Simulation Methods in Urban Infrastructure Systems
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Urbanization and public transport infrastructure coordination has become one of the leading principles of sustainable urban growth and mobility transition. This paper proposes a critical reflection on the implementation of the Transit-Oriented Development ...
Louvain research institute for Landscape, Architecture, Built environment2023
Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...
Cities have been shaped by the exchange of food goods. The market hall was the building where the market took place as a social and economic activity. Their slow disappearance in the 20th Century was mostly due to a shift in consumption pattern as well as ...
Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...
WaterShed is an urban structure that takes advantage of usable rainwater to cool ambient air in pedestrian areas, publics spaces and transportation hubs. The system addresses pedestrian thermal comfort in conditions of heatwave, which can be life threateni ...
Contemporary urbanism, as a science and ideology, sometimes induces a violence of urbanization that is exercised through formal planning on informal settlements. The latter are marginalized through processes of (in)visibility that exclude them from the cit ...
One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...
The Covid-19 pandemic brought to light the social inequalities and exposed frailties in the access to certain fundamental human rights, such as access to health and mobility. The type of housing, sanitation level, mobility, and urban structures play an ess ...
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...
Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...
