Emission estimation based on traffic models and measurements

Emission estimation based on traffic models and measurements

Author: Nikolaos Tsanakas

Publisher: Linköping University Electronic Press

Published: 2019-04-24

Total Pages: 131

ISBN-13: 9176850927

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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.


Book Synopsis Emission estimation based on traffic models and measurements by : Nikolaos Tsanakas

Download or read book Emission estimation based on traffic models and measurements written by Nikolaos Tsanakas and published by Linköping University Electronic Press. This book was released on 2019-04-24 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Real-time Vehicle Emission Estimation Using Traffic Data

Author: Anjie Liu

Publisher:

Published: 2019

Total Pages: 142

ISBN-13:

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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.


Book Synopsis Real-time Vehicle Emission Estimation Using Traffic Data by : Anjie Liu

Download or read book Real-time Vehicle Emission Estimation Using Traffic Data written by Anjie Liu and published by . This book was released on 2019 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Measurement, Analysis, and Modeling of On-Road Vehicle Emissions Using Remote Sensing

Author:

Publisher:

Published: 1905

Total Pages:

ISBN-13:

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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.


Book Synopsis Measurement, Analysis, and Modeling of On-Road Vehicle Emissions Using Remote Sensing by :

Download or read book Measurement, Analysis, and Modeling of On-Road Vehicle Emissions Using Remote Sensing written by and published by . This book was released on 1905 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Citywide Time-dependent Grid-based Traffic Emissions Estimation and Air Quality Inference Using Big Data

Citywide Time-dependent Grid-based Traffic Emissions Estimation and Air Quality Inference Using Big Data

Author: Qing Li

Publisher:

Published: 2017

Total Pages: 276

ISBN-13:

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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.


Book Synopsis Citywide Time-dependent Grid-based Traffic Emissions Estimation and Air Quality Inference Using Big Data by : Qing Li

Download or read book Citywide Time-dependent Grid-based Traffic Emissions Estimation and Air Quality Inference Using Big Data written by Qing Li and published by . This book was released on 2017 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Environmental Sustainability and Economy

Environmental Sustainability and Economy

Author: Pardeep Singh

Publisher: Elsevier

Published: 2021-07-28

Total Pages: 384

ISBN-13: 0128223650

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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


Book Synopsis Environmental Sustainability and Economy by : Pardeep Singh

Download or read book Environmental Sustainability and Economy written by Pardeep Singh and published by Elsevier. This book was released on 2021-07-28 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

A Sampling of Emissions Analysis Techniques for Transportation Control Measures

Author:

Publisher:

Published: 2000

Total Pages: 64

ISBN-13:

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Book Synopsis A Sampling of Emissions Analysis Techniques for Transportation Control Measures by :

Download or read book A Sampling of Emissions Analysis Techniques for Transportation Control Measures written by and published by . This book was released on 2000 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Overview of Emission and Traffic Models and Evaluation of Vehicle Simulation Tools

Overview of Emission and Traffic Models and Evaluation of Vehicle Simulation Tools

Author: Marina Kousoulidou

Publisher:

Published: 2013

Total Pages: 75

ISBN-13: 9789279346811

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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.


Book Synopsis Overview of Emission and Traffic Models and Evaluation of Vehicle Simulation Tools by : Marina Kousoulidou

Download or read book Overview of Emission and Traffic Models and Evaluation of Vehicle Simulation Tools written by Marina Kousoulidou and published by . This book was released on 2013 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Handbook on Climate Change and Technology

Handbook on Climate Change and Technology

Author: Frauke Urban

Publisher: Edward Elgar Publishing

Published: 2023-12-11

Total Pages: 563

ISBN-13: 1800882114

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This timely Handbook presents the latest knowledge on technological innovation for climate change mitigation and adaptation. Looking beyond technical fixes, it further draws on economics, politics and sociology to explore how modern technology can contribute to effective and socially just sustainability transitions.


Book Synopsis Handbook on Climate Change and Technology by : Frauke Urban

Download or read book Handbook on Climate Change and Technology written by Frauke Urban and published by Edward Elgar Publishing. This book was released on 2023-12-11 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely Handbook presents the latest knowledge on technological innovation for climate change mitigation and adaptation. Looking beyond technical fixes, it further draws on economics, politics and sociology to explore how modern technology can contribute to effective and socially just sustainability transitions.

Improving On-road Emission Estimates with Traffic Detection Technologies

Improving On-road Emission Estimates with Traffic Detection Technologies

Author: Hang Liu

Publisher:

Published: 2013

Total Pages: 188

ISBN-13: 9781303462016

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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.


Book Synopsis Improving On-road Emission Estimates with Traffic Detection Technologies by : Hang Liu

Download or read book Improving On-road Emission Estimates with Traffic Detection Technologies written by Hang Liu and published by . This book was released on 2013 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Emissions Reduction Through Better Traffic Management

Emissions Reduction Through Better Traffic Management

Author:

Publisher:

Published: 2001

Total Pages: 398

ISBN-13:

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The objectives of this study were to: (1) evaluate a new low-cost approach for measuring on-road tailpipe emissions of highway vehicles; (2) investigate factors that affect the amount and variability of on-road emissions, using statistical methods; and (3) devise and demonstrate methods for designing and conducting observational experiments that realistically evaluate pollution prevention strategies for on-road vehicles. Portable instruments were used for measuring carbon monoxide (CO), nitric oxide (NO), and hydrocarbon (HC) emissions and vehicle activity (e.g., vehicle speed, engine parameters) on a second-by-second basis. Data collection, quality assurance, reduction, and analysis protocols were developed. Field data collection occurred in a pilot and an evaluation phase. In total, over 1,200 one-way trips were made with more than 20 vehicles, 4,000 vehicle-miles traveled, 160 hours of data, and 10 drivers. The pilot study was used to identify key factors influencing on-road emissions and as input to the design of the evaluation study. In the evaluation study, data were collected intensively with a small number of vehicles on two corridors before and after signal timing and coordination changes were implemented. For the first corridor, changes in signal timing and coordination did not result in a significant change in traffic flow or emissions. However, substantial reductions in emissions were estimated for uncongested versus congested traffic flow when comparing travel in the same direction at different times of day. For the second corridor, there were significant improvements in traffic flow and some reduction in emissions for three of the four time period and travel direction combinations evaluated. The impact of signal timing and coordination changes with respect to non-priority movements involving cross-streets was evaluated. For the first corridor, there was no statistically significant observed change in emissions for non-priority movements. For the second corridor, there typically was a decrease in average speed and an increase in emissions for non-priority movements; however, many of the observed changes were not statistically significant. The study also demonstrated other analysis methods, including: (a) macro-scale analysis of trip average emissions and traffic parameters; (b) micro-scale analysis of second-by-second emissions and vehicle operation; (c) mesoscale analysis of modal emission rates; and (d) spatial analysis of emissions at specific locations along the corridors. Both statistical and theoretical-based approaches were evaluated. The implications of the study results for pollution prevention strategies are discussed. Conclusions are presented regarding instrumentation, protocols, analysis techniques, and case study-specific findings. Recommendations are given regarding future applications of on-board measurements.


Book Synopsis Emissions Reduction Through Better Traffic Management by :

Download or read book Emissions Reduction Through Better Traffic Management written by and published by . This book was released on 2001 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objectives of this study were to: (1) evaluate a new low-cost approach for measuring on-road tailpipe emissions of highway vehicles; (2) investigate factors that affect the amount and variability of on-road emissions, using statistical methods; and (3) devise and demonstrate methods for designing and conducting observational experiments that realistically evaluate pollution prevention strategies for on-road vehicles. Portable instruments were used for measuring carbon monoxide (CO), nitric oxide (NO), and hydrocarbon (HC) emissions and vehicle activity (e.g., vehicle speed, engine parameters) on a second-by-second basis. Data collection, quality assurance, reduction, and analysis protocols were developed. Field data collection occurred in a pilot and an evaluation phase. In total, over 1,200 one-way trips were made with more than 20 vehicles, 4,000 vehicle-miles traveled, 160 hours of data, and 10 drivers. The pilot study was used to identify key factors influencing on-road emissions and as input to the design of the evaluation study. In the evaluation study, data were collected intensively with a small number of vehicles on two corridors before and after signal timing and coordination changes were implemented. For the first corridor, changes in signal timing and coordination did not result in a significant change in traffic flow or emissions. However, substantial reductions in emissions were estimated for uncongested versus congested traffic flow when comparing travel in the same direction at different times of day. For the second corridor, there were significant improvements in traffic flow and some reduction in emissions for three of the four time period and travel direction combinations evaluated. The impact of signal timing and coordination changes with respect to non-priority movements involving cross-streets was evaluated. For the first corridor, there was no statistically significant observed change in emissions for non-priority movements. For the second corridor, there typically was a decrease in average speed and an increase in emissions for non-priority movements; however, many of the observed changes were not statistically significant. The study also demonstrated other analysis methods, including: (a) macro-scale analysis of trip average emissions and traffic parameters; (b) micro-scale analysis of second-by-second emissions and vehicle operation; (c) mesoscale analysis of modal emission rates; and (d) spatial analysis of emissions at specific locations along the corridors. Both statistical and theoretical-based approaches were evaluated. The implications of the study results for pollution prevention strategies are discussed. Conclusions are presented regarding instrumentation, protocols, analysis techniques, and case study-specific findings. Recommendations are given regarding future applications of on-board measurements.