Genetic Algorithm Based Microscale Vehicle Emissions Modelling

Sicong Zhu,Lisian Tey,Luis Ferreira

doi:10.1155/2015/178490

Abstract

There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

Highlights

Fuel consumption and emission estimation can be critical for comprehensive transportation planning
The proposed genetic algorithm (GA) approach was applied to the HC pollutant for different driving modes, namely, acceleration, cruising, and deceleration
All the predicting variables selected are statistically significant at P < 0.05. Both the instantaneous traffic emissions model (see (1)) and the newly modified equation parameters were calibrated by least-square regression on the same test-bed dataset that was used to develop the new model

Summary

Introduction

Fuel consumption and emission estimation can be critical for comprehensive transportation planning. Microscopic traffic models should integrate real time emission prediction models, which are able to utilize high-resolution transportation modelling results, generating potentially more precise emission estimations. There have been a number of modelling approaches on microlevel proposed to estimate future vehicle emissions in conjunction with the outputs of transport models One such approach is the use of engine power as the main predictive basis. The data used for analysis in the current paper were extracted from the Australian national in-service emissions study (NISE2) [15], which was developed using a Composite Urban Emissions Drive Cycle (petrol CUEDC). The emission rates for CO2, CO, HC, and NOx of the test-bed vehicles from the NISE2 fleet, which travels on the composite urban driving cycle (CUEDC), were recorded second-by-second in addition to the instantaneous speed.

Development of Emission Models

Findings

Conclusions