Traffic Emission Model Research Articles

Comparison of NOx emissions calculated using road traffic emission model (MEFA) with emissions derived from tunnel measurements in Czech Republic

Comparison of NOx emissions calculated using road traffic emission model (MEFA) with emissions derived from tunnel measurements in Czech Republic

Trends in black carbon and size-resolved particle number concentrations and vehicle emission factors under real-world conditions

Kerbside concentrations of NOx, black carbon (BC), total number of particles (diameter > 4 nm) and number size distribution (28–410 nm) were measured at a busy street canyon in Stockholm in 2006 and 2013. Over this period, there was an important change in the vehicle fleet due to a strong dieselisation process of light-duty vehicles and technological improvement of vehicle engines. This study assesses the impact of these changes on ambient concentrations and particle emission factors (EF). EF were calculated by using a novel approach which combines the NOx tracer method with positive matrix factorisation (PMF) applied to particle number size distributions. NOx concentrations remained rather constant between these two years, whereas a large decrease in particle concentrations was observed, being on average 60% for BC, 50% for total particle number, and 53% for particles in the range 28–100 nm. The PMF analysis yielded three factors that were identified as contributions from gasoline vehicles, diesel fleet, and urban background. This separation allowed the calculation of the average vehicle EF for each particle metric per fuel type. In general, gasoline EF were lower than diesel EF, and EF for 2013 were lower than the ones derived for 2006. The EFBC decreased 77% for both gasoline and diesel fleets, whereas the particle number EF reduction was higher for the gasoline (79%) than for the diesel (37%) fleet. Our EF are consistent with results from other on-road studies, which reinforces that the proposed methodology is suitable for EF determination and to assess the effectiveness of policies implemented to reduce vehicle exhaust emissions. However, our EF are much higher than EF simulated with traffic emission models (HBEFA and COPERT) that are based on dynamometer measurements, except for EFBC for diesel vehicles. This finding suggests that the EF from the two leading models in Europe should be revised for BC (gasoline vehicles) and particle number (all vehicles), since they are used to compile national inventories for the road transportation sector and also to assess their associated health effects. Using the calculated kerbside EF, we estimated that the traffic emissions were lower in 2013 compared to 2006 with a 61% reduction for BC (due to decreases in both gasoline and diesel emissions), and 34–45% for particle number (reduction only in gasoline emissions). Limitations of the application of these EF to other studies are also discussed.

Activity-Based Approach for the Design of Sustainable Area and Cordon Pricing Schemes

Background Vehicle-generated emissions remain a serious threat to public health, and a major contributing factor to climate change. In response to this situation, several cities around the world have successfully reduced vehicle emissions by implementing area and cordon-based pricing (ACP). Despite these successes, designing ACP schemes continues to be a challenging task given the complexities of estimating the multidimensional effects of this type of strategy. Existing transportation network design methodologies focus primarily on congestion-related goals, and employ an aggregate representation of travel demand. However, the assessment of the public health impacts of vehicle emissions requires information on disaggregate travel and activity participation behavior, as well as spatially and temporally varying pollutant concentrations; existing methodologies do not meet the planning needs of agencies interested in determining the optimal design of ACP schemes that explicitly account for both congestion and public health. Methods We propose an optimization model that can be used by transportation planning agencies to determine optimal charging boundary locations and toll levels with the objective of reducing the health impacts of vehicular traffic and improve mobility. The proposed ACP design problem is formulated as a bi-level, simulation-based optimization model. The problem's upper-level is composed of the policy makers’ goals (i.e., health and mobility objectives). The travelers’ responses to the policy maker's decisions, as well as the resulting system-wide impacts, are analyzed in the lower-level. The lower-level model system is composed of five sub-models: (a) a disaggregate activity-based travel behavior model to simulate the agents’ behavioral response to ACP specifications, (b) a traffic assignment model to estimate the distribution of traffic in the network, (c) a traffic emissions model, (d) a pollutant dispersion model, and (e) a health impact assessment model to estimate the changes in public health risks resulting from ACP schemes. The proposed methodology is computationally expensive given the type of models being combined. Therefore, we present a multi-objective, surrogate-based optimization approach that is intended to accelerate the discovery of good solutions to the design problem. Results An illustrative application of the proposed methodology is presented using Chicago as a case study. As expected, preliminary results suggest that there is a tradeoff between improving travel mobility objectives and reducing traffic's public health impacts. Conclusions The proposed methodology can be an important tool for transportation planners. Further research is necessary to refine the different parameters and strategies used in the solution algorithm.

The Air Quality Emissions and Health Benefits of Cleaner Buses: A City of York (UK) Case Study Using Micro-Scale Models and a Health Impact Toolkit

Background Air qualities on the heavily trafficked streets in the City of York are breaching the EU nitrogen dioxide annual average standard. City Buses are heavy-duty diesel vehicles that repeatedly ply their routes and make a significant contribution to the total volume of NO X emitted in York, particularly in central areas where building density, pedestrian activity and exposure to air pollution at their peak. The case for investing in cleaner Buses should be compelling if reliable vehicle emissions and air quality evidence was available, along with tools to economically evaluate health benefits. This UK case study attempts to make this environmental and economic case by bringing together calibrated and validated traffic and vehicle emission model results with the Cost-effectiveness of Air PolluTiOn Reduction model (CAPTOR) toolkit. Methods The movements of cars, vans, buses and commercial vehicles across the City York have been simulated at a detailed/ microscopic level as they negotiate traffic junctions, signals and interact with each other in weekday peak and off-peak periods. The traffic and instantaneous emission (PHEM) research models were configured to reflect the local, operational fleet i.e. mix of vehicle and fuel types in each Euro standard category. The resultant NO X emission factors are in agreement with published EU studies. The models were used to test the impact of replacing the fleet of 24 diesel Buses that service the York ‘Park & Ride’ sites with (a) new Euro VI diesel; and (b) full-Electric Vehicles (EVs) over the period 2017-2022. Results Replacing the ‘Park & Ride’ Buses with new, clean Euro VI diesels was simulated to lower the total road transport NO X emissions by 6.5% in the first year (2017). If this fleet were all EVs then the reduction was forecast to be 7%. Assuming a City of York population of 114,000, a £20,000 monetary value for QALY and lower-bound estimates of health endpoints the CAPTOR £4.65 million economic evaluation of health benefits in year one (2017) exceeded the £4.3 million cost of 24 Euro VI Buses. The EV fleet was estimated to save £5 million of health costs in the first year, only slightly less than the £7 million cost of purchasing the EVs and charging infrastructure. Conclusions The cumulative economic health benefit valuations (lower-bound) over the period 2017-2022 of the Euro VI and EV Park & Ride fleet were £25.7 million and £27.6 million respectively far exceed the capital investment in the Low Emission Vehicles.

Modelling NOX concentrations through CFD-RANS in an urban hot-spot using high resolution traffic emissions and meteorology from a mesoscale model

Air quality management requires more detailed studies about air pollution at urban and local scale over long periods of time. This work focuses on obtaining the spatial distribution of NOx concentration averaged over several days in a heavily trafficked urban area in Madrid (Spain) using a computational fluid dynamics (CFD) model. A methodology based on weighted average of CFD simulations is applied computing the time evolution of NOx dispersion as a sequence of steady-state scenarios taking into account the actual atmospheric conditions. The inputs of emissions are estimated from the traffic emission model and the meteorological information used is derived from a mesoscale model. Finally, the computed concentration map correlates well with 72 passive samplers deployed in the research area. This work reveals the potential of using urban mesoscale simulations together with detailed traffic emissions so as to provide accurate maps of pollutant concentration at microscale using CFD simulations.

Study of the traffic noise source intensity emission model and the frequency characteristics for a wet asphalt road

The traffic noise source intensity emission model is the basis of traffic noise prediction. In addition, the presence of water on the road is an important factor of road traffic noise. However, to date, none of the traffic noise source intensity emission models account for a situation in which the road surface is wet. Therefore, the main objective of this paper is to build a traffic noise source intensity emission model on a wet asphalt road based on traffic noise measurements, in which speed, sound pressure level and frequency spectrum were recorded as a single vehicle passed by. By using statistical analysis on the measurement data, a traffic noise source intensity emission model on a wet asphalt road was acquired, which conforms to correlation test, F-test and t-test. The result shows that the sound pressure level increases considerably with the presence of water on asphalt road for all types of vehicles; the mean difference in the sound pressure level between the wet and dry asphalt roads for light, middle-size, and heavy vehicles are 10.09dB(A), 5.56dB(A), and 4.26dB(A), respectively. In addition, the noise difference between a wet and dry asphalt road decreases as the speed and vehicle size grow. Furthermore, the frequency characteristics of the road traffic noise were analysed through the sound pressure level and noise energy percentage in the 1/3 octave band spectrum. The result shows that for the wet asphalt road, the sound pressure level is high at high frequency and low at low frequency, which is completely different from the response on a dry asphalt road. In addition, for a dry asphalt road, the vehicle’s noise energy percentage at low frequency decreases as the speed increases, whereas at high frequency, the percentage increases as the speed grow. However, for a wet asphalt road, the noise energy percentage changes little with speed. The findings can be applied to the accurate traffic noise prediction and noise control, especially in the rainy regions.

Model Predictive Control for Freeway Networks Based on Multi-Class Traffic Flow and Emission Models

The main aim of this paper is to use multi-class macroscopic traffic flow and emission models for Model Predictive Control (MPC) for traffic networks. In particular, we use and compare extended versions of multi-class METANET, FASTLANE, multi-class VT-macro, and multi-class VERSIT+. In addition, end-point penalties based on these multi-class traffic flow and emission models are also included in the objective function of MPC to account for the behavior of the traffic system beyond the prediction horizon. A simulation experiment is implemented to evaluate the multi-class models. The results show that the approaches based on multi-class METANET and the extended emission models (multi-class VT-macro or multi-class VERSIT+) can improve the control performance for the total time spent and the total emissions with respect to the non-control case, and they are more capable of dealing with the queue length constraints than the approaches based on FASTLANE. Including end-point penalties can further improve the control performance with a small sacrifice in the computational efficiency for the approaches based on multi-class METANET but not for the approaches based on FASTLANE.

Traffic emission modelling at road transport corridors in Delhi

The vehicular pollution has increased substantially in recent times. Indian mega-cities like Delhi which has distinctly been credited with dangerously high vehicular pollution levels. The present study has been undertaken aiming at pollution estimation for different transport corridors of Delhi city with a view to analysing the changing traffic composition trends and its impact on the levels of ambient air pollutants. From the study, it was found that Ring road (Safdarjung) had the highest concentration of CO and PM10 as 3,066 and 422 μg/m3 while Auchandi Road bore the lowest concentration of the same as 193 and 23 μg/m3. Maximum values of 363 μg/m3 of NOx at Maa Anandmayi Marg, 542 μg/m3 of PM2.5 at ISBT Flyover and 42 μg/m3 of SOX at Nizamuddin Bridge were reported in comparison to minimum values as 24 μg/m3 at Pusa Road, 53 μg/m3 at Sansad Marg and 2 μg/m3 at Auchandi Road respectively.

Traffic emission modelling at road transport corridors in Delhi

Traffic emission modelling at road transport corridors in Delhi

An original traffic additional emission model and numerical simulation on a signalized road

Based on VSP (Vehicle Specific Power) model traffic real emissions were theoretically classified into two parts: basic emission and additional emission. An original additional emission model was presented to calculate the vehicle’s emission due to the signal control effects. Car-following model was developed and used to describe the traffic behavior including cruising, accelerating, decelerating and idling at a signalized intersection. Simulations were conducted under two situations: single intersection and two adjacent intersections with their respective control policy. Results are in good agreement with the theoretical analysis. It is also proved that additional emission model may be used to design the signal control policy in our modern traffic system to solve the serious environmental problems.

Options for reducing noise from roads and railway lines

The fundamental noise generation mechanisms of road and rail vehicles are discussed with attention to noise abatement measures. Based on an evaluation of publicly available tire noise data and the European road traffic noise emission model CNOSSOS, it is shown that on the road side there is a significant noise reduction potential in the usage of low-noise tires. From a three months measurement campaign a noise model was derived to predict the maximal sound pressure level of heavy duty vehicles during a pass-by in 7.5m distance with the parameters vehicle speed and number of axles. With help of recently published information about external costs caused by heavy duty vehicles and the noise prediction tool, a model was developed to derive a money equivalent that can be used as a bonus/malus in a heavy duty vehicle fee. As a measure at the infrastructure, the installation of low-noise pavements is an effective, durable and economically attractive measure. Recent experiences with different technologies from all over the world are compiled and evaluated. On the rail side, an overview of the possible noise reduction strategies is given, followed by a discussion of the current policy and legislation in the EU and on the national level of different European countries.

Integrated Modeling Framework for Highway Traffic Pollution Estimation and Dispersion

Air quality is of great concern to the public. Airborne pollutants, such as carbon monoxide (CO), carbon dioxide (CO2) and Particulate Matter (PM), negatively impact people’s living conditions. In particular, vehicular exhaust is a major source of these pollutants. Extensive previous research has contributed to modeling traffic pollutant emission. Popular approaches include computational simulation-based methods, which have been dominant in studies related to traffic emissions. However, simulation-based methods may not reflect changes in traffic and environmental factors in a real world situation. In order to address this and other limitations, our study employs real world traffic, meteorological and geographical data. In order to fully utilize this data and apply it to traffic pollutant emission and dispersion modeling, this study proposes a framework to integrate a traffic emission model, dispersion model and multi-source public data to best estimate traffic emissions and dispersion. The framework is based on the concept that traffic sensors + meteorology sensors = emissions sensors. The results from case studies show that the concept is feasible and that air pollutants can be estimated over a large area. Sensitivity analyses on atmosphere stability and surface roughness showed not only the effectiveness of the proposed framework, but also demonstrated potential applications for inter-disciplinary research including public health and land-use planning.

The treatment of uncertainties in reactive pollution dispersion models at urban scales

The ability to predict NO2 concentrations ([NO2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street-scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties which should be taken into account when using models within decision making. This paper presents an analysis of mean [NO2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, and a Reynolds averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. The analysis focuses on the sensitivity of predicted in-street increments of [NO2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO + O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO2], particularly for regions of high NOx emissions such as close to traffic queues.

Rapid Modeling Method for Road Traffic Noise Sources: A case study in Beijing

AbstractBased on the local road traffic conditions in Beijing, China, this contribution proposes a rapid modeling method for road traffic noise sources. Since establishing the standardized experiment fields are expensive, real roads are used to determine the road traffic noise emission model in the method. Due to the similarity in the urban structures in China and Japan, this paper uses the ASJ- 2013 model as a template and replaces its model parameters with the ones output by an optimization program which minimizes the sum of absolute errors between the predicted and the measured LAeq. Real road experiments are conducted to verify the effectiveness and feasibility of the modeling method. The mean error of the model deduced by the method and the ASJ-2013 model is respectively 0.4 dB and 2.6 dB, and the mean absolute error of the two models is respectively 1.1 dB and 2.6 dB. The results of the real road experiments show that the road traffic noise sources deduced by the method are more accurate to conduct local noise prediction than those of other models.

Air quality impact of intelligent transportation system actions used in a decision support system for adaptive traffic management

The presented traffic control system (CARBOTRAF) combines real-time monitoring of traffic and air pollution with simulation models for traffic, emission and local air quality predictions to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimising the traffic flows. A chain of models combines microscopic traffic simulations, emission models and air quality simulations for a range of traffic demand levels and intelligent transport system (ITS) actions. These ITS scenarios simulate combinations of traffic signal optimisation plans and variable messaging systems. The real-time decision support system uses these simulations to select the best traffic management in terms of traffic and air quality. In this paper the modelled effects of ITS measures on air quality are analysed with a focus on BC for urban areas in two European cities, Graz and Glasgow.

Simulating the air quality impacts of traffic calming schemes in a dense urban neighborhood

In this study, the effects of isolated traffic calming measures and area-wide calming schemes on air quality in a dense neighborhood were estimated using a combination of microscopic traffic simulation, emission, and dispersion modeling. Results indicated that traffic calming measures did not have as large an effect on nitrogen dioxide (NO2) concentrations as the effect observed on nitrogen oxide (NOx) emissions. Changes in emissions resulted in highly disproportional changes in pollutant levels due to daily meteorological conditions, road geometry and orientation with respect to the wind. Average NO2 levels increased between 0.1% and 10% with respect to the base-case while changes in NOx emissions varied between 5% and 160%. Moreover, higher wind speeds decreased NO2 concentrations on both sides of the roadway. Among the traffic calming measures, speed bumps produced the highest increases in NO2 levels.

Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization

Nowadays, the increasing levels of polluting emissions and fuel consumption of the road traffic in modern cities directly affect air quality, the city economy, and especially the health of citizens. Therefore, improving the efficiency of the traffic flow is a mandatory task in order to mitigate such critical problems. In this article, a Swarm Intelligence approach is proposed for the optimal scheduling of traffic lights timing programs in metropolitan areas. By doing so, the traffic flow of vehicles can be improved with the final goal global target of reducing their fuel consumption and gas emissions (CO and N O x ). In this work we optimize the traffic lights timing programs and analyze their effect in pollution by following the standard HBEFA as the traffic emission model. Specifically, we focus on two large and heterogeneous urban scenarios located in the cities of Malaga and Seville (in Spain). When compared to the traffic lights timing programs designed by experts close to real ones, the proposed strategy obtains significant reductions in terms of the emission rates (23.3 % CO and 29.3 % N O x ) and the total fuel consumption.

Simulating the environmental effects of isolated and area-wide traffic calming schemes using traffic simulation and microscopic emission modeling

This study focuses on the development of a microscopic traffic simulation and emission modeling system which aims at quantifying the effects of different types of traffic calming measures on vehicle emissions both at a link-level and at a network-level. It also investigates the effects of isolated traffic-calming measures at a corridor level and area-wide calming schemes, using a scenario analysis. Our study is set in Montreal, Canada where a traffic simulation model for a dense urban neighborhood is extended with capabilities for microscopic emission estimation. The results indicate that on average, isolated calming measures increase carbon dioxide (CO2), carbon monoxide (CO), and nitrogen oxides (NOx) emissions by 1.5, 0.3, and 1.5 %, respectively across the entire network. Area-wide schemes result in a percentage increase of 3.8 % for CO2, 1.2 % for CO, and 2.2 % for NOx across the entire network. Along specific corridors where traffic calming measures were simulated, increases in emissions of up to 83 % were observed. We also account for the effect of different measures on traffic volumes and observe moderate decreases in areas that have undergone traffic calming. In spite of traffic flow reductions, total emissions do increase.

Modelling of air pollution in the area of Algiers City, Algeria

Algiers is the political and economic capital of Algeria. The city drains a large part of the activities of the country with intense road traffic. This road traffic is the main source of air pollution, followed by industry and waste combustion. An application of the new road traffic emission model, called EMISENS, is performed to build road traffic emission inventories for the Algiers region. These emission inventories are then used as input to the MM5/CHIMERE system to estimate air pollution. The objective of this study is to propose a methodology to improve the quality of emission inventories, and to validate results. First, by studying the correlation between observed measurements and data concerning cadastre of primary pollutants emitted mainly by road traffic, then by comparing the pollutants concentrations from observed measures with the simulated concentrations with the model of chemistry-transport CHIMERE.

Integrated Urban Traffic Control for the Reduction of Travel Delays and Emissions

Refining transportation mobility and improving the living environment are two important issues that need to be addressed in urban traffic. To reduce traffic delays and traffic emissions for urban traffic networks, this paper first proposes an integrated macroscopic traffic model that integrates a macroscopic urban traffic flow model with a microscopic traffic emission model for individual vehicles. This integrated model is able to predict the traffic flow states and the emissions released by every vehicle at different operational conditions, i.e., the speed and the acceleration. Then, model predictive control (MPC) is applied to control urban traffic networks based on this integrated traffic model, aiming at reducing both travel delays and traffic emissions of different gases. Finally, simulations are performed to assess this multiobjective control approach. The obtained simulation results illustrate the control effects of the model predictive controller.