Emission Estimation Based On Traffic Models And Measurements
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Emission Estimation Based On Traffic Models And Measurements
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Author : Nikolaos Tsanakas
language : en
Publisher: Linköping University Electronic Press
Release Date : 2019-04-24
Emission Estimation Based On Traffic Models And Measurements written by Nikolaos Tsanakas and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-24 with categories.
Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements. In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect. Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.
Real Time Vehicle Emission Estimation Using Traffic Data
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Author : Anjie Liu
language : en
Publisher:
Release Date : 2019
Real Time Vehicle Emission Estimation Using Traffic Data written by Anjie Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Traffic flow categories.
The current state of climate change should be addressed by all sectors that contribute to it. One of the major contributors is the transportation sector, which generates a quarter of greenhouse gas emissions in North America. Most of these transportation related emissions are from road vehicles; as result, how to manage and control traffic or vehicular emissions is therefore becoming a major concern for the governments, the public and the transportation authorities. One of the key requirements to emission management and control is the ability to quantify the magnitude of emissions by traffic of an existing or future network under specific road plans, designs and traffic management schemes. Unfortunately, vehicular traffic emissions are difficult to quantify or predict, which has led a significant number of efforts over the past decades to address this challenge. Three general methods have been proposed in literature. The first method is for determining the traffic emissions of an existing road network with the idea of measuring the tail-pipe emissions of individual vehicles directly. This approach, while most accurate, is costly and difficult to scale as it would require all vehicles being equipped with tail-pipe emission sensors. The second approach is applying ambient pollutant sensors to measure the emissions generated by the traffic near the sensors. This method is only approximate as the vehicle-generated emissions can easily be confounded by other nearby emitters and weather and environmental conditions. Note that both of these methods are measurement-based and can only be used to evaluate the existing conditions (e.g., after a traffic project is implemented), which means that it cannot be used for evaluating alternative transportation projects at the planning stage. The last method is model-based with the idea of developing models that can be used to estimate traffic emissions. The emission models in this method link the amount of emissions being generated by a group of vehicles to their operations details as well as other influencing factors such as weather, fuel and road geometry. This last method is the most scalable, both spatially and temporally, and also most flexible as it can meet the needs of both monitoring (using field data) and prediction. Typically, traffic emissions are modelled on a macroscopic scale based on the distance travelled by vehicles and their average speeds. However, for traffic management applications, a model of higher granularity would be preferred so that impacts of different traffic control schemes can be captured. Furthermore, recent advances in vehicle detection technology has significantly increased the spatiotemporal resolutions of traffic data. For example, video-based vehicle detection can provide more details about vehicle movements and vehicle types than previous methods like inductive loop detection. Using such detection data, the vehicle movements, referred to as trajectories, can be determined on a second-by-second basis. These vehicle trajectories can then be used to estimate the emissions produced by the vehicles. In this research, we have proposed a new approach that can be used to estimate traffic generated emissions in real time using high resolution traffic data. The essential component of the proposed emission estimation method is the process to reconstruct vehicle trajectories based on available data and some assumptions on the expected vehicle motions including cruising, acceleration and deceleration, and car-following. The reconstructed trajectories containing instantaneous speed and acceleration data are then used to estimate emissions using the MOVES emission simulator. Furthermore, a simplified rate-based module was developed to replace the MOVES software for direct emission calculation, leading to significant improvement in the computational efficiency of the proposed method. The proposed method was tested in a simulated environment using the well-known traffic simulator - Vissim. In the Vissim model, the traffic activities, signal timing, and vehicle detection were simulated and both the original vehicle trajectories and detection data recorded. To evaluate the proposed method, two sets of emission estimates are compared: the "ground truth" set of estimates comes from the originally simulated vehicle trajectories, and the set from trajectories reconstructed using the detection data. Results show that the performance of the proposed method depends on many factors, such as traffic volumes, the placement of detectors, and which greenhouse gas is being estimated. Sensitivity analyses were performed to see whether the proposed method is sufficiently sensitive to the impacts of traffic control schemes. The results from the sensitivity analyses indicate that the proposed method can capture impacts of signal timing changes and signal coordination but is insufficiently sensitive to speed limit changes. Further research is recommended to validate the proposed method using field studies. Another recommendation, which falls outside of this area of research, would be to investigate the feasibility of equipping vehicles with devices that can record their instantaneous fuel consumption and location data. With this information, traffic controllers would be better informed for emission estimation than they would be with only detection data.
Measurement Analysis And Modeling Of On Road Vehicle Emissions Using Remote Sensing
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Author :
language : en
Publisher:
Release Date : 1905
Measurement Analysis And Modeling Of On Road Vehicle Emissions Using Remote Sensing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1905 with categories.
The main objectives of this research are; to develop on-road emission factor estimates for carbon monoxide (CO) and hydrocarbon (HC) emissions; to collect traffic and vehicle parameters that might be important in explaining variability in vehicle emissions; to develop an empirical traffic-based model that can predict vehicle emissions based upon observable traffic and vehicle parameters. Remote sensing technology were employed to collect exhaust emissions data. Traffic parameters were collected using an area-wide traffic detector, MOBILIZER. During the measurements, license plates were also recorded to obtain information on vehicle parameters. Data were collected at two sites, having different road grades and site geometries, over 10 days of field work at the Research Triangle area of North Carolina. A total of 11,830 triggered measurement attempts were recorded. After post-processing, 7,056 emissions were kept in the data base as valid measurements. After combining with the traffic and license vehicle parameters, a data base has been developed. Exploratory analysis has been conducted to find variables that are important to explain the variability of the emission estimates. Statistical methods were used to compare the mean of the emissions estimates for different sub-populations. For example, multi-comparison analysis has been conducted to compare the mean emissions estimates from vehicles having different model years. This analysis showed that the mean emissions from older vehicles were statistically different than the mean emissions estimates from the recent model year vehicles. One of the contributions of the research was developing an empirical traffic-based emission estimation model. For this purpose, data collected during the study were used to develop a novel model which combines the Hierarchical Tree-Based Regression method and Ordinary Least Squares regression. The key findings from this research include: (1) the measured mean CO emission estimate for Resear.
Improving On Road Emission Estimates With Traffic Detection Technologies
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Author : Hang Liu
language : en
Publisher:
Release Date : 2013
Improving On Road Emission Estimates With Traffic Detection Technologies written by Hang Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Transportation has been a significant contributor to greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation's impacts on our environment. In order to effectively develop and evaluate on-road emissions reduction strategies, accurate quantification of emissions is the critical first step. The accuracy and resolution of the traffic measures needed by the emission models will directly affect the emission estimation results. This dissertation investigates the ability of traffic detection technologies to provide the traffic measures needed for accurate on-road emissions estimation. A review of traffic detection technologies is provided with insight into their capability and suitability for estimating emissions. The Inductive Vehicle Signature (IVS) system is identified as currently the most promising technology to couple with EPA's latest MOVES emission model for estimating emissions. Models and algorithms based on the IVS detection system are developed to generate the two most important traffic measures for emission estimation: vehicle mix and average speed. The performances of the models are verified using real-world data. Assuming the IVS system and the models developed are deployed to generate vehicle mix and average speeds, the accuracy and reliability of the emissions estimation results based on these traffic measures are evaluated by simulating the operations of the models in the field using NGSIM data. Very promising results are obtained, which clearly demonstrates the capability of the IVS system for on-road emissions estimation. A Real-Time Emissions Estimation and Monitoring System based on the IVS technology is implemented on the I-405 freeway to estimate operational emissions on the road in real-time. Although average speed has been the most common input into emission models, the MOVES model is capable of using second-by-second vehicle speed trajectories to estimate emissions more accurately. Vehicle speed trajectories are becoming increasingly available thanks to the proliferation of GPS-enabled personal navigation devices and smartphones. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. This dissertation studies the use of a limited number of GPS speed trajectories to estimate emissions for all traffic on the road. Two fundamental questions are answered by this work: 1) how can GPS data be used for emissions estimation, and 2) how does the penetration rate of the GPS probes affect the emission results. With the methods proposed in this study, it is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes, when combined with the vehicle mix data generated from the IVS system. Discussions on the applications of the proposed systems and methods to various emissions analysis scenarios are also provided in this dissertation.
Citywide Time Dependent Grid Based Traffic Emissions Estimation And Air Quality Inference Using Big Data
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Author : Qing Li
language : en
Publisher:
Release Date : 2017
Citywide Time Dependent Grid Based Traffic Emissions Estimation And Air Quality Inference Using Big Data written by Qing Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Due to industrial development and an increasing number of vehicles, many countries are suffering from air pollution, especially smog. High costs of directly measuring traffic emissions (one of the major sources of air pollution) and air quality have restricted government agencies to obtain accurate and timely information. Cellular phone activity data is cell phone communication records with cellular towers, generated during phone calls, texting, user data exchange activities, and all other cellular network system communication. This study develops a grid-based time-dependent traffic emissions estimation and air quality inference model on a citywide scale by using cellular activity data. First, data processing and mode choice model are proposed to remove noise data and detect travel mode. Then, map matching algorithm is proposed to project non-consecutive points to obtain the complete paths. Traffic emissions can be estimated based on these trajectories and the International Vehicle Emissions (IVE). Moreover, a feature-based air quality inference model is proposed. Weighted air quality information, traffic information, weather, human mobility and POI information are used as model inputs, and random forest learner is introduced to infer grid-based time-dependent air quality information for locations without monitor stations. Two case studies are designed to demonstrate the performance of the proposed models. In the case study of Taicang, the results demonstrated the effectiveness and the rationality of the proposed model in traffic and emissions estimation. Different from traditional vehicle emission models that can only detect emissions in some fixed points, the proposed model can estimate traffic emissions on a citywide scale on the hour-by-hour basis. In the case study of Shanghai, the first part is to demonstrate the effectiveness of the traffic emissions estimation model. The traffic calculated by the proposed model is close to the average weekly vehicle miles traveled. The second part of the experiments on Shanghai demonstrated the effectiveness of the proposed air quality inference model which improves both root-mean-square errors and mean absolute percentage error. The proposed model is applicable in the real world and helps government agencies to obtain accurate and timely information of traffic emissions and air quality.
Urban Traffic Emissions Cost Estimation Based On An Integrated Modeling Approach
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Author : Song Bai
language : en
Publisher:
Release Date : 2019
Urban Traffic Emissions Cost Estimation Based On An Integrated Modeling Approach written by Song Bai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
"According to Canadian Environmental Sustainability Indicators (CESI), in 2015, the transport sector was the 4th leading source of PM2.5 emissions in Canada. Vehicular emissions contribute significantly to air quality problems and public health issues in urban areas. Nevertheless, previous studies have shown that transport users do not perceive their travel-related emissions as out of pocket costs. In addition, travelers prefer emissions’ information in monetary values rather than in their own units (tons or grams of emissions). In this study, I estimate the health-related costs of transport emissions for Montreal residents. In addition, I examine the use of an air dispersion model (AERMOD) in estimating travel-related air pollution concentrations for four intersections in Bucaramanga, Columbia.In the second chapter, I estimate and quantify emissions generated on the Montreal road network. First, I transform the emissions rates estimated from the MOVES software into air pollution concentrations. Then, I convert the concentrations into health outcomes. Finally, I valuate these health outcomes in monetary terms. My results show that among three key emission types, NOx has the highest emission cost (up to $0.38/km), followed by PM2.5 ($0.31/km) and CO ($0.0074/km), during peak hours. In addition, the downtown and Plateau areas have the highest total emissions costs per km. In the second part of the thesis, I apply an air dispersion model (AERMOD) to simulate the air pollutant movements at four intersections in Bucaramanga, Colombia. My results show that the higher traffic volume, the higher the emission rates for both PM2.5 and Black Carbon, except for when heavy trucks’ percentage is high. The La Provenza intersection generates the highest PM2.5 rate (90g/h during peak hours and 16g/h during off-peak hours) and Black Carbon (15g/h during peak hours and 3g/h during off-peak hours). In addition, the air pollution concentrations are highest among the most congested links, in all studied intersections. Moreover, the PM2.5 and Black Carbon concentrations drop off substantially when moving away from the intersections’ centers, and then gradually decrease after 50 meters. In addition, compared to the real measurements (by the equipment installed in the intersections), the proposed set of models (MOVES+AERMOD) captures most of the general trends in PM2.5 and Black Carbon. However, the predicted concentrations are less than the observed measurements. This could be due to the fact that some factors are neglected, and those can affect the results, factors including emissions generated by people’s other daily activities (e.g., cooking), the relatively old vehicle fleet in Colombia (different from MOVES’s fleet), etc. I conducted a set of sensitivity analyses to understand the performance of the AERMOD dispersion model in estimating PM2.5 concentrations, by altering the input data. My results show that AERMOD is highly sensitive to wind conditions. The temperature was observed to have a slightly negative correlation with PM2.5 concentrations. My results could be used to raise public awareness regarding the health impacts of traffic-induced air pollution, and eventually could change travel behavior of urban travelers. Keywords: Urban traffic; health-related emissions cost; Montreal transport users; MOVES; Emission rates; Bucaramanga, Colombia intersections; Air pollution dispersion modeling, and air pollution concentration"--
Overview Of Emission And Traffic Models And Evaluation Of Vehicle Simulation Tools
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Author :
language : en
Publisher:
Release Date : 2013
Overview Of Emission And Traffic Models And Evaluation Of Vehicle Simulation Tools written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
One of the main concerns regarding the road transport sector is the fact that it constitutes one of the main sources of air pollution, especially in urban areas, since the combustion of hydrocarbon fuels in vehicles produces several pollutants. The most common approach for the assessment of traffic-related emission factors is the exhaust gas measurement of vehicles on chassis dynamometers over various driving cycles. A rather favourable approach in order to reduce the number of experimental procedures and thereby the cost of such tests is the development and calibration of vehicle simulation tools and emission models which could be used for the accurate evaluation and quantification of vehicle emissions without the necessity of expensive experimental campaigns. Today, there are several tools for the estimation of traffic-related emissions. Such tools are essential in any European or global policy dealing with emission projections, air pollution or climate change issues.^This study presents a description of the current emission models (COPERT, EMFAC, etc.), traffic (AIMSUN, Vissim, etc.) and vehicle simulation tools (ADVISOR, AUTONOMIE, PH EM etc.). The review of existing models and methods provides evidence that there is a large variety of available tools to calculate traffic-related emissions and to develop road transport emission inventories, however, new trends and policies must also be fully incorporated in the existing tools. In addition, in order to use emission models and vehicle simulation tools in the proper way, detailed and precise measurements of vehicle operation are required, otherwise any potential benefits may be lost. This is likely to be rather difficult since such information is relatively expensive or difficult to collect. For example, certain input data may not be available, such as vehicle loading and gear-shift behaviour. The last point raises an important consideration regarding model complexity.^More complex models have the potential to provide more accurate predictions as they take into account more variables. However, they also require more detailed input data which may not be readily available to the model user.
Environmental Sustainability And Economy
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Author : Pardeep Singh
language : en
Publisher: Elsevier
Release Date : 2021-07-28
Environmental Sustainability And Economy written by Pardeep Singh and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-28 with Science categories.
Environmental Sustainability and Economy contains the latest practical and theoretical concepts of sustainability science and economic growth. It includes the latest research on sustainable development, the impact of pollution due to economic activities, energy policies and consumption influencing growth and environment, waste management and recycling, circular economy, and climate change impacts on both the environment and the economy. The 21st century has seen the rise of complex and multi-dimensional pathways between different aspects of sustainability. Due to globalization, these relationships now work at varying spatiotemporal scales resulting in global and regional dynamics. This book explores the complex relationship between sustainable development and economic growth, linking the environmental and social aspects with the economic pillar of sustainable development. Utilizing global case studies and interdisciplinary perspectives, Environmental Sustainability and Economy provides a comprehensive account of sustainable development and the economics of environmental protection studies with a focus on the environmental, geographical, economic, anthropogenic and social-ecological environment. Includes extensive interdisciplinary coverage, including intersectional topics such as environmental pollution and economic growth, resource utilization and circular economy, climate change and emissions, and sustainable solutions and green behavior Discusses market innovations and strategies through the lens of global case studies in sustainability and economic growth Bridges the gap between environmental studies and economics to reflect sustainable practices for enhancing environmental protection in response to climate change
A Sampling Of Emissions Analysis Techniques For Transportation Control Measures
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Author :
language : en
Publisher:
Release Date : 2000
A Sampling Of Emissions Analysis Techniques For Transportation Control Measures written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Air categories.
Individual And Environmental Determinants Of Traffic Emissions And Near Road Air Quality
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Author : Junshi Xu
language : en
Publisher:
Release Date : 2020
Individual And Environmental Determinants Of Traffic Emissions And Near Road Air Quality written by Junshi Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
On-road motor vehicles are responsible for a considerable proportion of near-road air pollution. While background levels of air pollutants are continuously tracked by regional monitoring networks, assessing near-road air quality remains a challenge in urban areas with complex built environments, traffic composition, and meteorological variation, leading to significant spatiotemporal variability in air pollution. This research addresses current gaps in the literature on local traffic emissions and near-road air quality. This thesis first investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Traffic emissions are estimated using radar data as well as simulated traffic based on various speed aggregation methods. It provides recommendations for project-level analysis and particulate matter (PM) hotspot analysis. We further compare fleet averaged emission factors (EFs) derived from a traffic emission model, the Motor Vehicle Emissions Simulator (MOVES), with EFs using plume-based measurements. This second module stresses the need to collect local traffic information for a better understanding of on-road traffic emissions. Besides, we validate default drive cycles in MOVES against representative drive cycles derived based on real-world GPS data. The validation results are helpful for transportation planners to quantify uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories. The third module develops eco-score models and evaluates the effect of various factors such as driver and trip characteristics on emission intensities. The results shed light on the impact of driving style on emissions and identify the most important factors affecting the amount of emissions generated by every individual driver. The fourth module focuses on the impact of traffic emissions on near-road air quality and presents the results of two different experiments. First, it explores the effect of various factors on near-road ultrafine particle (UFP) concentrations based on short-term fixed monitoring, which stresses the significance of using local traffic characteristics to improve near-road air quality prediction. In addition, it captures the distribution of truck movements in urban environments and investigates the impacts of land-use variables and detailed traffic information on near-road Black Carbon (BC) concentrations.