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Journal Articles IEEE Transactions on Intelligent Transportation Systems Year : 2019

A stochastic data-based traffic model applied to vehicles energy consumption estimation

Abstract

A new approach to estimate traffic energy consumption via traffic data aggregation in (speed,acceleration) probability distributions is proposed. The aggregation is done on each segment composing the road network. In order to reduce data occupancy, clustering techniques are used to obtain meaningful classes of traffic conditions. Different times of the day with similar speed patterns and traffic behavior are thus grouped together in a single cluster. Different energy consumption models based on the aggregated data are proposed to estimate the energy consumption of the vehicles in the road network. For validation purposes, a microscopic traffic simulator is used to generate the data and compare the estimated energy consumption to the reference one. A thorough sensitivity analysis with respect to the parameters of the proposed method (i.e. number of clusters, size of the distributions support, etc.) is also conducted in simulation. Finally, a real-life scenario using floating car data is analyzed to evaluate the applicability and the robustness of the proposed method.
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Dates and versions

hal-01774621 , version 1 (23-04-2018)
hal-01774621 , version 2 (26-11-2018)
hal-01774621 , version 3 (04-09-2019)

Identifiers

Cite

Arthur Le Rhun, Frédéric Bonnans, Giovanni de Nunzio, Thomas Leroy, Pierre Martinon. A stochastic data-based traffic model applied to vehicles energy consumption estimation. IEEE Transactions on Intelligent Transportation Systems, In press, ⟨10.1109/TITS.2019.2923292⟩. ⟨hal-01774621v3⟩
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