This paper examines the effects of two traffic management measures, speed limit reduction and coordinated traffic lights, in a case study area in Antwerp, Belgium. For this purpose, an integrated model that combines the microscopic traffic simulation model Paramics with the CO 2 and NO x emission model VERSIT+ is constructed and validated. On the one hand, reductions in CO 2 and NO x emissions in the order of 25 % were found if speed limits are lowered from 50 to 30 km/h in the residential part of the case study area. On the other hand, reductions in the order of 10 % can be expected from the implementation of a green wave signal coordination scheme along an urban arterial road.

The need for accurate emissions measurements has coerced researchers into trying to reconstruct the true transient emission signal from that measured by the analyser. This paper discusses two such methods and examines the validity of those methods by testing them with real-time emissions data. The first method is the sequential inversion technique, which tries to reconstruct the input second by second, based on the measured response at each second and the dispersion characteristics of the analyser. The reconstruction was found to be accurate, but there were some constraints associated with the dispersion characteristics and the reconstruction failed if there was signal noise. The second method, the differential coefficients method (DCM) of Ajtay and Weilenmann, reconstructs the input signal by approximating the analyser input as a linear combination of the output and the output derivatives. When tested with real-time data, the DCM predicted the emission signal even when there was noise imposed on the signal. While the DCM is clearly a better prediction technique, the accuracy of the DCM is reduced when noise is added to the analyser input. The DCM, when coupled with cross-correlation techniques, can be a powerful tool in retrieving ‘lost’ information associated with the measurement delays and dispersion characteristics of the analyser.

Methods for Reconstruction of Transient Emissions from Heavy-Duty Vehicles Madhava R. Madireddy Emissions measurement analyzers give out a response that may not reflect the true instantaneous engine-out emissions. Currently, the heavy-duty diesel engines are being certified for emissions measured in a thirty second time window with certain specification requirements for the analyzers. Since these measured emissions values may not be the same as the true instantaneous emissions, integrated values for the thirty second windows may be affected by analyzer response. This document presents and examines reconstruction techniques to estimate instantaneous heavy-duty engine-out emissions. These techniques will take as the input, the continuous set of emissions data and approximate dispersion characteristics of the analyzer employed in measuring the continuous data. For this purpose, this research dealt with understanding and modeling the transient dynamics (dispersion function) of the analyzers and the sampling system to establish a relationship between the measured and instantaneous heavy-duty emissions. Four methods of reconstruction were presented in this study: Sequential Inversion Technique (SIT), Differential Coefficients Method (DCM), Inverse Fast Fourier Transform (IFFT) and Modified Deconvolution Technique (MDT). The application of each method in reconstructing real-time emissions data was presented. While SIT failed in practical applications, each of the other three methods was shown to offer advantage in the post-processing of the measured emissions data. DCM accounted for the small errors

Engine tailpipe emissions as measured by a sampling system and analyser do not clearly reflect the actual transient emissions from the engine at the tailpipe of the vehicle exhaust. With increasing demand for accurate emissions measurements, several research efforts are being made to compensate for the measurement distortions of the analyser system. The differential coefficients method is one such approach which reconstructs the input signal by approximating the analyser input as a linear combination of the output and the first two derivatives of the output. While the results with this approach were found to be acceptable, this paper focuses on the possibilities of improving the accuracy of the reconstruction by altering the ways in which the numerical derivatives are computed and by considering higher order derivatives in the linear combination. It was found that the use of backward differences in computing the numerical derivatives proved more effective than forward differences. Using higher order derivatives has shown improvement of about 10 per cent over using just the first two derivatives, but this margin may be important for model accuracy or for assessing pass/fail emissions criteria.