This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …

A spatiotemporal approach that simultaneously utilises both spatial and temporal
relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate …

We propose an approach to forecast the short-term passenger flows of the urban rail
network of Paris. Based on dynamic Bayesian networks, this approach is designed to …

Predicting how changes to the urban environment layout will affect the spatial distribution of
pedestrian flows is important for environmental, social and economic sustainability. We …

The idea that quantum effects could be harnessed to allow faster computation was first
proposed by Feynman. As of 2020 we appear to have achieved 'quantum supremacy', that …

Betweenness is a measure long used in spatial network analysis (SpNA) to predict flows of
pedestrians and vehicles, and more recently in public health research. We improve on this …

A dynamic Bayesian network approach is proposed for short-term passenger flow
forecasting. The graphical structure is based on the causal relationships between the flows …

This paper develops spatial network metrics that contribute to analysis of regional
development. We use the sDNA software to derive longitudinal road network density and …

This paper is built around a research project developed with the support of the Regional
Planning Authority of the Algarve Region in Portugal which assessed mobility patterns …

La maturité de la télématique et des technologies de l'information et la communication (TIC),
ainsi que l'avènement du big data dans le transport ont conduit à des développements …