ARTÍCULO
TITULO

A multi-scale fine-grained LUTI model to simulate land use scenarios in Luxembourg

Philippe Gerber    
Geoffrey Caruso    
Eric Cornelis    
Cyrille Médard de Chardon    

Resumen

The increasing attractiveness of Luxembourg as a place to work and live puts its land use and transport systems under high pressure. Understanding how the country can accommodate residential growth and additional traffic in a sustainable manner is a key and difficult challenge that requires a policy relevant, flexible and responsive modelling framework. We describe the first fully-fletched land use and transport interaction framework (MOEBIUS) applied to the whole of Luxembourg. We stress its multi-scalar nature and detail the articulation of two of its main components: a dynamic demographic microsimulation at the scale of individuals and a micro-spatial scale simulation of residential choice. Conversely to traditional zone-based approaches, the framework keeps full details of households and individuals for residential and travel mode choice, making the model highly consistent with theory. In addition, results and policy constraints are implemented at a very fine resolution (20m) and can thus incorporate local effects (residential externalities, local urban design). Conversely to fully disaggregated approaches, a linkage is organized at an intermediate scale, which allows (i) to simplify the generation and spatial distribution of trips, (ii) to parallelise parts of the residential choice simulation, and (iii) to ensure a good calibration of the population and real estate market estimates. We show model outputs for different scenarios at the horizon 2030 and compare them along sustainability criteria.

 Artículos similares

       
 
Shufang Lu, Funan Lu, Xufeng Shou and Shuaiyin Zhu    
Accurate human body profiles have many potential applications. Image-based human body profile estimation can be regarded as a fine-grained semantic segmentation problem, which is typically used to locate objects and boundaries in images. However, existin... ver más
Revista: Applied Sciences

 
Gabriele Accarino, Marco Chiarelli, Francesco Immorlano, Valeria Aloisi, Andrea Gatto and Giovanni Aloisio    
One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide new... ver más
Revista: AI

 
Arunabha M. Roy and Jayabrata Bhaduri    
In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current... ver más
Revista: AI

 
Fan Yang, Deming Yang, Zhiming He, Yuanhua Fu and Kui Jiang    
Upgrading ordinary streetlights to smart streetlights to help monitor traffic flow is a low-cost and pragmatic option for cities. Fine-grained classification of vehicles in the sight of smart streetlights is essential for intelligent transportation and s... ver más
Revista: Algorithms

 
Haydn Lawrence, Colin Robertson, Rob Feick and Trisalyn Nelson    
Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplac... ver más