Frederic Reynaud

TL;DR: In this article, an integrated model for the prediction of exposure to traffic related air pollution in an urban area as a result of transport policy scenarios is presented. But, the model is not suitable for large-scale urban areas.

TL;DR: A behaviorally quantitative model that accommodates for the influence of temporal, meteorological, bicycle infrastructure, built environment and land-use attributes on bicycle availability is developed and can be employed by BSS operators to arrive at hourly system state predictions and used for rebalancing operations.

TL;DR: This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains.

TL;DR: The analysis shows that new infrastructure in Montreal will reduce the overall burden of transportation by 2.5 DALYs per 100,000 persons, caused by the reduction of road traumas occurring in the Inner suburbs and central Montreal region as well as gains in active transportation in the inner suburbs.

TL;DR: The Network Robustness Index (NRI) was one of the first tools to successfully assess networkwide congestion effects due to single-link closures as mentioned in this paper, and was used to assess network robustness.