Cars with petrol-driven internal combustion engines are sources of air pollution. Until alternative car engines will replace petrol-driven engines, road transportation is a major source for emissions of carbon monoxide, carbon dioxide, hydrocarbons, and many other organic compounds into the environment. There is a direct relation between the car's emissions and its acceleration: an accelerating car will pollute more than a non-speeding car. In this paper is presented a mobile system capable of guiding the driver's decisions with the goal of reducing vehicle emissions. The system considers parameters ranging from the car's characteristics to human reactions. The results are demonstrating the capability of the system to produce decisions that reduce pollution in urban traffic environment. The solution minimizes the number of stop-starts due to the red light and the accelerations needed to catch the green light (happening quite frequent and having an important influence on the emissions rate). Periodically the traffic lights broadcast information about the status of the current traffic light color. This information is used by a decision algorithm. The role of this algorithm is to assist the driver make informed decisions to 1) avoid the red traffic light and 2) catch the green traffic light, if possible, and reduce the quantity of emitted gases. In order to decide whether the driver's action of catching the green light leads to less fuel consumption, by recommending accelerate/decelerate, is devised a method to predict the car's movement. For this we use the motion equation of a car to predict its speed and position at any time. The prediction of the movement of the car is among the most challenging part. To estimate a specific driver's behavior and predict how the car is going to move in different situations is a difficult task, because of the number of parameters to be considered: all forces that act on the car, coupled with the human factor. In the end we proposed a solution that was evaluated in an implementation on top of the VNSim simulator. The obtained results are promising and show that the proposed algorithm can recommend speeds that, in fact, lead to a decrease in the emissions of a car.


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    Title :

    Intelligent traffic lights to reduce vehicle emissions


    Contributors:


    Publication date :

    2012


    Size :

    8 Seiten, 6 Bilder, 16 Quellen


    Remarks:

    (nicht paginiert)



    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English




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