Using natural driving experiments and Markov chains to develop realistic driving cycles
通过自然驾驶实验发现交通和道路拓扑比驾驶员特征更影响驾驶风格，并用马尔可夫链方法开发驾驶循环，识别出26个关键指标以准确估算油耗和排放。
• Traffic and road topology dominates driving style not driver/vehicle characteristics. • We accurately reproduce the metrics and fuel economy of natural driving experiments. • We identify trade-offs in accuracy of reproducing vehicle dynamics and fuel economy. • We show the impact of natural driving variability on the accuracy of candidate cycles. • We identify a reduced set of 26 metrics that materially influence fuel economy. 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.