Traffic Emission Model Research Articles

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

Analysis of Low-Carbon, Energy-Saving, and Emission Reduction Models Based on Rail Transit

It is difficult to define scientific applicability conditions when dealing with different evaluation objects and scope. This article is based on multi-source big data such as the Automatic Fare Collection System (AFC) of urban rail transit, comprehensively considering the multidimensional impact of urban rail transit on the urban transportation system, and conducting a quantitative analysis of the energy-saving and emission reduction effects of urban rail transit. This study adopts a traffic emission model based on specific driving forces and a traffic demand prediction model, coupling the model and data to establish an urban rail transit energy conservation and emission reduction evaluation model suitable for different urban rail transit setting scenarios. Finally, this study selected six districts in Beijing as model application cases and used a combination of RP (display preference) survey and SP (state preference) survey to complete model parameter calibration for application cases.

Speed limits in traffic emission models using multi-objective optimization

AbstractClimate change compels a reduction of greenhouse gas emissions, yet vehicular traffic still contributes significantly to the emission of air pollutants. Hence, in this paper we focus on the optimization of traffic flow while simultaneously minimizing air pollution using speed limits as controllable parameters. We introduce a framework of traffic emission models to simulate the traffic dynamic as well as the production and spread of air pollutants. We formulate a multi-objective optimization problem for the optimization of multiple aspects of vehicular traffic. The results show that multi-objective optimization can be a valuable tool in traffic emission modeling as it allows to find optimal compromises between ecological and economic objectives.

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.

Integrating the Bi-dimensional-GSOM traffic flow interfaced models with a macroscopic emission model for very large-scale traffic networks

In this study, a coupled macroscopic traffic models and pollutant emission model for a very large-scale traffic networks is proposed. In order to overcome the missing data for large networks a continuum traffic modeling is developed aiming at simulating traffic of very large and dense networks. The present paper aims to merge both macroscopic traffic models (i. e. the Bi-dimensional model, BIDIM) with the generic second order model (GSOM) using real data. These two models will be associated with an emission model. The method consists of proposing an interface node model to ensure the exchange between the BIDIM and the GSOM traffic model. Then a vehicle emission model COPERT was adopted to provide the emission factors based on average speeds, in order to calculate macroscopic emissions for large networks. The dynamic of the developed simulation model reproduces the propagation of the congestion between both models and the evolution of emission pollutant in the networks. Applied to real networks, Marne La Vallée site and the A4 highway in France, the output results are very promising.

Determination of road surface correction factors for CNOSSOS-EU in Ireland

Transport Infrastructure Ireland (TII) is a state agency in the Republic of Ireland. The primary function of TII is to provide an integrated approach to the future development and operation of the national roads network and light rail infrastructure throughout Ireland. TII are designated as the Noise Mapping Body (NMB) responsible for the development of strategic noise maps for all national roads carrying in excess of 3 million vehicles a year. Directive 2002/49/EC (commonly referred to as the Environment Noise Directive, 'the END') relates to the assessment and management of environmental noise. It is the main instrument of the European Union (EU) to quantify noise pollution levels, and trigger action within both Member States and at EU level. The END has the aim of establishing a common approach to the management of noise within the EU. Commission Directive (EU) 2015/996 established the common noise assessment methodology for Europe (CNOSSOS-EU). The CNOSSOS-EU road traffic noise emission model calculates rolling noise for different vehicle categories as a function of speed and frequency. The emission is initially calculated under reference conditions, then corrected for differing situations. For road surfaces which do not align with the reference surface, road surface correction factors should ideally be developed, otherwise a number of default values are provided within CNOSSOS-EU which are taken from the Dutch national noise assessment method, Rmg2012, and therefore contain only surfaces that are common in the Netherlands. For the implementation of CNOSSOS-EU in Ireland, a project was undertaken to develop road surface corrections for at least the three most common pavement types on the TII network with no direct match to the CNOSSOS-EU database. This paper will describe the measurements taken on Irish roads, the resulting road surface corrections, and our conclusions and recommendations for using the road surface corrections.

A new vehicle specific power method based on internally observable variables: Application to CO2 emission assessment for a hybrid electric vehicle

As an important vehicle activity recognition method, vehicle specific power (VSP) has been widely used for on-road traffic emission modelling since its introduction in 1999. The conventional VSP (VSP_veh) is calculated from externally observable variables (EOVs) on the vehicle level and represents the power that a running vehicle needs to overcome. However, for hybrid electric vehicles (HEVs) with two power sources, vehicle activity is not always directly related to engine emissions. This study introduces the engine level VSP (VSP_eng), which estimates engine power from internally observable variables (IOVs) obtained from the vehicle’s on-board electronic control unit (ECU). An engine bench test is first implemented to validate the estimation algorithm for VSP_eng. A real-world driving emission (RDE) test is then conducted with a HEV in Ningbo city of China to evaluate the performance of VSP_veh and VSP_eng in emission estimation. The results show a strong correlation between emission and VSP_eng (R2 = 0.9783), while a much weaker correlation was found between emission and VSP_veh (R2 = 0.4216). Further analysis indicates that this strong correlation between emission and VSP_eng applies to all driving conditions (urban, rural and highway). The differences between VSP_veh and VSP_eng are then highlighted by a combined correlation analysis where the four work modes of HEV can be graphically identified. Lastly, this study discusses the feasibility and potential benefits of the intelligent and remote vehicle emissions monitoring through the upcoming vehicle to everything (V2X) network.

Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China

Accurate assessments of exposure to urban air pollution with higher traffic emissions and its health risks still face several challenges, such as intensive computation of air pollution modeling and the limited availability of personal activity data. The macroscopic health effects can be transmitted to the whole population for personal prevention via air quality health index (AQHI), but the possibility risk index of the specific allergic diseases is still lacking. This interdisciplinary study aims at evaluating the forecasted results of high-resolution air quality with updated traffic emissions and accessing the potential impacts of outdoor pollution on morbidity of rhinitis for urban residents. A high-resolution modelling system (1 km × 1 km) containing the online traffic emission model (VEIN), meteorological and air quality model (WRF-CHIMERE) and the health impact module was developed. A new health index of Potential Morbidity Risk Index (PMRI) was further established using higher resolution health risk coefficients of major air pollutants on allergic rhinitis, and different methods (with/without considering population distributions) targeting different user groups (residents, hospitals and health administrations) were calculated and analyzed. Operational forecasted results of hourly PMRI can be further combined with online map services to serve as an effective tool for patients with allergic rhinitis to arrange their daily activities so as to avoid acute exacerbation. The forecasted PMRIs accessible to the public will also be beneficial for the public health administrations in planning the medical resource and improving the outpatient efficiency.

Implementing the CNOSSOS-EU correction near traffic light junctions and roundabouts

Under the Environmental Noise Directive (END), the Round 4 strategic noise maps are to be calculated using the methodology set out in Directive 2015/996 (as amended) (CNOSSOS-EU). As part of the road traffic source emission model, a correction for the effect on noise levels due to the acceleration and deceleration of vehicles shall be applied near crossings with traffic lights and roundabouts. As part of the revision of the EPA guidelines on strategic noise mapping in Ireland, a technical investigation was carried out to identify the effect of the correction factor on road traffic noise emissions, and determine whether there was a practical approach to implement the correction factor within the upcoming noise mapping. This paper will provide an overview of the results, including the change in noise level emissions for low speed low flow roads, and high speed high flow road. It will also present a practical approach to preparing the road traffic flow models in GIS based on available datasets.

Bi-objective optimization of traffic assignment with air quality consideration via CFD-based surrogate model

Vehicular emissions form a major source to ambient air pollution, constituting the leading risk factor for public health globally. To mitigate this problem with sustainable urban mobility, the paper proposes an optimization framework to minimize the air pollutant concentrations at selected site(s), where the residents are susceptible to airborne hazards. Kowloon Peninsula, Hong Kong, is taken as the example. The on-road traffic volumes nearby are optimized to minimize the carbon monoxide (CO) concentrations as well as the accumulative travel time. The CO concentration is implicitly determined via the surrogate model (Gaussian process regression; GPR). Its dataset is established by the combination of traffic model, emission model, and computational fluid dynamics (CFD). The non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the bi-objective optimization problem. The outcome helps advance traffic strategy with the newly proposed optimization objective, giving priorities to vulnerable recipients at selected sensitive locations. Enabling the capacity to directly evaluate the pollutant concentration, it is the first attempt to incorporate surrogate model for pollutant dispersion based on experimentally verified CFD results into traffic assignment. This framework will facilitate the transport authorities to assign the traffic volumes and address health equity issues in a more precise manner.

Assessing on-road emissions from urban buses in different traffic congestion scenarios by integrating real-world driving, traffic, and emissions data

In recent years, the integration of traffic simulators and emission models has become the most preferred option for evaluating vehicle emissions in different traffic states. However, the definition of a ‘traffic condition’ is often subjective, as driving patterns can vary significantly with the spatial domain of study. Alternatively, the implementation of ‘Cooperative Intelligent Transport Systems’ has led to a growing variety of devices being installed, both on the road and in public transport vehicles for monitoring traffic-flow conditions and vehicle speeds in cities. This study purposed an original approach for integrating real-world emissions (as an micro-emission model), real-world driving profiles, and city traffic sensor data to assess the effects of traffic congestion at the route level on emissions from urban buses in Madrid (Spain). The definition of the traffic scenarios was based on a K-means clustering analysis by linking stationary (from city sensors) and dynamic (from bus driving profiles) congestion indicators. In parallel, a micro-emissions model based on vehicle-specific power (VSP) methodology was used to model second-by-second CO2 and NOx emissions from individual trips of the diesel and compressed natural gas (CNG) buses. Finally, the clustering and modelled emissions data were combined.A comparison of the free flow and the severe congestion scenarios showed that the average speed of the route decreased by approximately 50 %, and the number of stops per kilometre increased by a multiple of 1.5; furthermore, the CO2 and NOx emissions from buses increased by approximately 50 % and 85 %, respectively. The diesel bus showed a lower sensitivity to variations in the congestion level at the route level, although the low-NOx emissions from the CNG buses were evident for all traffic scenarios. The results of this study, based on extensive real-world data, can be used to develop high-resolution vehicle emissions inventories.

Efficient and environmentally friendly operation of intermittent dedicated lanes for connected autonomous vehicles in mixed traffic environments

The emergence of connected autonomous vehicles (CAVs) provides a possibility to develop an efficient and sustainable mobility option. To foster the adoption of CAVs, the dedicated CAV lanes have been widely discussed in terms of optimal planning and allocating infrastructures for CAVs. Meanwhile, a sensible operation manner of dedicated lanes is also crucial to reap the maximum benefits. In this paper, we propose a novel idea, i.e., the intermittent dedicated CAV lanes (IDCLs), which dynamically provide a separate right-of-way for human-driven vehicles (HVs) to maximize traffic efficiency and minimize environmental costs. To this end, we first tailor two multi-class queuing models as fundamental tools to evaluate the traffic state. A traffic emission model is proposed based on the proposed queuing models. Then, a queuing-based bi-objective optimization model, which aims to maximize the traffic throughput and minimize the green tax simultaneously, is developed for the optimal operation of IDCL. Subsequently, the non-dominated sorting genetic algorithm-II (NSGA-II) is employed to find the Pareto optimal solutions. Finally, numerical experiments are conducted under different market penetration rates (MPRs) of CAVs and various traffic intensities. The results reported in this paper help policymakers and authorities draw insights into the IDCL policy.

Comparison of microscale traffic emission models for urban networks

Traffic-related air quality issues remain in urban areas worldwide. For this reason, there is an increasing need to estimate the contribution of road traffic to atmospheric emissions at local level with high temporal and spatial resolution. Modal models compute emission rates as a function of specific engine or vehicle operating conditions at the highest resolution (seconds). They can be applied for microscale studies being a cost-effective tool to emulate differences in emissions levels in road networks. Two modal emission models, the Australian PΔP (Power-delta-Power) and the simplified version of the European PHEM (Passenger Car and Heavy-duty Emission Model), PHEM-light model, have been used. Also, a comparison to the cycle-variable emission model VERSIT+micro (Netherlands organisation for applied scientific research state of the art traffic emission model) has been performed. For the comparison of both modal models, the main variables involved in traffic emission calculation were identified. 1 Hz speed-time profiles for individual vehicles were generated with the traffic microsimulation model VISSIM (Vehrkehr in Statden SIMulation) for different traffic conditions. To understand the response of modal models, detailed estimations of NO X emissions and fuel consumption were compared for different vehicle classes. Instantaneous emission profiles for individual driving patterns are highly sensitive to speed-acceleration profiles, vehicle mass, and road gradient, which are essential variables for the emission calculation. Although there are differences between European and Australian models, engine power and load were used to map vehicle classes for a more consistent comparison. It is essential to accurately define these parameters for each vehicle class in addition to detailed driving patterns to obtain high-resolution emissions estimates. In this sense, a larger number of vehicle classes included in the model provides more flexibility to develop representative emissions estimates. Emission predictions between modal models were reasonably consistent presenting larger differences with the cycle-variable model, despite both modal models being based on different on-road fleet measurements. In conclusion, analysing emission estimations for different traffic conditions demonstrates the importance of an accurate definition of the model parameters for a specific vehicle fleet.

Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach

The role of the shipping industry in international logistics has been highlighted with the development of the global economy and the increase in international trade. Simultaneously, some of the environmental problems caused by shipping activities have gradually surfaced. The development of modern communication technology and marine communication equipment increased the feasibility of real-time ship dynamic data, as an information source for monitoring ship sailing states, and provided a data basis for the control of ship pollutant emissions. Based on the Automatic Identification System (AIS) data and ship-related data obtained from the waters of the ports of Los Angeles and Long Beach in 2020, the dynamic method is combined with the ship traffic emissions model STEAM2 to calculate the ship pollutant emissions in the two ports, and the relevant analysis work is conducted to evaluate the control effect of the Emission Control Area (ECA) policies on pollutant emissions. Results show that the ship pollutant emissions for CO, CXHX, NOX, SO2, PM10, and PM2.5 were 1230, 510, 11,700, 6670, 248, and 232 tons, respectively. These results also indicate the possible presence of a large gap in the distribution trend of ship pollutant emissions, according to different ship types and sailing states. Moreover, the control effect of various ECA policies on pollutant emissions is not the same, that is, the impact of ECA policies on SO2 and particulate matter is the largest, and that on NOX is minimal.

Decoupling Emission Reductions and Trade-Offs of Policies in Norway Based on a Bottom-Up Traffic Emission Model

The way Norway is spearheading electrification in the transport sector is of global interest. In this study, we used the Norwegian Emissions from Road Vehicle Exhaust (NERVE) model, a bottom-up high-resolution traffic emission model, to calculate all emissions in Norway (2009–2020) and evaluate potential co-benefit and trade-offs of policies to target climate change mitigation, air quality and socioeconomic factors. Results for municipal data with regard to traffic growth, road network influences, vehicle composition, emissions and energy consumption are presented. Light vehicle CO2 emissions per kilometer have been reduced by 22% since 2009, mainly driven by an increasing bio-fuel mixing and battery electric vehicles (BEV) share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicles has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEVs represent an investment in emission reductions in years to come, and current sales set Norway up for an accelerated decline in all exhaust emissions despite the continual growth in traffic.

To what extent the traffic restriction policies applied in Barcelona city can improve its air quality?

Barcelona city (Spain) is applying a series of traffic restriction measures that aim at renewing and reducing the amount of circulating vehicles to improve air quality. The measures include changes in the built environment to reduce private vehicle space in specific areas through the so-called “superblocks” and tactical urban planning actions, along with the implementation of a city-wide Low Emission Zone (LEZ) that restricts the entry of the most polluting vehicles to the city. Our study quantifies the impact of these measures in the greater area of Barcelona combining a coupled macroscopic traffic and pollutant emission model with a multi-scale air quality model. Our modelling system allows estimating the effect of different traffic restrictions upon traffic and the associated emissions and air quality levels at a very high resolution (20 m). The measures were evaluated both individually and collectively to assess both their relative and overall impact upon emissions and air quality. We show that in the absence of traffic demand reductions, the application of isolated measures that reduce private vehicle space, either through superblocks or tactical urban planning, have no overall emission impacts; only localized street-level NOx positive and negative changes (±17%) are found due to traffic re-routing and the generation of new bottlenecks. It is only when these measures are combined with optimistic fleet renewal as a result of the LEZ implementation and demand reductions, that relevant global emission reductions in NOx are obtained (−13% and −30%, respectively) with estimated NO2 reductions of −36% and −23% at the two traffic air quality monitoring stations. Despite the potential improvements, our simulations suggest that current measures are insufficient to comply with EU air quality standards and that further traffic restriction policies to reduce traffic demand are needed.

Impact of Various Operating Conditions on Simulated Emissions-Based Stop Penalty at Signalized Intersections

Sustainability has become one of the most important goals when optimizing traffic signals. This goal is achieved through utilizing various objective functions to reduce sustainability metrics (e.g., fuel consumption and emissions). However, most available objective functions do not distinguish between the reduction mechanism of various types of emissions. Further, such functions do not consider the compound impact of multiple operational conditions (e.g., road gradient) influencing emissions on the optimized signal plans. This study derives a new Environmental Performance Index representing a surrogate measure for emission estimates that can be used as an objective function in signal timings optimization to reduce emissions under various operational conditions. The Environmental Performance Index is a linear combination of delays and stops. The key factor of the Environmental Performance Index is the emissions-based stop penalty, which represents an emission stop equivalency measured in seconds of delay. This study also uses traffic simulation and emission models to investigate the compound impact of several operational conditions on the stop penalty. Results show that the stop penalty varies significantly with all the investigated conditions and that the stop penalty is unique for different types of emissions. These findings may have significant implications on the current practice of sustainable signal timing optimization.

Bike sharing systems implementation impact on emissions, for cyclist preferred routes in urban areas

Southern European countries such as Portugal still have a low bicycle share (up to 1%). Therefore, it is fundamental to research the potential benefits and drawbacks of modal shifts. This work analyses the implementation of a bike-sharing system (BSS) and its expected implications in emission reductions from road traffic for the case study of Coimbra city, Portugal. Although many different pollutants result from road traffic, we focused on small Particulate Matter (PM2.5) due to its harmful effects on human health. The methodology considers an integrated set of models for a) estimation of potential demand for the use of bike-sharing; b) mobility studies for the analysis of travel behavior; c) optimization modeling for the location of the bike-sharing stations and d) traffic emission modeling, to estimate the reduction of emissions due to bike-sharing modal shift. We considered two different potential scenarios for the location of bike stations. A modal share shift from car use toward bicycle in each is assumed, and the consequent traffic reduction and the associated emissions reduction are analyzed. The results show a non-homogeneous relation between traffic reduction and emission reduction across the urban space, achieving 12.5% of daily PM2.5 emissions in some urban roads.

Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection

ABSTRACT Combining traffic micro-simulation models and emission models is a primary method to estimate traffic emissions. However, there has been limited research on the impact of traffic micro-simulation model parameters on simulator outputs, which are critical to emissions calculations. Based on combining VISSIM and MOVES, this study uses the Morris and Sobol methods to explore the impact of parameters in VISSIM on operating mode distribution and travel time distribution. Taking an urban signal control intersection with three traffic scenarios in Chengdu, China as an example, this study verifies the methods’ feasibility. Apart from these parameters, which have been proved to be necessary calibration parameters, including the desired speed distribution, the desired acceleration function, and the desired deceleration function, an additional 24 parameters related to simulation setting and driving behaviour models are selected as the initial parameters. The number of interaction objects, maximum look-ahead distance, average standstill distance, additive part of safety distance, and safety distance reduction factor close to a stop line, are considered to be the important parameters for this case study. The impact of these five parameters on the bins of operating mode distribution and travel time distribution are further analyzed with One-at-a-time, and these parameters are compared with those reported in previous studies. It is concluded that the important parameters selected in this study are reasonable and can support the calibration of VISSIM parameters for this case’s traffic emissions.