Estimation of vehicular emissions by capturing traffic variations (original) (raw)
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Model for Traffic Emissions Estimation
A~tmct--A model is developed for the spatial and temporal evaluation of traffic emissions in metropolitan areas based on sparse measurements. All traffÉc data available are fully employed and the pollutant emissions are determined with the highest precision possible. The main roads are regarded as line sources of constant traffic parameters in the time interval considered. The method is flexible and allows for the estimation of distributed small traffic sources (non-line/area sources). The emissions from the latter are assumed to be proportional to the local population density as well as to the traffic density leading to local main arteries. The contribution of moving vehicles to air pollution in the Greater Athens Area for the period 1986-1988 is analysed using the proposed model. Emissions and other related parameters are evaluated. Emissions from area sources were found to have a noticeable share of the overall air pollution.
Transportation Research Part D: Transport and Environment, 2018
The development of accurate emission inventories at an urban scale is of utmost importance for cities in light of climate change commitments and the need to identify the emission reduction potential of various strategies. Emission inventories for on-road transportation are sensitive to the network models used to generate traffic activity data. For large networks (cities or regions), average-speed models have been relied upon extensively in research and practice, primarily due to their computational attractiveness. Nevertheless, these models are myopic to traffic states and driving cycles and therefore lack in accuracy. The aim of this study is to improve the quality of regional on-road emission inventories without resorting to computationally-intensive traffic microsimulation of an entire region. For this purpose, macroscopic, mesoscopic, and microscopic emission models are applied and compared, using average speed, average speed and its standard deviation, and instantaneous speeds. We also propose a hybrid approach called the CLustEr-based Validated Emission Re-calculation (CLEVER), which bridges between the microscopic and mesoscopic approaches. CLEVER defines unsupervised traffic conditions using a combination of mesoscopic traffic characteristics for selected road segments, and identifies a representative emission factor (EF) for each condition based on the microscopic driving cycle of the sample. Regional emissions can then be estimated by classifying segments in the regional network into these conditions, and applying corresponding EFs. The results of the CLEVER method are compared with the results of microsimulation and of mesoscopic approaches revealing a robust methodology that improves the emission inventory while reducing computational burden.
Using observed traffic volumes to improve fine-grained regional emissions estimation
Transportation Research Part D: Transport and Environment, 1999
When translating travel demand model output to photochemical model input, period-based network assignment volumes must be converted to gridded-hourly vehicle emissions. A post-processor, such as the California Direct Travel Impact Model (DTIM2), is frequently used to disaggregate the period-based travel demand assignments to the ®ne grained spatial and temporal resolution required by the photochemical models. A recent theoretical enhancement proposed re®ning the temporal and spatial resolutions of travel demand model predictions using observed count data. This method provides a technique for disaggregating the period-based travel demand model assignments (e.g., AM peak, PM peak) into the hourly summaries required by most photochemical model (Lin and Niemeier, 1997). In this study we present a methodological framework for applying the new theory and discuss the results of a large-scale application empirical comparison between the standard and proposed methods for estimating regional mobile emissions in Sacramento, California. The standard method produced slightly higher estimates of daily emissions (about 1%) when compared to the emissions estimated using observed count data. However, the two approaches produced hourly emissions estimates that diered by as much as 15% in some hours.
Emissions Modeling for Road Transportation in Urban Areas: State-of-Art Review
Estimating and measuring emissions by road traffic is a key-issue for air pollution management in transportation sector. It introduces a good method for the environmental evaluation of the transportation system scenarios. Emission models are important issue in this respect. There are several emission models available worldwide. These models distinguish between static and dynamic models. It depends on the amount of data available for the transport fleet and its specifications. Monitoring emissions in a study area can, also, be a good method for calibration of such emission models. This paper presents an analysis of models used in estimating emissions from road transportation systems in urban areas. It compares between different emissions models that used in air pollution management of transportation scenarios. The study discusses, also the potential and limitation of each type. The difficulties of applying these models in developing countries are also discussed. Then, the study determines the required steps towards realistic transportation emission modeling in developing countries.
Traffic pollution modelling and emission data
Environmental Modelling & Software, 2006
Evaluation of traffic pollution in streets requires basically information on three main factors: traffic emissions, the meteorological conditions and the street surroundings. Dispersion models exist with various degree of sophistication, which are able to properly describe the dispersion conditions, and thus to predict the relationships between emissions and the concentration levels in the street. However, for real-world applications, the model calculations must be based on ''true'' emission data, and their estimation is not trivial. Significant uncertainty is still connected with emission data. Examining the relationships between model predictions and measurements with respect to the meteorological conditions and inter-relationships between different pollution components allows quantitative evaluation of the traffic emissions. This methodology is illustrated using the Danish Operational Street Pollution Model e OSPM, and time series of traffic related pollutants. Street level concentrations of NO x and CO are calculated using OSPM as the dispersion model and emission data estimated by the widely used COPERT methodology. Comparison with measurements shows significant underestimation of the pollution concentrations and especially the CO/NO x ratio. An alternative set of traffic emission factors, using a more simplified vehicle classification methodology, provides better agreement with the measured concentrations. Evaluation of these results provides some guidance on the necessary modifications of the ''real-world'' traffic emission factors.
Influence of Traffic Emissions Estimation Variability on Urban Air Quality Modelling
2002
The main objective of this work is to analyse how uncertainties in emission data of nitrogen oxides (NO x ) and volatile organic compounds (VOC), originated from road traffic, influence the model prediction of ozone (O 3 ) concentration fields. Different methods to estimate emissions were applied and results were compared in order to obtain their variability. Based on these data, different emission scenarios were compiled for each pollutant considering the minimum and the maximum values of the estimated emission range. These scenarios were used as input to the MAR-IV mesoscale modelling system. Simulations have been performed for a summer day in the Northern Region of Portugal. The different approaches to estimate NO x and VOC traffic emissions show a significant variability of absolute values and of their spatial distribution. Comparison of modelling results obtained from the two scenarios presents a dissimilarity of 37% for ozone concentration fields as a response of the system to a variation in the input emission data of 63% for NO x and 59% for VOC. Far beyond all difficulties and approximations, the developed methodology to build up an emission data base shows to be consistent and an useful tool in order to turn applicable an air quality model. Nevertheless, the sensitivity of the model to input data should be considered when it is used as a decision support tool.
A new modelling approach for road traffic emissions: VERSIT+
Transportation Research Part D: Transport and Environment, 2007
The objective of VERSIT+ LD is to predict traffic stream emissions for light-duty vehicles in any particular traffic situation. With respect to hot running emissions, VERSIT+ LD consists of a set of statistical models for detailed vehicle categories that have been constructed using multiple linear regression analysis. The aim is to find empirical relationships between mean emission factors, including confidence intervals, and a limited number of speed-time profile and vehicle related variables. VERSIT+ is a versatile model that has already been used in different projects at different geographical levels. Compared to COPERT IV, the VERSIT+ average speed algorithms provide increased accuracy with respect to the prediction of emissions in specific traffic situations.
2017
Traffic management definition and assessment strategies rely on results from successive stages of modelling: from traffic to air pollution concentrations. The objective of this study was to improve this modelling process. Combining microscopic traffic modelling and 3 pollutant emission modellings was performed: two using aggregated traffic estimates (HBEFA, Copert) and the other using vehicle trajectory (Phem). The studied area is part of the Lyon urban area (6,2 km², 2091 road sections). Traffic and emissions were simulated for 16 scenario resulting from modifications of supply or demand (traffic calibration on the afternoon rush hour). Copert and HBEFA estimations show many similarities and differences with Phem. Ranking of scenarios on the basis of their variation to the reference was performed and analysed. Copert and HBEFA provide the same ranking. To focus on the analysis of two scenarios, difference of NOx emissions per link were maped (only the higher variations). The releva...
This paper discusses the development and application of a new high resolution traffic emissions and fuel consumption model. The model is needed to adequately address increasingly complex policy and research questions. Over recent years, a large body of test data has become available in Australia, which amounts to hundreds of hours of second-by-second emissions and driving behaviour data for relevant vehicle classes. The data were measured using real-world driving cycles that were developed from Australian on-road driving data. This large amount of data inspired the development of a new hybrid model with a number of innovative aspects. The model uses (new) model variables that reflect vehicle and driving aspects known to influence vehicle emissions (e.g. speed fluctuation, delta power, power oscillation) and employs a statistical approach to find the best empirical relationships. The algorithms are designed to combine an engineering and a statistical approach. This paper will discuss that the information generated by the model can be used in various ways, for instance to develop an emission inventory, to analyse the impacts of particular traffic management measures (e.g. dynamic speed limits, traffic signal coordination, metering signals). In this paper we will demonstrate this by examining the effects of congestion on emissions and fuel consumption.