Instantaneous Emission Model Research Articles

Advances in terminal management: simulation of vehicle traffic in container terminals

AbstractControlling and managing traffic flows on internal roads in container terminals are crucial in achieving expected productivity levels and reducing negative externalities caused by congestion inside and outside the terminal areas. This paper proposes a simulation approach which terminal operators can use as a decision-support tool to assess the effects of their management strategies and improve terminal performance, resilience, and sustainability. A microscopic traffic simulation approach models key operations of a typical container terminal affecting road traffic flows. In particular, to estimate quantitative indicators, an import truck process is reproduced, considering the overlapping of the external truck and internal trailer flows. To measure environmental impacts, the model is extended with an instantaneous emissions model linked directly to the step-by-step traffic data. The proposed method is tested on a sector of the PSA Genova Pra’, the main Italian container gateway terminal. Performance indicators related to the terminal’s efficiency and sustainability are estimated, to compare alternative scenarios considering possible operational configurations and disturbance events, such as the closure of a part of the yard. By focusing on the interactions between vehicle flows and terminal equipment operations, this approach offers a new perspective on terminal operations, oriented both towards container terminal operators and stakeholders, such as road hauliers.

Effect of speed humps on instantaneous traffic emissions in a microscopic model with limited deceleration capacity

As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO2, NO X and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined NaSch model, the present model has lower instantaneous emissions such as CO2, NO X and PM and higher volatile organic components (VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO2, NO X and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.

Data mining techniques for estimating instantaneous NOX emissions from Euro 6 diesel passenger cars

Nitrogen oxides (NOX) emissions that are caused by road traffic diesel engines affects public health. The existing instantaneous emissions models are often imprecise due to the lack of knowledge of highly non-linear processes behind real-world emissions and they do not include meteorological and driving volatility variables. This paper applied data mining techniques based on the Cross Industry Standard Process for Data Mining (CRISP-DM) method to a dataset of four diesel Euro 6 passenger cars tested in real-world driving conditions to: a) model stabilised hot NOX emissions based on kinematic (speed), internal engine (engine coolant temperature, engine load, engine speed, intake air temperature, manifold absolute pressure and mass air flow), meteorological (humidity) and driving volatility (acceleration and vehicular jerk); b) compare the performance of different machine learning (ML) techniques in predicting NOX emission rates, namely: Artificial Neural Networks (ANN), Random Forest (RF), and Gradient-Boosted Trees (GBT). The model that utilizes a set of detailed variables, particularly engine coolant temperature, engine load, engine speed, intake air temperature, humidity, acceleration and vehicular jerk, and using ANN technique was better able to deal with variability in emission data than models based on a single set of these variables. It was also found that models produced high Root Mean Square Error due to their inability in predicting high peaks in measured emission data. The presented models rely on fast inference times and can therefore be deployed for engine control units to inform drivers about their NOX emissions during driving.

Emission impacts of left-turn lane on light-heavy-duty mixed traffic in signalized intersections: Optimization and empirical analysis

Reducing emissions from the transport sector is one of the crucial countermeasures for climate action. This study focuses on the optimization and emission analysis regarding the impacts of left-turn lanes on the emissions of mixed traffic flow (CO, HC, and NOx) with both heavy-duty vehicles (HDV) and light-duty vehicles (LDV) at urban intersections, combining high-resolution field emission data and simulation tools. Based on high-precision field emission data collected by Portable OBEAS-3000, this study first develops instantaneous emission models for HDV and LDV under various operating conditions. Then, a tailored model is formulated to determine the optimal left-lane length for mixed traffic. Afterward, we empirically validate the model and analyze the effect of the left-turn lane (before and after optimization) on the emissions at the intersections using the established emission models and VISSIM simulations. The proposed method can reduce CO, HC, and NOx emissions crossing intersections by around 30% compared to the original scenario. The proposed method significantly reduces average traffic delays after optimization by 16.67% (North), 21.09% (South), 14.61% (West), and 2.68% (East) in different entrance directions. The maximum queue lengths decrease by 79.42%, 39.09%, and 37.02% in different directions. Even though HDVs account for only a minor traffic volume, they contribute the most to CO, HC, and NOx emissions at the intersection. The optimality of the proposed method is validated through an enumeration process. Overall, the method provides useful guidance and design methods for traffic designers to alleviate traffic congestion and emissions at urban intersections by strengthening left-turn lanes and improving traffic efficiency.

How accelerating the electrification of the van sector in Great Britain can deliver faster CO2 and NOx reductions

As a major emission contributor with significant growth potential, the light goods vehicles (vans) play an important part in achieving net-zero. In 2020 the UK government committed to phasing out sales of new internal combustion engine (ICE) vans by 2030, but the impact of the policy and how far are we to decarbonize the entire van fleet by 2050 is unclear. This paper investigates the CO2 and NOx emission trends in the van sector in Great Britain under the 2030 ICE phase-out. ECCo model11http://www.element-energy.co.uk/sectors/low-carbon-transport/project-case-studies/ is used to forecast the future van population by powertrain. The annual van mileage is estimated based on the van activity survey22https://www.gov.uk/government/statistics/van-statistics-2019-to-2020. The instantaneous emission model PHEM, NAEI emissions inventory and remote sensing measurements are used to parameterize real-world driving emission factors of CO2 and NOx. Scenarios have been set out to assess the impact of enablers and barriers affecting the pace of emission reductions. Results suggest vans are on track to reach the tailpipe net-zero target by 2050 under all scenarios, and the speed of NOx reduction is even faster. A rapid transition to battery electric vans in the early to mid-2020s will significantly lower CO2, with associated estimated monetary benefits of £1.3 billion.

High Resolution Modelling of Traffic Emissions Using the Large Eddy Simulation Code Fluidity

The large eddy simulation (LES) code Fluidity was used to simulate the dispersion of NOx traffic emissions along a road in London. The traffic emissions were represented by moving volume sources, one for each vehicle, with time-varying emission rates. Traffic modelling software was used to generate the vehicle movement, while an instantaneous emissions model was used to calculate the NOx emissions at 1 s intervals. The traffic emissions were also modelled as a constant volume source along the length of the road for comparison. A validation of Fluidity against wind tunnel measurements is presented before a qualitative comparison of the LES concentrations with measured roadside concentrations. Fluidity showed an acceptable comparison with the wind tunnel data for velocities and turbulence intensities. The in-canyon tracer concentrations were found to be significantly different between the wind tunnel and Fluidity. This difference was explained by the very high sensitivity of the in-canyon tracer concentrations to the precise release location. Despite this, the comparison showed that Fluidity was able to provide a realistic representation of roadside concentration variations at high temporal resolution, which is not achieved when traffic emissions are modelled as a constant volume source or by Gaussian plume models.

Estimation of traffic emissions in a polycentric urban city based on a macroscopic approach

This paper presents a macroscopic dynamic modeling framework for road traffic emissions in an urban city that has numerous central business districts (CBDs). The framework combines a two-dimensional (2D) continuum dynamic traffic assignment (DTA) model that captures macroscopic features of traffic flow and a microscopic instantaneous emission model associated with the instantaneous speed and acceleration of traffic flow. A heterogeneous traveling population continuously distributed in space is classified according to the trip destinations chosen by travelers. Each copy of flow tends to select an optimal route with the minimum cost to destination by means of instantaneous traffic information. For each copy of flow, an unstructured finite volume method as well as a fast sweeping method is designed to solve the proposed model. In order to validate the feasibility of the model and algorithm, a case study is conducted as an example to study the macroscopic characteristics of traffic flow and predict traffic-related pollutants like NOx, VOC, CO2 and PM in urban areas.

The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques

Road transport contributes to almost a quarter of carbon dioxide emissions in the EU. To analyze the exhaust emissions generated by vehicle flows, it is necessary to use specialized emission models, because it is infeasible to equip all vehicles on the road in the tested road sections with the Portable Emission Measurement System (PEMS). However, the currently used emission models may be inadequate to the investigated vehicle structure or may not be accurate due to the used macroscale. This state of affairs is especially related to full hybrid vehicles, since there are none of the microscale emission models that give estimated emissions values exclusively for this kind of drive system. Several automakers over the past decade have invested in hybrid vehicles with great opportunities to reduce costs through better design, learning, and economies of scale. In this work, the authors propose a methodology for creating a CO2 emission model, which takes relatively little computational time, and the models created give viable results for full hybrid vehicles. The creation of an emission model is based on the review of the accuracy results of methods, such as linear, robust regression, fine, medium, coarse tree, linear, cubic support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and neural network (NNET). Particularly in the work, the best fit for the road input data for the CO2 emission model creation was the GPR method. PEMS data was used, as well as model training data and model validation. The model resulting from this methodology can be used for the analysis of emissions from simulation tests, or they can be used for input parameters for speed, acceleration, and road gradient.

Statistical Modeling of Exhaust Emissions from Gasoline Passenger Cars

In this study, an instantaneous vehicle emission model was developed and validated. A group of emission regression functions with vehicle speed and VSP as variables was established using the multiple linear regression method and embedded in the instantaneous emission model to predict vehicle emissions. The inputs of the instantaneous vehicle emission model consist of the driving cycle, vehicle parameters and accessories use, all of which are used to calculate the instantaneous vehicle specific power (VSP). The simulated results of the emission model are second-by-second emission rates, emission factors and fuel consumption over the target driving cycle. The predicted emissions as well as fuel consumption of four passenger cars were very close to the tested emission data, and the prediction errors of emission factors and fuel consumptions were acceptable.

Real-world CO2 and NOX emissions from refrigerated vans

Refrigerated vans used for home deliveries are attracting attention as online grocery shopping in the UK is expanding rapidly and contributes to the increasing greenhouse gas (CO2) and nitrogen oxides (NOX) emissions. These vans are typically 3.5-tonne gross weight vehicles equipped with temperature-controlled units called Transport Refrigeration Units (TRUs), which are usually powered off the vehicles' engine. It is obvious that vehicles with added weight of TRUs consume more fuel and emit more NOX, let alone the vehicles' diesel engines are also powering the refrigeration units, which further elevates the emissions.This research uses an instantaneous vehicle emission model PHEM (version 13.0.3.21) to simulate the real-world emissions from refrigerated vans. A validation of PHEM is included using data from laboratory (chassis dynamometer) tests over a realistic driving profile (the London Drive Cycle), to assess its ability to quantify the impact of changing vehicle weights and carrying loads. The impact of the TRU weight, greater frontal area increasing aerodynamic drag and refrigeration load on van emissions is then estimated by PHEM. The influence of ambient temperature, cargo weight and driving condition on CO2 and NOX emission from refrigerated van are also assessed.Overall CO2 emissions of vans with TRUs are found to be 15% higher than standard vehicles, with NOX emissions estimated to be elevated by 18%. This confirms the need to take into account the impact of additional engine load when predicting van emissions in this and other sectors such as ambulances which are relatively heavy, high powered vehicles. Moreover, findings of the impact of TRUs on fuel consumptions can be used to optimize fuel-saving strategies for refrigerated vans and test cases for alternative low- or zero-emission technologies, to support progress to a sustainable net-zero society.

Capturing the Variability in Instantaneous Vehicle Emissions Based on Field Test Data

Emission models are important tools for traffic emission and air quality estimates. Existing instantaneous emission models employ the steady-state “engine emissions map” to estimate emissions for individual vehicles. However, vehicle emissions vary significantly, even under the same driving conditions. Variability in the emissions at a specific driving condition depends on various influencing factors. It is important to gain insight into the effects of these factors, to enable detailed modeling of individual vehicle emissions. This study employs a portable emissions measurement system (PEMS), to collect vehicle emissions including the corresponding parameters of engine condition, vehicle activity, catalyst temperature, geography, and meteorology, to analyze the variability in emission rates as a function of those factors, across different vehicle specific power (VSP) categories. We observe that carbon dioxide, carbon monoxide, nitrogen oxides, and particle number emissions are strongly correlated with engine parameters (engine speed, torque, load, and air-fuel ratio) and vehicle activity parameters (vehicle speed and acceleration). In the same VSP bin, emissions per second on highways and ramps are higher than those on arterial roads, and the emissions when the vehicle is traveling downhill tend to be higher than the emissions during uphill traveling, because of higher observed speeds and accelerations. Morning emissions are higher than afternoon emissions, due to lower temperatures.

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

Modelling of instantaneous emissions from diesel vehicles with a special focus on NOx: Insights from machine learning techniques

Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, we apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to: (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. The application of dynamic time warping and clustering analysis by NOx emissions resulted in 17 clusters capturing 88% of trips in the dataset. We show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, we evaluate three instantaneous emissions models: a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOx predictions, on par with the MLP: relative errors in prediction of emission factors are below 20% for both models, average fractional biases are −0.01 (s.d. 0.02) and −0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that we present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOx emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.

The Effect of Speed Humps on Instantaneous Traffic Emissions

Bad air quality due to free pollutants such as particulate matter (PM), carbon dioxide (CO 2 ), nitrogen oxides (NO x ) and volatile organic components (VOC) increases the risk of long- term health diseases. The impact of traffic-calming measures on air quality has been studied using specialized equipment at control sites or mounted on cars to monitor pollutants levels. However, this approach suffers from a large number of variables on the experiments such as vehicles types, number of monitored vehicles, driver’s behavior, traffic density, time of the day, elapsed monitoring time, road conditions and weather. In this work, we use a cellular automata and an instantaneous traffic emissions model to capture the effect of speed humps on traffic flow and on the generation of CO 2 , NO x , VOC and PM pollutants. This approach allows us to study and characterize the effect of many speed humps on a single lane. We found that speed humps significantly promote the generation of pollutants when the number of vehicles on a lane is low. Our results may provide insight into urban planning strategies to reduce the generation of traffic emissions and lower the risk of long-term health diseases.

Evaluating the Effects of One-Way Traffic Management on Different Vehicle Exhaust Emissions Using an Integrated Approach

One-way traffic management is a recognized traffic organization to improve traffic efficiency and safety, but its effects on different traffic emissions remains unclear. This paper aims to investigate the impacts of one-way traffic management on three typical vehicle exhaust emissions including Carbonic Oxide (CO), Hydrocarbon Compounds (HC), and Nitrogen Oxides (NOx) in a traffic system using an integrated approach. Field experiment was conducted to collect the vehicular emission data under different traffic conditions using the onboard portable emission measurement system. An instantaneous emission model (i.e., Vehicle Specific Power) is calibrated using the collected field emission data and is incorporated into the microscopic traffic simulation tool VISSIM for quantifying the emissions before and after one-way traffic management through simulation. Two scenarios based on real networks and traffic demands of peak hours in part areas of Shanghai are developed for simulation and evaluation. The results show that in the intersections, the emission rates of COHC, NOx after one-way traffic management is significantly reduced by 20.46%, 21.29% and 21.06%, respectively. In the road sections, the emission rates of CO, HC, NOx in the road sections decrease by 23.38% and 26.29%. The overall CO, HC, NOx emissions in the studied network reduce by 21.34%, 22.29% and 23.77% separately due to one-way traffic management. The results provide insights into the derivative effects of one-way traffic management on traffic emissions in the intersections, road sections and network levels, and thus support scientific traffic management for promoting the sustainability of transport system.

Enhancing average speed emission models to account for congestion impacts in traffic network link-based simulations

This study aims to demonstrate a new approach for estimating fuel consumption from vehicles circulating on urban roads with different levels of congestion. A microscopic dynamic traffic model (AIMSUN) was coupled with an instantaneous emissions model (CRUISE) and their output was used to refine the average speed model (COPERT) functions. The integrated methodology was applied in an urban corridor in Turin using two validated vehicle models of Euro 5 passenger cars. Three traffic conditions were examined, corresponding to free-flow, normal and congested traffic. New average speed – fuel consumption functions were developed for free and congested traffic, which successfully estimated the differences in fuel consumption when moving from normal to other conditions. The results showed that under congested conditions the fuel consumption can increase by more than 18% in some cases, indicating the significance of incorporating similar congestion algorithms in macro emission models. Moreover, if a more comprehensive method does not exist, the standard COPERT functions could be used to estimate fuel consumption under congested traffic conditions.

A large eddy simulation of the dispersion of traffic emissions by moving vehicles at an intersection

Traffic induced flow within urban areas can have a significant effect on pollution dispersion, particularly for traffic emissions. Traffic movement results in increased turbulence within the street and the dispersion of pollutants by vehicles as they move through the street. In order to accurately model urban air quality and perform meaningful exposure analysis at the microscale, these effects cannot be ignored. In this paper we introduce a method to simulate traffic induced dispersion at high resolution. The computational fluid dynamics software, Fluidity, is used to model the moving vehicles through a domain consisting of an idealised intersection. A multi-fluid method is used where vehicles are represented as a second fluid which displaces the air as it moves through the domain. The vehicle model is coupled with an instantaneous emissions model which calculates the emission rate of each vehicle at each time step. A comparison is made with a second Fluidity model which simulates the traffic emissions as a line source and does not include moving vehicles. The method is used to demonstrate how moving vehicles can have a significant effect on street level concentration fields and how large vehicles such as buses can also cause acute high concentration events at the roadside which can contribute significantly to overall exposure.

A study of the environmental impacts of intelligent automated vehicle control at intersections via V2V and V2I communications

This article presents a novel intersection traffic management system for automated vehicles and quantifies its impact on fuel consumption and greenhouse gas emissions of CO2 relative to traditional traffic signal and roundabout intersection control. The developed intelligent traffic management (ITM) techniques, which are based on a spatiotemporal reservation scheme, ensure that vehicles proceed through the intersection without colliding with other vehicles while at the same time reducing the intersection delay and environmental impacts. Specifically, the spatiotemporal reservation scheme provides each vehicle a collision-free path that is decomposed into a speed profile along with navigational instructions. The integration of the developed microscopic traffic simulator with instantaneous emission model, provides improved assessments of the environmental impact of traffic control strategies at intersections. The simulator architecture integrates several ITM algorithms, vehicle sensors, V2V/V2I communications, and emission and fuel consumption models. Each vehicle is modeled by an agent and each agent provides information depending on the specific vehicle sensors. The ITM system is supported by V2V and V2I communications, allowing the exchange of information among vehicles and infrastructure. The data include the estimated vehicle position and speed. Compared with traditional traffic management techniques, the simulation results prove that the proposed ITM system reduces CO2 emissions significantly. The research also shows that these reductions are more significant when the traffic flow increases.

Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km2. The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.

Application of Instantaneous Emission Modeling for Rural Vehicle Emission Estimation

Rural vehicles (RVs) contribute a large proportion to total air pollutant emissions in China. In order to develop and evaluate the RV emission control legislation and policy, accurate emission factors and emission inventories for RVs need to be investigated. In this paper, based on the tri-wheel rural vehicle (3-W RV) emission data measured using portable emission measurement system (PEMS) on real roads, an instantaneous emission model was developed and used to estimate the hot stabilized 3-W RV emissions and fuel consumption under real driving conditions. The modeling approach of the instantaneous emission model was presented, and the instantaneous emission model was validated using the experimental results. The predicted emissions and fuel consumption of Foton Lovol 3-W RV met well with the measured results over the selected driving trip, and the prediction errors were in the range of 3-9%.