Evaluating Microtrip Definitions for Developing Driving Cycles

Abstract

Climate change has become one of the most critical environmental concerns of the past decades, with greenhouse gas (GHG) emissions being identified as the main culprit. Globally, policy makers have been trying to reduce GHG emissions through various policies and strategies. Given that in North America transportation accounts for 30% of total emissions, it has become the focus of attention for GHG reduction initiatives. The use of emissions models is necessary to assess the potential impact of those initiatives. The main component for emissions measurement and estimation is the driving cycle, which can be summed up as the speed profile that represents driving behaviors. The accuracy of estimations of emissions strongly depends on the accuracy of the driving cycles used; using inaccurate driving cycles would not be representative of real-world driving patterns and could provide erroneous results, even if the model used were the most reliable possible. Driving-cycle development has different steps, one being to divide the speed profiles into smaller sections called microtrips. There are several methods for establishing the parameters of the microtrips created; in this study, such methods, as well as a new one based on distance, were compared to determine which method could result in the most accurate driving cycle. The results show that microtrips based on spatial characteristics provide more representative driving cycles, whereas among spatial characteristics, distance-based approaches resulted in the most accurate driving cycle.