Assessment of Commercial Vehicle Emissions and Vehicle Routing of Fleets using Simulated Driving Cycles

Growing concern over greenhouse gas emissions and their adverse effect on the environment has prompted research on identifying means to control and reduce these emissions across various sectors. In transportation, vehicle emissions are strongly correlated with driver behaviour. Currently, in research on relating driving behaviour with emissions, driving cycles that are assumed to be constant regardless of vehicle type for a specific area are used to analyse emissions. However, a more realistic model of driving behaviour would enable better estimation of such emissions which can consequently be used to support decision making and policy design.;Currently, in the field of transportation management, driving behaviour is generally modelled using driving cycles. Using a single driving cycle, however, is simplistic for emission estimation purposes as there is significant variance in the driving behaviour of different vehicle types on various road types.;In this research, microsimulation models are used to generate road and vehicle specific driving cycles to improve emission estimation. Typically, the calibration of microsimulation models are carried out using vehicle counts and, in some cases, average speed of vehicles on roads. This research uses a genetic algorithm to show that by calibrating a microsimulation model against acceleration data in addition to average speed and vehicle count, the model would provide a more accurate representation of driver behaviour. This claim is validated by comparing simulation results with observed values of 13 different parameters related to driving behaviour that were highlighted as influencing factors on emissions and the acceleration-calibrated microsimulation model is shown to be an efficient tool for determining a rich array of driving cycles. The model is then used to create improved driving cycles for various types of vehicles on different types of roads. Use of simulation allows data to be collected under consistent traffic conditions for all vehicle and road types. It is also shown that using simulated driving cycles produces emission factors that are closer to the observed compared to using the average speed model.;Finally, in order to provide a demonstration of the application of these driving cycles, a green routing problem has been used as a test case. In this test case, the driving cycles generated from the acceleration-calibrated model have been used to optimize the routes of a hypothetical delivery company to minimize their emissions and costs under various circumstances incorporating the effect of vehicle load in estimating emissions. Results show statistically significant differences in total distance travelled, driving time, and CO 2-eq emitted as the result of different minimization criteria of distance-, time-, and emissions optimal. As a result of the test case optimization, it is shown that by using the proposed approach, intricate and insightful analysis of emissions under various policies of cap-and-trade vs. carbon taxing can be conducted with good accuracy without requiring technology changes or other investments which may be expensive.;Keywords: Emission modelling, microsimulation calibration, driving cycles, vehicle routing, carbon pricing.