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Are vehicle trajectories simulated by dynamic traffic models relevant for estimating fuel consumption?

Authors :
Bruno Jeanneret
Céline Parzani
Rochdi Trigui
Arnaud Can
Thamara Vieira da Rocha
Ludovic Leclercq
Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE)
Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)
Environnement, Aménagement, Sécurité et Eco-conception (IFSTTAR/AME/EASE)
PRES Université Nantes Angers Le Mans (UNAM)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)
Laboratoire Transports et Environnement (IFSTTAR/AME/LTE)
Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon
Source :
Transportation Research Part D: Transport and Environment, Transportation Research Part D: Transport and Environment, Elsevier, 2013, 24, pp. 17-26. ⟨10.1016/j.trd.2013.03.012⟩
Publication Year :
2013
Publisher :
HAL CCSD, 2013.

Abstract

This paper questions the relevance of microscopic traffic models for estimating the impact of traffic strategies on fuel consumption. Urban driving cycles from the ARTEMIS database are simplified into piecewise linear speed profiles to mimic the classical outputs of microscopic traffic flow models. Fuel consumption is estimated for real and simplified trajectories and links between kinematics and the fuel consumption errors are investigated. Simplifying trajectories causes fuel consumption underestimation, from −1.2 to −5.2% on average according to the level of simplification; errors can approach −20% for some cycles. A focus on kinematic phases indicates that the maximum speed reached and the time decelerating are the main influences on fuel consumption. Finally, in the case where maximum speeds are estimated correctly, it is shown that errors committed at each kinematic phase when acceleration distributions are approximated by their mean values, converge towards small errors over complete cycles. A method is developed to quantify and reduce these errors.

Details

Language :
English
ISSN :
13619209
Database :
OpenAIRE
Journal :
Transportation Research Part D: Transport and Environment, Transportation Research Part D: Transport and Environment, Elsevier, 2013, 24, pp. 17-26. ⟨10.1016/j.trd.2013.03.012⟩
Accession number :
edsair.doi.dedup.....fa1206b665d98292a8de64e69b812159