Using floating car data to analyse the effects of ITS measures and eco-driving

García Castro, Alvaro y Monzón de Cáceres, Andrés (2014). Using floating car data to analyse the effects of ITS measures and eco-driving. "Sensors", v. 14 (n. 11); pp. 21358-21374. ISSN 1424-8220. https://doi.org/10.3390/s141121358.

Descripción

Título: Using floating car data to analyse the effects of ITS measures and eco-driving
Autor/es:
  • García Castro, Alvaro
  • Monzón de Cáceres, Andrés
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: Noviembre 2014
Volumen: 14
Materias:
Palabras Clave Informales: CO2 emissions; speed profiles; traffic simulation; vehicle tracking; smartphones; variable speed limits; section speed control; cruise control; eco-driving
Escuela: Centro de Investigación del Transporte (TRANSyT) (UPM)
Departamento: Ingeniería Civil: Transporte y Territorio
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The road transportation sector is responsible for around 25% of total man-made CO2 emissions worldwide. Considerable efforts are therefore underway to reduce these emissions using several approaches, including improved vehicle technologies, traffic management and changing driving behaviour. Detailed traffic and emissions models are used extensively to assess the potential effects of these measures. However, if the input and calibration data are not sufficiently detailed there is an inherent risk that the results may be inaccurate. This article presents the use of Floating Car Data to derive useful speed and acceleration values in the process of traffic model calibration as a means of ensuring more accurate results when simulating the effects of particular measures. The data acquired includes instantaneous GPS coordinates to track and select the itineraries, and speed and engine performance extracted directly from the on-board diagnostics system. Once the data is processed, the variations in several calibration parameters can be analyzed by comparing the base case model with the measure application scenarios. Depending on the measure, the results show changes of up to 6.4% in maximum speed values, and reductions of nearly 15% in acceleration and braking levels, especially when eco-driving is applied.

Más información

ID de Registro: 33345
Identificador DC: http://oa.upm.es/33345/
Identificador OAI: oai:oa.upm.es:33345
Identificador DOI: 10.3390/s141121358
URL Oficial: http://www.ncbi.nlm.nih.gov/pubmed/25393787
Depositado por: Memoria Investigacion
Depositado el: 11 Abr 2015 11:10
Ultima Modificación: 11 Abr 2015 11:10
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