Understanding the effects of traffic congestion on emissions from urban buses. An integrated approach incorporating real-world emissions, naturalistic driving profiles, and city traffic sensor data

Resumen

In cities around the world, fossil fuel-powered buses have been key components in urban transport systems and hence, are important sources of CO2 and NOx emissions. Consequently, over the past decade, the European Union (EU) has introduced successive measures to reduce the energy consumption and emissions from heavy-duty vehicles (HDVs), including buses. Until recently, HDV engines were subjected to pollutant emissions type-approval tests, based on standardised driving cycles. However, these results do not reflect the performances of the buses and their engines when operating under real-world traffic conditions. The operation of urban buses is mainly characterised by low-speed driving conditions and a high frequency of stop-start events. These travel characteristics lead to increased energy consumption and emissions for fossil-fuelled buses, especially in congested urban areas. In this context, the main objective of this thesis is to understand the effects of urban traffic congestion on the CO2 and NOx emissions from fossil fuel-powered buses, based on developing an integrated approach incorporating to real-world emissions, naturalistic driving profiles, and city traffic sensor data. A representative bus route in Madrid (Route 74) is chosen to represent typical conditions of urban traffic congestion. In general, the development of this research addresses the variations in bus emissions owing to urban traffic congestion. In this study, these emission variations were analysed at three levels and/or phases: engine, vehicle, and route. Each phase also included a novel aspect and/or procedure; these were determined based on the gaps identified in a literature review. In addition, the data and procedures resulting from these three levels of analysis were systematically articulated to define a single integrated methodological research framework for achieving the defined overall objective. In the first phase, a Euro V diesel bus was tested in real-world urban traffic conditions, aiming to investigate the fuel efficiency and emission performance of its engine in terms of energy (g/kWh). For this purpose, this work developed a set of engine efficiency and emissions maps, based on combining transient engine data obtained directly from on-board diagnostic (OBD) systems and real-world emissions obtained with a portable emission measurement system (PEMS). Owing to the variability of the transient engine data, this work proposed a method for developing these engine maps, consisting of grouping the measured data into grids based on engine speed and torque ranges, and then averaging them to obtain a single value (either fuel consumption or emissions) per grid. This method was also characterised by a low computational cost. In the second phase, this work investigated the real-world fuel consumption and emissions of a Euro V diesel bus and Euro VI compressed natural gas (CNG) bus, with different levels of congestion (by link-average speed), passenger loads, and road gradients. The fuel consumption and emission factors were calculated in terms of distance (g/km), using PEMS-measured data. Additionally, these PEMS data were combined with a vehicle specific power (VSP) approach to study the differences between CNG and diesel buses, and to develop an empirical micro-emission model. Notably, both the resulting VSP-based emissions model and the engine-engine maps from the first phase could be used to accurately simulate the emissions of a bus under different operating scenarios, depending on the available input data. In the last phase, the CO2 and NOx emissions of diesel and CNG buses were modelled for different congestion scenarios at the route level. For this, a complex process was conducted to integrate real-world emissions, naturalistic driving profiles, and traffic sensor information. The definition of the traffic scenarios was based on a K-means clustering analysis for classifying the collected naturalistic driving trips. For this purpose, through a correlation analysis, two traffic condition indicators were chosen as criteria for the K-means clustering. In parallel, the CO2 and NOx emissions for the naturalistic driving trips were estimated using the VSP micro-emission model developed in the second stage of this research. Finally, the clustering and modelled emissions data were combined, so as to quantify the effects of trip-level congestion on urban bus emissions. The findings of this work represent scientifically accurate information that can be used to improve estimations of bus emissions, and that can be used by policy makers to design strategies for achieving a successful transition to sustainable urban transport systems.