Abstract

The current climate and environmental emergency, together with the growing traffic congestion and pollution in urban areas, make mobility and its sustainability a priority in current transport policies. It is essential to change citizen’s behaviour in order to increase the use of less pollutant, economic and egalitarian transport modes, such as walking, combining it with other public transport modes. For this change to happen, it is necessary to provide feasible alternatives to private cars, namely through the offer of high-quality pedestrian infrastructures, adapted to the cities’ specific characteristics and their citizen’s needs. These aspects are particularly important in hilly cities, where traveling by foot requires an additional effort. The present study aims to contribute to the promotion of soft mobility in hilly cities by creating a support instrument to assess the potential of existing pedestrian infrastructures. Three variables are considered in the analysis: trip generation poles, population density and pedestrian network characteristics, with especial consideration of slopes. These variables were processed with spatial and network analysis tools available in Geographic Information Systems (GIS) and combined using a multi-criteria decision analysis to obtain a measure of the pedestrian infrastructure potential. The identification of areas with high pedestrian potential supports the definition of priority intervention programs on the public space and a better allocation of human and financial resources. The proposed instrument was validated through its application to a case study, the hilly city of Covilhã (Portugal). From the results obtained it is possible to conclude that the variable with more impact on the pedestrian infrastructure suitability value is the location of the trip generation poles, influenced by the footpaths’ longitudinal slopes. The instrument also allowed to identify the city’s main expansion areas, corresponding to places presenting a good pedestrian potential and relatively low values of population density.

Full Text
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