Article

Using on-board logging devices to study the long-term impact of an eco-driving course

Transportation Research Part D Transport and Environment (Impact Factor: 1.94). 10/2009; 14(7):514-520. DOI: 10.1016/j.trd.2009.05.009

ABSTRACT

In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network. The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.

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    • "efs , as argued by Carrico et al . ( 2009 ) , with respect to the economic benefits and environmental impacts of cold engine idling and restarting the engine . Therefore there is scope for policies aimed at providing drivers with appropriate information and incorporating the principles of efficient driving into driver training ( Barkenbus , 2010 ; Beusen et al . , 2009 ; Onoda , 2009 ) . At the same time , price incentives make this information as well as the implied behavioral changes economically meaningful and should therefore stimulate demand for them ."
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    ABSTRACT: The volume of pollution produced by an automobile is determined by driver's behavior along three margins: (i) vehicle selection, (ii) kilometers driven, and (iii) on-road fuel economy. The first two margins have been studied extensively, however the third has received scant attention. How significant is this 'intensive margin'? What would be the optimal policies when it is taken into account? The paper develops and analyzes a simple model of the technical and behavioral mechanisms that determine the volume emissions produced by a car. The results show that an optimal fuel tax would provide drivers with appropriate incentives along all three margins and that only public information is needed for a fuel tax to be set optimally. In contrast, an optimal distance tax would require private information. Lastly, relative to the optimal fuel tax, a simple uniform fuel tax is shown to be progressive. Thus, being already deployed worldwide, a uniform fuel tax is an attractive second-best policy. These findings should be accounted for when designing new mechanisms to alleviate motor vehicle pollution.
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    • "The eco-driving vehicles apply fuel-saving techniques to automatic control systems, e.g., adaptive cruise control (ACC), to achieve more efficient operations than human drivers, which requires good understanding of vehicle fuel consumption and load. This can be done by learning traits of fuel-efficient drivers, such as accelerating smoothly, maintaining adequate headway, eliminating excessive idling, etc. (Beusen et al., 2009; Zarkadoula et al., 2007). An alternative model-based approach is to determine fuel-saving strategies from vehicle longitudinal dynamics. "
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    • "Drivers were then able to save an average of 10.2% in fuel during the training period, while in the post-training period the overall reduction was of 4.35% much less than in training. Beusen et al. (2009) corroborate with these observations in the case of car drivers. Their fuel consumption was reduced by 5.8% after four-month training sessions but a few monitored drivers fell back into their original driving habits. "
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    ABSTRACT: Energy costs account for an important share of the total costs of urban and suburban bus operators. The purpose of this paper is to expand empirical research on bus transit operation costs and identify the key factors that influence bus energy efficiency of the overall bus fleet of one operator and aid to the management of its resources.We estimate a set of multivariate regression models, using cross-section dataset of 488 bus drivers operating over 92. days in 2010, in 87 routes with different bus typologies, of a transit company operating in the Lisbon's Metropolitan Area (LMA), Rodoviária de Lisboa, S.A.Our results confirm the existence of influential variables regarding energy efficiency and these are mainly: vehicle type, commercial speed, road grades over 5% and bus routes; and to a lesser extent driving events such as: sudden longitudinal decelerations and excessive engine rotation. The methodology proved to be useful for the bus operator as a decision-support tool for efficiency optimization purpose at the company level.
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Questions & Answers about this publication

  • Luc LR Int Panis added an answer in Traffic Control:
    Driver behaviour on the approach to signals - Who has any trajectory data other than NGSIM?

    I'm researching driver behaviour on the approach to signals and need more data on how drivers react and adjust their speed under the following conditions:

    1)  lead platoon with red signal - no queue at stopline

    2) lead platoon with green signal - queue visible and discharging

    3) lead platoon with green signal, anticipating onset of red before stop line is reached

    I am interested in the variability in reaction time, point of reaction, deceleration rates over 3 stages- @foot off, @brake applied, and @coasting to final stop

    Luc LR Int Panis

    The dataset we used for the analysis in this paper may be useful, but I don't know how you would be able to get traffic signal data in sync with the GPS logging?

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      [Show abstract] [Hide abstract]
      ABSTRACT: In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network. The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.
      Full-text · Article · Oct 2009 · Transportation Research Part D Transport and Environment