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Generating traffic flow and speed regional model data using internet GPS vehicle records.
Ibarra-Espinosa, Sergio; Ynoue, Rita; Giannotti, Mariana; Ropkins, Karl; de Freitas, Edmilson Dias.
Afiliação
  • Ibarra-Espinosa S; Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Brazil.
  • Ynoue R; Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
  • Giannotti M; Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Brazil.
  • Ropkins K; Laboratory of Geoprocessing, Escola Politécnica (Poli), Universidade de São Paulo, Brazil.
  • de Freitas ED; Institute for Transport Studies, University of Leeds, United Kingdom.
MethodsX ; 6: 2065-2075, 2019.
Article em En | MEDLINE | ID: mdl-31667105
Nowadays, many smart-phones and vehicles are equipped with Global Position System (GPS) for tracking and navigation purposes, providing an opportunity to derive highly representative local vehicular flow and estimate vehicular emissions information. Here, we report and discuss methods used to handle large volumes of such activity data, namely 124 million GPS recordings from the web page Maplink.com.br, extract high spatial resolution vehicular flow information for a vast area in South-east Brazil, and correct for bias using traffic counts observations for the same area. The method consists in filter speed and accelerations, assign buffers to the road network, aggregate speed by street, fill missing number of lanes, generate traffic flow. Methods presented here were used to inform traffic-related air quality modelling and used as part of local air pollution management activities but are also amenable to any work that would be enhanced by more locally representative or time-resolved inputs for traffic flow, e.g. traffic network management, and demand modelling. •124 million GPS observations from electronic devices were used to generate traffic flow.•Spatial bias was investigated and accounted for using independent local traffic count data.•Traffic count rescaled GPS traffic flow provide a robust description of spatial and quantitative traffic patterns.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: MethodsX Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: MethodsX Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda