How Simplifying Urban Driving Cycles Influence Fuel Consumption Estimation?

Optimizing traffic management systems requires the development of dynamic traffic flow models capable of estimating environmental externalities. However, such models only produce simplified trajectories. Therefore they cannot be directly coupled with traditional emission models based on real trajectories, i.e. observed experimentally. The main objective of this research is to evaluate the impacts of using simplified instead of real trajectories as an input for a fuel consumption model, Vehlib library, developed at IFSTTAR/LTE.Driving cycles were selected from 37 ARTEMIS urban driving cycles and processed. The resulting driving cycles were then simplified to make them correspond to the classical outputs of microscopic traffic flow models, i.e. piecewise linear speed profiles. The simplification method used is based on a genetic algorithm with a given number of break points. Reducing the number of such points leads to several levels of simplification. The fuel consumption is then estimated for each simplified driving cycle and its original.A deeper analysis is finally provided to evaluate the error between fuel consumption estimations for original and simplified sub-cycles. The first results show average error equal to -1.27% for simplified sub-cycles with average RMSE equals to 0.90 km/h. According to the level of simplification, the average error is equal to -0.13% at fine level, -0.77% at intermediary level and -2.42% at coarse level. A complementary analysis is also provided studying individually several sub-cycles to figure out which kinds of simplification have the main influence on fuel consumption.