Real Time Vehicle Emission Estimation Using Traffic Data
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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.
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.
Modeling Transportation Emissions Using Radar Based Vehicle Detection Data
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Author : Lang Yu
language : en
Publisher:
Release Date : 2016
Modeling Transportation Emissions Using Radar Based Vehicle Detection Data written by Lang Yu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
This dissertation introduces a new and novel methodology for estimating vehicle emissions at signalized intersections. Radar based vehicle detection systems, when placed at intersection approaches, is able to track vehicle operational characteristics at very high frequency, thus provides an ideal data source for emission estimation. By combining radar based vehicle detection data and MOVES project level analysis operating mode distribution approach, a real-time emission estimation system for signalized intersections is proposed. The Emission Computation Tool for Radar Data is developed to facilitate the automatic and continuous computation of operating mode distribution and emissions. The emission rates computed can also be integrated with existing air dispersion models in order to be used for air quality conformity and hot spot analysis. A case study is conducted to test the feasibility and validity of the proposed real-time emission estimation system. The results showed that the data collected should be used for computing a variety of parameters, including traffic volume, average speed, operating mode distribution, total emissions and emission rates for various pollutants. With emission rates, existing pollutant dispersion models such as AERMOD are applied, yielding pollutant concentrations at various locations, providing additional functionalities to the system. Evaluation results showed that the traffic volume and emission rates computed matches closely with AADT data and EPA's emission standards. Finally, an operating mode based macroscopic emission model is developed by using both empirical data from the case study as well as incorporating existing traffic flow dynamics model. This predictive model is based on estimating total time spent in each operating mode directly from traffic demand and other variables. Total time idling is modeled using kinematic wave theory and queuing theory, while others are modeled using empirical data. The validation results showed that the model is able to achieve a high degree of accuracy, within approximately 10 percent of emission results computed using the radar data. In conclusion, both the proposed real-time emission estimation system at signalized intersections and the emission model developed showed to yield highly accurate and detailed results, and are applicable in real world intersection locations.
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.
Implications Of Using Real Time And Estimated Speed Data In Air Quality Analyses
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Author : Dorriah L. Page
language : en
Publisher:
Release Date : 1995
Implications Of Using Real Time And Estimated Speed Data In Air Quality Analyses written by Dorriah L. Page and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Air categories.
Estimating Emissions By Modeling Freeway Vehicle Speed Profiles Using Point Detector Data
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Author : Jinheoun Choi
language : en
Publisher:
Release Date : 2014
Estimating Emissions By Modeling Freeway Vehicle Speed Profiles Using Point Detector Data written by Jinheoun Choi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
A method for accurate emissions estimation that will contribute to promoting public health has been increasingly important. The purpose of this study is to develop a novel method that is designed to make accurate real-time emissions estimation from individual vehicles on freeways possible. The benefit of this method is that it can overcome the weakness of macroscopic emissions estimation methods, which underestimated emissions. The most distinguishing feature of the Speed Profile Estimation (SPE) method is that it uses a speed profile (SP) that is generated by the sum of a basic SP (BSP), which is calculated by the basic travel information of an individual vehicle obtained from vehicle reidentification (REID), and a residual SP (RSP), which is estimated by categorized traffic information. In order to estimate RSP this research employs Autoregressive (AR) model and Fourier series (FS). And to find the parameters of RSP, the total absolute difference between actual SP emissions and estimated SP emissions was optimized by genetic algorithm. For this, parameters are calculated for all possible combinations of three categorizations and clusters by K-mean clustering. Individual vehicle trajectories from two freeways, US101 and I-80, were provided by the Next Generation Simulation (NGSIM) dataset. US101 was examined for calibration, and I-80 for validation. And then, transferability tests were conducted for various section distances to verify model transferability. Finally, REID is simulated with low vehicle signatures match rates to test its applicability to real situations. Unlike previous methods, the SPE is notable for its real-time, transferable, reliable, and cost efficient emissions estimation. The calibration and validation account only 4.0 % and 4.1 % MAPEs, respectively. Moreover, transferability tests showed that MAPEs are lower than 4.4 % in both longer and shorter section distances. Furthermore, REID simulation increases only 0.2 % MAPE even in low vehicle signatures match rates, which is lower than 5 % MAPE in emissions estimation. Any signal-like formulation other than AR or FS can perform better emissions estimation when it replaces the RSP. Also, in this research the SPE method was calibrated only for LOS F, when it is arguably of greatest value, but further research should be coordinated to extend the models in other possible traffic conditions such as LOS A~E.
Improving Traffic Simulation Models And Emissions Models Using On Board Vehicle Dynamics Data
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Author : Eric Daniel Jackson
language : en
Publisher:
Release Date : 2008
Improving Traffic Simulation Models And Emissions Models Using On Board Vehicle Dynamics Data written by Eric Daniel Jackson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Nowcasting Co2 Emissions Early Estimates Or Nowcasts For Monitoring Changes In Greenhouse Gas Emissions Ghgs
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Author : Pandis Iveroth, Sofie
language : en
Publisher: Nordic Council of Ministers
Release Date : 2022-08-16
Nowcasting Co2 Emissions Early Estimates Or Nowcasts For Monitoring Changes In Greenhouse Gas Emissions Ghgs written by Pandis Iveroth, Sofie and has been published by Nordic Council of Ministers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-16 with Science categories.
Available online: https://pub.norden.org/temanord2022-537/ Anthesis AB, Real-Zero Consulting HB, PlanMiljø Aps and Kausal Ltd have conducted a survey on the use and the possible uses of early estimates or “nowcasts” for monitoring changes in greenhouse gas emissions (GHGs) with a focus on fossil CO2. Nordic countries report emission statistics to the UNFCCC as well as EU. Due to more ambitious national mitigation targets, there is need for more timely emission statistics. The project suggests a development of early estimates through:- further analysis of the need and possibilities for nowcasting, focusing on a clear understanding of what timely emission estimates will be used for.- instigate a climate policy lab, given the complexity to overview and communicate the need and possibilities for nowcasting data.- create a portal collecting available emission statistics, as currently it is a complex task to overview available emission statistics.
Modal Emissions Modeling With Real Traffic Data
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Author : Jason A. Crawford
language : en
Publisher:
Release Date : 1999
Modal Emissions Modeling With Real Traffic Data written by Jason A. Crawford and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Air categories.
This report details the use of a modal emissions model to estimate the relative emissions of CO due to changes in vehicle operating characteristics on urban roadways. The Davis Institute for Transportation Studies Emissions Model (DITSEM) was selected to demonstrate the emissions characteristics of different freeway operating conditions. Instrumented vehicle data collected in Houston, Texas provides a set of operating parameters for which CO emissions are estimated. These estimates are calculated for different times of the day on the same facility to determine the relative emissions levels from a representative vehicle traveling on the freeway. The research team examined 10 samples along three roadways (two freeways, and one arterial). Implausible results were found in data exhibiting high average speeds (>60 mph) where average emissions rates were higher than those on the same roadway under congested conditions. This led to several conclusions of which the most important was that the DITSEM model not be used with samples where the percent of the driving cycle greater than 60 mph 2/sec exceeds 9%. This limit represents the highest value from which the model was derived for this variable. In addition, it is noted that the speed instrumentation was not able to provide sufficient precision for meaningful analysis with the available data.