Investigating the influence of highway traffic flow condition on pollutant emissions using driving simulators

Abstract

In the last 20 years the attention of international organizations towards air pollution has been improved, leading to definition of laws and regulations. In order to evaluate strategies and policies, forecasting tools have been adopted by institutions. Currently, the estimation of traffic emissions is based on static models, in which the amount of pollutant is computed as a function of average parameters obtained on a single road stretch.The well-known traffic increase of recent years has significantly changed the actual flow conditions, producing a strong rise of interferences. As this facet affects the operating condition of each vehicle, the use of a standard emission model at high traffic interference can lead to some inaccuracies. In such cases, instantaneous emission models introduce deeper capabilities; essentially, the pollutant prediction is directly tied to the engine vehicle operation point in reallike traffic condition. This second modelling approach has been adopted in the current work. A complete lumped parameter vehicle model has been built to be used as a virtual on-road emission/fuel consumption test unit. Investigations have highlighted the dependence of emission level and fuel consumption on drivers' behaviour; indeed, the analysis took advantage of the experiments carried out in the virtual reality laboratory: on a typical highway geometry, characterized by a dual lane carriageway with three lanes each, three different flow conditions have been simulated. Once the relationship between highway interference level and drivers' behaviour has been evidenced (in terms of emissions and fuel consumption), a relation between highway interference level and emissions/fuel consumption has been highlighted. Finally, in order to assess the differences between static and instantaneous emission models, a comparative analysis has been carried out.

Road Emissions in London: Insights from Geographically Detailed Classification and Regression Modelling

“…Static and dynamic models are further classified into (1) traffic situation, (2) instantaneous, (3) average speed and (4) aggregate emission factor models [22]. The first three classes are also introduced in [23] and are generally accepted and widely used in the literature, although some studies use different classifications. For example, [21] introduce variations of average speed and instantaneous models, [24] classify the models based on the type of emissions, while [25] do so based on input data, study scale and type of pollutants being modelled.…”

Coupling Virtual Reality Simulator with Instantaneous Emission Model: A New Method for Estimating Road Traffic Emissions

“…Thus, a promising step forward can be represented by the coupling between a virtual reality driving simulator and a complete model of a vehicle, having the capability to predict the transients of emissions and fuel consumption depending on drivers' behavior while driving. A virtual reality driving simulator is an advanced tool capable of investigating how traffic flow conditions combined with geometrical characteristics of roads can influence drivers' behavior [21]. Among the major advantages of this tool, it is worthwhile to mention the possibility of repeating an identical set of simulations while varying a single parameter, which allows its actual influence on traffic emissions to be assessed.…”