Using natural driving experiments and Markov chains to develop realistic driving cycles

Abstract

The main purpose of driving cycles is to estimate accurately on-road fuel use and the associated emissions of greenhouse gases and other air pollutants by vehicles. Conventionally, driving cycles are developed using micro-trips, Markov chains, or hybrid approaches, with accuracy determined by comparing metrics of the candidate cycles with the observed data. Through a natural driving experiment, we suggest traffic and road topology have a dominant role in influencing individual driving styles, more so than driver age or gender, or vehicle characteristics. Using experimental data and a Markov chain approach, we make three contributions to driving cycle development. First, we identify a reduced set of 26 metrics which materially influence fuel economy. Second, we assess the trade-offs in accuracy between reproducing vehicle dynamics and fuel economy. Finally, we identify the impact of natural driving variability on the accuracy of candidate cycles.