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Article

Using Floating Car Data to Analyse the Effects of ITS Measures and Eco-Driving

1
Transport Research Centre (TRANSyT), Escuela de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid 28040, Spain
2
Transport- Civil Eng. Department, Escuela de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Ciudad Universitaria, Madrid 28040, Spain
*
Author to whom correspondence should be addressed.
Sensors 2014, 14(11), 21358-21374; https://doi.org/10.3390/s141121358
Received: 22 September 2014 / Revised: 1 November 2014 / Accepted: 3 November 2014 / Published: 11 November 2014
(This article belongs to the Special Issue Positioning and Tracking Sensors and Technologies in Road Transport)
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. View Full-Text
Keywords: CO2 emissions; speed profiles; traffic simulation; vehicle tracking; smartphones; variable speed limits; section speed control; cruise control; eco-driving CO2 emissions; speed profiles; traffic simulation; vehicle tracking; smartphones; variable speed limits; section speed control; cruise control; eco-driving
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MDPI and ACS Style

Garcia-Castro, A.; Monzon, A. Using Floating Car Data to Analyse the Effects of ITS Measures and Eco-Driving. Sensors 2014, 14, 21358-21374. https://doi.org/10.3390/s141121358

AMA Style

Garcia-Castro A, Monzon A. Using Floating Car Data to Analyse the Effects of ITS Measures and Eco-Driving. Sensors. 2014; 14(11):21358-21374. https://doi.org/10.3390/s141121358

Chicago/Turabian Style

Garcia-Castro, Alvaro, and Andres Monzon. 2014. "Using Floating Car Data to Analyse the Effects of ITS Measures and Eco-Driving" Sensors 14, no. 11: 21358-21374. https://doi.org/10.3390/s141121358

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