies on both metrics, while random strategies perform worst and are unlikely to be Pareto efficient. Centrality-based greedy strategies tend to perform best for directness but are worse than random strategies for coverage, while coverage-based greedy strategies can achieve maximum global coverage as fast as possible but perform as poorly for directness as random strategies. Directness-based greedy strategies get stuck in local optimum traps. These results hold for a number of stylized urban transport network topologies. Our insights are crucial for applications where the order in which links are added to a spatial network is important, such as in urban or regional transport network design problems.

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