sc 17(6): e1

Research Article

Evaluating urban neighbourhoods in terms of mobility performances, using open data and GPS tracks to assess actual people travel behaviour

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  • @ARTICLE{10.4108/eai.20-12-2017.153495,
        author={Matilde Oliveti and Stefan van der Spek},
        title={Evaluating urban neighbourhoods in terms of mobility performances, using open data and GPS tracks to assess actual people travel behaviour},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={2},
        number={6},
        publisher={EAI},
        journal_a={SC},
        year={2017},
        month={12},
        keywords={GPS tracking, Open Data, OpenStreetMap, People Travel Behaviour, Mobility Patterns, Neighbourhoods},
        doi={10.4108/eai.20-12-2017.153495}
    }
    
  • Matilde Oliveti
    Stefan van der Spek
    Year: 2017
    Evaluating urban neighbourhoods in terms of mobility performances, using open data and GPS tracks to assess actual people travel behaviour
    SC
    EAI
    DOI: 10.4108/eai.20-12-2017.153495
Matilde Oliveti1, Stefan van der Spek1
  • 1: TUDelft University

Abstract

Managing urban areas has become one of the most important development challenges of the 21st century. Building sustainable cities is a major factor nowadays. In this context, the advent of technologies such as GPS (Global Positioning System) and GIS (Geographical Information Systems) enables to better address the relationship between urban form and people travel behaviour. Spatial and temporal data can be collected at once, giving an insight into the actual movement pattern of people. In this paper we make a contribution to the existing literature in mobility and urban studies by comparing a series of GIS-based neighbourhood indicators with the actual people travel behaviour detected by GPS survey. Information about built environment characteristics is retrieved by OpenStreetMap and other datasets. 10 different neighbourhoods in The Netherlands are compared and in the end the main features that characterize efficient neighbourhoods in terms of sustainable mobility patterns are identified.