Comparison of three methodologies for driving cycles construction

Driving cycles are frequently used for estimating the energy consumption and environmental impact of land vehicles. Currently exists an increasing interest in driving cycles that accurately represent the driving patterns of a given region obtained through a repeatable and reproducible methodology. This work compared the procedure and the resulting cycles from three methodologies used in the generation of representative driving cycles. For this purpose, 3 monitoring campaigns were developed, recording driving variables in a fleet of 15 transit busses. From the 138 journeys sampled, a database was built with on-road driving data: speed, road gradient and fuel consumption, which were sampled at a frequency of 1 Hz on roads with three different levels of service. This data set was analysed through two methodologies based on stochastic processes, Micro-trips and Markov process theory, and through a deterministic methodology called Minimum Weighted Difference - Characteristic Parameters (MWD-CP). We found that both stochastic approaches are reproducible, but not repeatable. This means that the resulting driving cycle is different every time the methods are applied. Hence, the speed-time profile does not remain constant. Even if their global characteristics, such as average speed, are closely the same, the local characteristics in short time intervals are not the same, entailing variances in fuel consumption and emissions results. On the other hand, the MWD-CP defines as typical and representative driving cycle, the whole trip that best fits the overall sample data. The MWD-CP is a reproducible and repeatable methodology, which means that with the same set of driving data, it produces the same result even if the methodology is applied several times. As consequence, a constant speed-time profile for a specific area or region is defined while the average and local characteristics are preserved, avoiding variances in fuel consumption and emissions estimations.