Effect of roadway network aggregation levels on modeling of traffic-induced emission inventories in Beirut

Traffic-induced emissions pose a serious threat to air quality in heavily congested urban centers. While air quality can be characterized through field measurements and continuous monitoring, forecasting future conditions depends largely on estimating vehicle-emission factors coupled with mathematical modeling. Traffic and environmental planners have relied on overall average network speed in conjunction with speed-based emission factor models to estimate traffic emissions. This paper investigates the effect of three levels of roadway network aggregation, macro-scale (overall network basis), meso-scale (roadway functional class basis) and micro-scale (link-by-link basis) on emission inventories. A traffic model and an emission factor model were integrated to determine total emissions in the future Beirut Central District area for these three modeling approaches.     

Assessing the importance of transportation activity data for urban emission inventories

The aim of this research is the implementation of a GPS-based modelling approach for improving the characterization of vehicle speed spatial variation within urban areas, and a comparison of the resulting emissions with a widely used approach to emission inventory compiling. The ultimate goal of this study is to evaluate and understand the importance of activity data for improving the road transport emission inventory in urban areas. For this purpose, three numerical tools, namely, (i) the microsimulation traffic model (VISSIM); (ii) the mesoscopic emissions model (TREM); and (iii) the air quality model (URBAIR), were linked and applied to a medium-sized European city (Aveiro, Portugal). As an alternative, traffic emissions based on a widely used approach are calculated by assuming a vehicle speed value according to driving mode. The detailed GPS-based modelling approach results in lower total road traffic emissions for the urban area (7.9, 5.4, 4.6 and 3.2% of the total PM10, NO_x, CO and VOC daily emissions, respectively). Moreover, an important variation of emissions was observed for all pollutants when analysing the magnitude of the 5th and 95th percentile emission values for the entire urban area, ranging from −15 to 49% for CO, −14 to 31% for VOC, −19 to 46% for NO_x and −22 to 52% for PM10. The proposed GPS-based approach reveals the benefits of addressing the spatial and temporal variability of the vehicle speed within urban areas in comparison with vehicle speed data aggregated by a driving mode, demonstrating its usefulness in quantifying and reducing the uncertainty of road transport inventories.     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

The significant impacts on traffic and emissions of ferrying children to school in Beijing

After having implemented numerous regulations, e.g., coercive policies on vehicle use and purchase, it is becoming increasingly difficult to find further potential to control vehicle emissions in Beijing, as the air quality is still poor. This research provides a different approach for policy-makers to reduce vehicle emissions by managing demand. We found that parents ferrying their children to and from school is an important but long-neglected contributor to traffic congestion and vehicle emissions. This phenomenon is very common in China because of the social culture. In this research, parallel tests during both the school season and the non-school season were adopted, and emissions in both seasons were calculated based on travel demand and emission models. The results revealed that emissions factors (in g/km) for criteria pollutants and CO₂ increased by over 10% during rush hours during the school season due to traffic condition deterioration compared with non-school season. Daily HC, CO, NO_x, PM and CO₂ emissions from the passenger car fleet were 8.3%, 7.8%, 6.4%, 6.3% and 6.5% higher compared with those during the non-school season, respectively. These differences are greater than the total vehicular emission reduction by other control measures in 2014 in Beijing. For policy makers, providing safe and efficient ways to ferry children would be a useful and harmonious strategy for future vehicle emission control.     

Air traffic assignment to reduce population noise exposure using activity-based approach

Aircraft noise has been regarded as one of the major environmental issues related to air transport. Many airports have introduced a variety of measures to reduce its impact. Several air traffic assignment strategies have been proposed in order to allocate noise more wisely. Even though each decision regarding the assignment of aircraft to routes should consider population exposure to noise, none of the air traffic assignment strategies has addressed daily migrations of population and number of people exposed to noise. The aim of this research is to develop a mathematical model and a heuristic algorithm that could assign aircraft to departure and arrival routes so that number of people exposed to noise is as low as possible, taking into account temporal and spatial variations in population in an airport’s vicinity. The approach was demonstrated on Belgrade airport to show the benefits of the proposed model. Numerical example showed that population exposure to noise could be reduced significantly by applying the proposed air traffic assignment model. As a consequence of the proposed air traffic assignment, overall fuel consumption increased by less than 1%.     

Estimating transboundary economic damages from climate change and air pollution for subnational incentives for green on-road freight

Subnational incentives to adopt zero emission vehicles (ZEVs) are critical for reducing the external economic damages posed by transportation to air quality and the climate. Few studies estimate these damages for on-road freight, especially at scales relevant for subnational policies requiring cross-border cooperation. Here, we assess the damages to US receptors from emissions of air pollutants (PM_2.5, NO_x, SO₂, NH₃), and greenhouse gases (CO₂, CH₄, N₂O) from medium and heavy duty freight trucking, and the benefits of ZEV adoption by census division in the Province of Ontario. We develop an integrated modelling framework connecting a travel demand model, a mobile emissions simulator, and a regression based marginal damages model of air pollutants and climate change. We estimate $1.9 billion (2010 USD) in annual cross-border damages, or $0.16/VKT, resulting from scaled up atmospheric emissions from a ‘typical day’ of medium and heavy duty truck traffic volume for Ontario in 2012. This implies approximately $8000 per truck per year in damages, which could inform an economic incentive for emission reduction. The provincial goal of 5% ZEV adoption would reduce GHG emissions in 2012 by 800 ktCO₂e, yielding $89 Million (2010 USD) in cross-border benefits annually, with air quality co-benefits of $83/tCO₂e. This result varies between −19% and 22% based on sensitivity analysis for travel and emissions models, though economic damages are likely the largest uncertainty source. Such advances in subnational scale integrated modeling of the environmental impacts of freight can offer insights into the sustainable design of clean freight policy and programs.     

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 (NO_x) 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 NO_x 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.     

Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data

Nowadays, the massive car-hailing data has become a popular source for analyzing traffic operation and road congestion status, which unfortunately has seldom been extended to capture detailed on-road traffic emissions. This study aims to investigate the relationship between road traffic emissions and the related built environment factors, as well as land uses. The Computer Program to Calculate Emissions from Road Transport (COPERT) model from European Environment Agency (EEA) was introduced to estimate the 24-h NOx emission pattern of road segments with the parameters extracted from Didi massive trajectory data. Then, the temporal Fuzzy C-Means (FCM) Clustering was used to classify road segments based on the 24-h emission rates, while Geographical Detector and MORAN’s I were introduced to verify the impact of built environment on line source emissions and the similarity of emissions generated from the nearby road segments. As a result, the spatial autoregressive moving average (SARMA) regression model was incorporated to assess the impact of selected built environment factors on the road segment emission rate based on the probabilistic results from FCM. It was found that short road length, being close to city center, high density of bus stations, more ramps nearby and high proportion of residential or commercial land would substantially increase the emission rate. Finally, the 24-h atmospheric NO₂ concentrations were obtained from the environmental monitor stations, to calculate the time variational trend by comparing with the line source traffic emissions, which to some extent explains the contribution of on-road traffic to the overall atmospheric pollution. Result of this study could guide urban planning, so as to avoid transportation related built environment attributes which may contribute to serious atmospheric environment pollutions.     

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21%, 33%, 24% and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than non-commercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity.     

Comparing the impact of future airline network change on emissions in India and the United States

In this paper we use simulation to analyze how flight routing network structure may change in different world regions, and how this might impact future traffic growth and emissions. We compare models of the domestic Indian and US air transportation systems, representing developing and mature air transportation systems respectively. We explicitly model passenger and airline decision-making, capturing passenger demand effects and airline operational responses, including airline network change. The models are applied to simulate air transportation system growth for networks of 49 airports in each country from 2005 to 2050. In India, the percentage of connecting passengers simulated decreases significantly (from over 40% in 2005 to under 10% in 2050), indicating that a shift in network structure towards increased point-to-point routing can be expected. In contrast, very little network change is simulated for the US airport set modeled. The simulated impact of network change on system CO₂ emissions is very small, although in the case of India it could enable a large increase in demand, and therefore a significant reduction in emissions per passenger (by nearly 25%). NO_x emissions at major hub airports are also estimated, and could initially reduce relative to a case in which network change is not simulated (by nearly 25% in the case of Mumbai in 2025). This effect, however, is significantly reduced by 2050 because of frequency competition effects. We conclude that network effects are important when estimating CO₂ emissions per passenger and local air quality effects at hub airports in developing air transportation systems.     

Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data

Ambient concentrations of pollutants are correlated with emissions, but the contribution to ambient air quality of on-road mobile sources is not necessarily equal to their contribution to regional emissions. This is true for several reasons such as the distribution of other pollution sources and regional topology, as well as meteorology. In this paper, using a dataset from a travel demand model for the Sacramento metropolitan area for 2005, regional vehicle emissions are disaggregated into hourly, gridded emission inventories, and transportation-related concentrations are estimated using an atmospheric dispersion model. Contributions of on-road motor vehicles to urban air pollution are then identified at a regional scale. The contributions to ambient concentrations are slightly higher than emission fractions that transportation accounts for in the region, reflecting that relative to other major pollution sources, mobile sources tend to have a close proximity to air quality monitors in urban areas. The contribution results indicate that the impact of mobile sources on PM₁₀ is not negligible, and mobile sources have a significant influence on both NO_x and VOC pollution that subsequently results in secondary particulate matter and ozone formation.     

A traffic noise model for road intersections in the city of Cartagena de Indias,Colombia

Road traffic noise models are fundamental tools for designing and implementing appropriate prevention plans to minimize and control noise levels in urban areas. The objective of this study is to develop a traffic noise model to simulate the average equivalent sound pressure level at road intersections based on traffic flow and site characteristics, in the city of Cartagena de Indias (Cartagena), Colombia. Motorcycles are included as an additional vehicle category since they represent more than 30% of the total traffic flow and a distinctive source of noise that needs to be characterized. Noise measurements are collected using a sound level meter Type II. The data analysis leads to the development of noise maps and a general mathematical model for the city of Cartagena, Colombia, which correlates the sound levels as a function of vehicle flow within road intersections. The highest noise levels were 79.7 dB(A) for the road intersection María Auxiliadora during the week (business days) and 77.7 dB(A) for the road intersection India Catalina during weekends (non-business days). Although traffic and noise are naturally related, the intersections with higher vehicle flow did not have the highest noise levels. The roadway noise for these intersections in the city of Cartagena exceeds current limit standards. The roadway noise model is able to satisfactorily predict noise emissions for road intersections in the city of Cartagena, Colombia.     

The environmental effects of airline carbon emissions taxation in the US

This study investigates how air traffic emissions taxes may impact carbon emissions in the US. The magnitude of emissions savings in the US domestic airline industry that would result from lower demand for air travel as taxes are levied and air fares increase is estimated. At the same time, the air-automobile substitution effect is considered and it is argued that some air travelers may divert to automobiles, thus increasing automobile carbon emissions. Both the analysis of the aggregate US domestic airline industry and the study of a sample of US domestic route markets indicate that potentially sizeable increases in automobile traffic and related emissions may substantially reduce the environmental benefits of air travel carbon emissions taxes. In some instances, carbon emissions may even increase in short-haul markets. Sensitivity analyses are performed to demonstrate the robustness of these findings.     

Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand

This paper proposes a novel short/medium-term prediction method for aviation emissions distribution in en route airspace. An en route traffic demand model characterizing both the dynamics and the fluctuation of the actual traffic demand is developed, based on which the variation and the uncertainty of the short/medium-term traffic growth are predicted. Building on the demand forecast the Boeing Fuel Flow Method 2 is applied to estimate the fuel consumption and the resulting aviation emissions in the en route airspace. Based on the traffic demand prediction and the en route emissions estimation, an aviation emissions prediction model is built, which can be used to forecast the generation of en route emissions with uncertainty limits. The developed method is applied to a real data set from Hefei Area Control Center for the en route emission prediction in the next 5 years, with time granularities of both months and years. To validate the uncertainty limits associated with the emission prediction, this paper also presents the prediction results based on future traffic demand derived from the regression model widely adopted by FAA and Eurocontrol. The analysis of the case study shows that the proposed method can characterize well the dynamics and the fluctuation of the en route emissions, thereby providing satisfactory prediction results with appropriate uncertainty limits. The prediction results show a gradual growth at an average annual rate of 7.74%, and the monthly prediction results reveal distinct fluctuation patterns in the growth.     

Research review of air traffic management

As air transport demand keeps growing more quickly than system capacity, efficient and effective management of system capacity becomes essential to the operation of the future global air traffic system. Although research in the past two decades has made significant progress in relevant research fields, e.g. air traffic flow management and airport capacity modelling, research loopholes in air traffic management still exist and links between different research areas are required to enhance the system performance of air traffic management. Hence, the objective of this paper is to review systematically current research in the literature about the issue of air traffic management to prioritize productive research areas. Papers about air traffic management are discussed and categorized into two levels: system and airport. The system level of air transport research includes two main topics: air traffic flow management and airspace research. On the airport level, research topics are: airport capacity, airport facility utilization, aircraft operations in the airport terminal manoeuvring area as well as aircraft ground operations research. Potential research interests to focus on in the future are the integration between airspace capacity and airport capacity, the establishment of airport information systems to use airport capacity better, and the improvement in flight schedule planning to improve the reliability of schedule implementation.     

Air transport and high-speed rail competition: Environmental implications and mitigation strategies

We build a duopoly model to shed light on the environmental impact of HSR-air transport competition, capturing the effects of induced demand, schedule frequency and HSR speed. The net environmental effect can be negative since there is a the trade-off between the substitution effect – how many passengers using the HSR are shifted from air transport – and the traffic generation effect – how much new demand is generated by the HSR. We conduct a simulation study based on the London-Paris market where HSR has served 70% of the market. The introduction of HSR is detrimental to LAP, while it is beneficial to GHG emissions. HSR entry increases neither LAP nor GHG emissions when the ratio between HSR and air transport emissions is relatively low. Moreover, competition is more likely to be detrimental to the environment when the weight of the social welfare in HSR objective function is high. Since the magnitude of the environmental friendliness of HSR compared to air transport hinges on the mix of energy sources used to generate the electricity (which is heavily constrained by the country in which HSR operates), regulators should assess the implications of HSR entry taking into account the energy policy and mitigation strategies available to transport modes.     

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.     

Temporal disaggregation of travel demand for high resolution emissions inventories

The commonly used photochemical air quality model, the Urban Airshed Model (UAM), requires emission estimates with grid-based, hourly resolution. In contrast, travel demand models, used to simulate the travel activity model inputs for the transportation-related emissions estimation, typically only provide traffic volumes for a specific travel period (e.g. the a.m. and p.m. peak periods). A few transportation agencies have developed procedures to allocate period-based travel demand data into hourly emission inventories for regional grid cells. Because there was no theoretical framework for disaggregating period-based volumes to hourly volumes, application of these procedures frequently relied upon a single hypothetical hourly distribution of travel volumes. This study presents a new theoretical modeling framework that integrates traffic count data and travel demand model link volume estimates to derive intra-period hourly volume estimates by trip purpose. We propose a new interpretation of the model coefficients and define hourly allocation factors by trip purpose. These allocation factors can be used to disaggregate the travel demand model ‘period-based’ simulation volumes into hourly resolution, thereby improving grid-based, hourly emission estimates in the UAM.     

Determination and Applications of Environmental Costs at Different Sized Airports – Aircraft Noise and Engine Emissions

With the increasing trend of charging for externalities and the aim of encouraging the sustainable development of the air transport industry, there is a need to evaluate the social costs of these undesirable side effects, mainly aircraft noise and engine emissions, for different airports. The aircraft noise and engine emissions social costs are calculated in monetary terms for five different sized airports, ranging from hub airports to small regional airports. The number of residences within different levels of airport noise contours and the aircraft noise classifications are the main determinants for accessing aircraft noise social costs. The environmental impacts of aircraft engine emissions include both aircraft landing and take-off and 30-minute cruise. The social costs of aircraft emissions vary by engine type and aircraft category, depending on the damage caused by different engine pollutants on the human health, vegetation, materials, aquatic ecosystem and climate. The results indicate that the relationship appears to be curvilinear between environmental costs and the traffic volume of an airport. The results and methodology of environmental cost calculation could be applied to the proposed European wide harmonised noise charges as well as the social cost benefit analysis of airports.     

Induced road traffic in Spanish regions: A dynamic panel data model

Distinguishing between traffic generated exclusively from the expansion of the road network (induced demand) and that resulting from other demand factors is of crucial importance to properly designed transport policies. This paper analyzes and quantifies the induced demand for road transport for Spain’s main regions from 1998 to 2006, years that saw mobility in Spain attain its highest growth rate. The lack of research in this area involving Spain and the key role played by the sector, given its high level of energy consumption and the negative externalities associated with it (accidents, noise, traffic congestion, emissions, etc.), endow greater relevance to this type of research. Based on a Dynamic Panel Data (DPD) reduced-form model, we apply alternative approaches (fixed and random effects and GMM-based methods) for measuring the induced demand. The results obtained provide evidence for the existence of an induced demand for transport in Spain, though said results vary depending on the estimating method employed.