Abstract

With the help of technologies such as GPS tracks, GIS and open data is now possible to study people travel behaviour in a new way. Nowadays, large datasets can be easily handled thanks to databases and better visualized using GIS. Moreover, the availability of GPS data, open data and VGI makes accessible a lot of new information, which was not obtainable before. This research is about the analysis of mobility patterns in different neighbourhoods in three cities in the Netherlands. The study is based on the validation of mobility theoretical performances by the actual performances measured through the analysis of GPS logs of households, who live in the neighbourhoods. A series of spatial indicators based on proximity, density and accessibility are computed in order to assess the theoretical performances of the neighbourhoods. To achieve this task, information about built environment characteristics and infrastructure networks is retrieved from OpenStreetMap, and other datasets. In the end the neighbourhoods are classified into five classes, according to the different levels of performances in terms of sustainable mobility. In such a way, it is possible to better understand the key factors that influence actual people travel patterns, providing policy makers with accurate information about the real movement of people.

Abstract

With the help of technologies such as GPS tracks, GIS and open data is now possible to study people travel behaviour in a new way. Nowadays, large datasets can be easily handled thanks to databases and better visualized using GIS. Moreover, the availability of GPS data, open data and VGI makes accessible a lot of new information, which was not obtainable before. This research is about the analysis of mobility patterns in different neighbourhoods in three cities in the Netherlands. The study is based on the validation of mobility theoretical performances by the actual performances measured through the analysis of GPS logs of households, who live in the neighbourhoods. A series of spatial indicators based on proximity, density and accessibility are computed in order to assess the theoretical performances of the neighbourhoods. To achieve this task, information about built environment characteristics and infrastructure networks is retrieved from OpenStreetMap, and other datasets. In the end the neighbourhoods are classified into five classes, according to the different levels of performances in terms of sustainable mobility. In such a way, it is possible to better understand the key factors that influence actual people travel patterns, providing policy makers with accurate information about the real movement of people.