@article{
 title = {Analyzing the configuration of multimodal urban networks},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 pages = {368-391},
 volume = {46},
 id = {c6a29329-437e-399f-94fd-4868472530db},
 created = {2018-03-13T09:36:48.357Z},
 file_attached = {false},
 profile_id = {13f51d71-4d38-3625-9c41-8cba7418c0e7},
 group_id = {8c9695d2-3561-35b8-b3be-e18eb82c961b},
 last_modified = {2018-06-07T02:52:24.486Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {de610a62-40ae-4339-a9e3-c56949460071},
 private_publication = {false},
 abstract = {This article proposes urban network models as instruments to measure urban form, structure and function indicators for the assessment of the sustainable mobility of urban areas, thanks to their capacity to describe the detail of a local environment in the context of a wider city-region. Drawing from the features of existing street network models that offer disaggregate, scalable and relational analysis of the spatial configuration of urban areas, it presents a multi-modal urban network (MMUN) model that describes an urban environment using three systems — private transport (i.e. car, bicycle and pedestrian), public transport (i.e. rail, tram, metro and bus), and land use. This model offers a unifying framework that allows the use of a range of analysis metrics and conceptions of distance (i.e. physical, topological and cognitive), and aims to be simple and applicable in practice. An implementation of the MMUN is created for the Randstad city- region in the Netherlands. This is analyzed with network centrality measures in a series of experiments, testing its performance against empirical data. The experiments yield conclusions regarding the use of different distance parameters, the choice of network centrality metrics, and the relevant combinations of multi-modal layers to describe the structure and configuration of a city-region},
 bibtype = {article},
 author = {Gil, Jorge},
 journal = {Geographical Analysis},
 number = {4}
}

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