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Driving cycles are important parameters to estimate vehicle emissions. However, most previous driving cycles, which were developed at the city scale or even national scale, cannot resolve the emission variations affected by road types and thus might introduce large uncertainties in the emission estimation. In this study, we proposed a new approach based on road type-based (RT-based) driving cycles to improve the estimation of vehicle emissions. As a case study, RT-based driving cycles for passenger cars were developed using more than 600,000 s of GPS data collected through on-road tests in Guangzhou. Results showed that driving cycles varied across road types (urban arterial road, highway, and other urban road), which featured varied velocities, acceleration, deceleration, and driving mode percentages. The urban arterial road had the lowest velocity (18.7 km/h), but the largest creeping mode proportion (61%). The other urban road had the largest acceleration and deceleration, while the highway had the highest average velocity (43.2 km/h) but the lowest acceleration and deceleration. Evaluations revealed that RT-based driving cycles could accurately depict separate driving patterns and emission factors on different road types. In comparison, city-level driving cycles and standard driving cycles typically overestimated emission factors of highways but underestimated those of other road types in Guangzhou. Consequently, emissions of light-duty gasoline passenger cars could be underestimated by 33% in the downtown and overestimated by approximately 25% in and around the highways. This study highlights the development of RT-based driving cycles to accurately estimate vehicle emissions and characterize their spatial variations. |
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