arXiv subjects

Didier Josselin

Publications and source records attributed to Didier Josselin.

3 recordsLinked to original sources

arXiv2026

Unravelling Nature's Models for Transportation Network: Considering a Biomimicry Framework

Researchers worldwide have drawn inspiration from nature to optimize network design and dynamics. Some of the wonders of the living world exhibit remarkable abilities in generating efficient and resilient spatial structures. By mimicking biological strategies, transportation infrastructures could be profoundly rethought. This paper aims to provide the basis for a biomimicry framework for addressing transportation networks. In light of examples from the literature, the relevance of such a framework for advancing research in nature-inspired networks is demonstrated, with the aim of achieving resilience and efficiency.

physics.soc-ph↗

arXiv2016

Straightness of rectilinear vs. radio-concentric networks: modeling simulation and comparison

This paper proposes a comparison between rectilinear and radio-concentric networks. Indeed, those networks are often observed in urban areas, in several cities all over the world. One of the interesting properties of such networks is described by the \textit{straightness} measure from graph theory, which assesses how much moving from one node to another along the network links departs from the network-independent straightforward path. We study this property in both rectilinear and radio-concentric networks, first by analyzing mathematically routes from the center to peripheral locations in a theoretical framework with perfect topology, then using simulations for multiple origin-destination paths. We show that in most of the cases, radio-concentric networks have a better straightness than rectilinear ones. How may this property be used in the future for urban networks?

cs.SI↗

arXiv2015

Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums

This paper proposes a new method to provide personalized tour recommendation for museum visits. It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy. This project includes researchers from computer and social sciences. Some results are obtained with numerical experiments. They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour. This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.

cs.AI↗