State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performan...State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performance evaluations. Nonetheless, an apparent gap exists between the need for ITS performance measurements and the actual implementation. The evidence available points to challenges in the ITS performance measurement processes. This paper evaluated the state of practice of performance measurement for ITS across the US and provided insights. A comprehensive literature review assessed the use of performance measures by DOTs for monitoring implemented ITS programs. Based on the gaps identified through the literature review, a nationwide qualitative survey was used to gather insights from key stakeholders on the subject matter and presented in this paper. From the data gathered, performance measurement of ITS is fairly integrated into ITS programs by DOTs, with most agencies considering the process beneficial. There, however, exist reasons that prevent agencies from measuring ITS performance to greater detail and quality. These include lack of data, fragmented or incomparable data formats, the complexity of the endeavor, lack of data scientists, and difficulty assigning responsibilities when inter-agency collaboration is required. Additionally, DOTs do not benchmark or compare their ITS performance with others for reasons that include lack of data, lack of guidance or best practices, and incomparable data formats. This paper is relevant as it provides insights expected to guide DOTs and other agencies in developing or reevaluating their ITS performance measurement processes.展开更多
Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data main...Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.展开更多
Hydrologic modeling is a popular tool for estimating the hydrological response of a watershed. However, modeling processes are becoming more complex due to land-use changes such as urbanization, industrialization, and...Hydrologic modeling is a popular tool for estimating the hydrological response of a watershed. However, modeling processes are becoming more complex due to land-use changes such as urbanization, industrialization, and the expansion of agricultural activities. The primary goal of the research was to use the HEC-HMS model to evaluate the impact of impervious soil layers and the increase in rainfall-runoff processes on hydrologic processes. For these purposes, the Watershed Modelling System (WMS) and Hydrologic Engineering Center’s-Hydrologic Modeling System (HEC-HMS) models were used in this study to simulate the rainfall-runoff process. To compute runoff rate, runoff volume, base flow, and flow routing methods SCS curve number, SCS unit hydrograph, recession, and loss routing methods were selected for the research, respectively. To reduce the processing time and computational complexity, a small section of the Pipestem Creek Watershed was selected to understand the methods and concepts associated with the hydrologic simulation model building. A DEM along with other required data such as land use land cover data, soil type data, and meteorological data was utilized to delineate the watershed in WMS. The output of WMS was utilized to run the HEC-HMS model for five different scenario analyses. All the relevant data were plugged in to the model to get the desired map. Subsequently, outlets at appropriate locations were selected for the sub-basin delineation for further analysis. Finally, the model was parametrized to get successful simulation results. Overall, peak discharges and runoff volumes were increased with increasing storm depths and impervious areas. Peak discharges were increased to 36% and 51% when rainfall depths were increased by 10% and 20% from the initial rainfall depth, respectively. Runoff volumes were also increased to 35% and 49% for the same scenarios, respectively. Peak discharges were increased to 12% and 78% with a 10% and 20%, respectively, increase in impervious areas. The runoff volumes were increased by 12% and 76% when impervious areas were increased by 10% and 20%, respectively. The simulation models responded well, and the peak discharges and runoff volumes increased with increasing storm depths and impervious areas.展开更多
Soil infiltration is a very important concept in hydrology as well as irrigation, which plays a vital role in estimating surface runoff and groundwater recharge. It is a complicated process that varies with numerous f...Soil infiltration is a very important concept in hydrology as well as irrigation, which plays a vital role in estimating surface runoff and groundwater recharge. It is a complicated process that varies with numerous factors. Accurate estimation of soil infiltration is required for future irrigation, and many other purposes. To estimate the infiltration process, there are numerous models. The majority of them have some presumptions, a unique calculation method, and some limitations. The purpose of the paper was to assess the model’s performance for a similar hypothetical scenario involving soil infiltration. It compared the infiltration rate, runoff rate, and incremental infiltration versus time for three different infiltration models: the Green-Ampt model (GA), the Horton model and the Modified Green-Ampt (MGA) model. A spreadsheet was used to calculate the Horton model, and HYDROL-INF (V 5.03) was used to simulate the other two models. Among those three models, the MGA model outperformed those three models, while the GA model produced greater infiltration rate than rainfall, which was insensible. The study showed that the MGA model, which provides useful infiltration predictions, outperformed the other two infiltration models. Since the Horton model does not consider ponding conditions, it is only applicable when the effective rainfall intensity exceeds the final infiltration capacity. Moreover, the GA model’s initial infiltration rate is irrational because it disregards the intensity of the rainfall. The results of this study will assist in selecting the most accurate method for estimating soil infiltration for agricultural purposes.展开更多
Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep...Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.展开更多
Greenhouse gas monitoring on a broader scale is necessary to ensure that a cap-and-trade system is effective, reduces measurement uncertainty, and detects fraudulent or illegal activities. The recent strict air qualit...Greenhouse gas monitoring on a broader scale is necessary to ensure that a cap-and-trade system is effective, reduces measurement uncertainty, and detects fraudulent or illegal activities. The recent strict air quality regulation in livestock production facilities has accelerated the need for accurate on-farm determination of greenhouse gas (GHG) emission rates (ERs) from livestock operations in the United States under a wide range of production, management, and climate conditions. The estimation of GHG emissions from different ground-level sources or at a property line is a very complicated process, and such measurements require multidirectional expertise including engineering, micrometeorology, agronomy, applied physics, and chemistry. Accurate measurement of gaseous concentration from an emitting source is a prerequisite and of paramount importance for estimating emissions rates (ERs) using any micro-meteorological and sampling device-based method. This paper provides an overview of the state-of-the-art sensors and analyzers used to measure GHG concentrations. Sensor and analyzer selection and their performance in the laboratory and field were discussed. In addition, protocols for data quality control (QC) and quality assurance (QA) when deploying sensors in the area for long-term use were also discussed. In addition, the preparation of measurement systems, coupling of air samplers with sensing systems for measuring gaseous concentrations, and uncertainties inherent to such measurement methods as a whole to estimate ERs were discussed in this paper.展开更多
A series of tests were carried out on sulfate rich,high-plasticity clay and poorly-graded natural sand to study the effectiveness of a methylene diphenyl diisocyanate based liquid polymer soil stabilizer in improving ...A series of tests were carried out on sulfate rich,high-plasticity clay and poorly-graded natural sand to study the effectiveness of a methylene diphenyl diisocyanate based liquid polymer soil stabilizer in improving the unconfined compressive strength(UCS) of freshly stabilized soils and aged sand specimens.The aged specimens were prepared by exposing the specimens to ultraviolet radiation,freeze-thaw,and wet-dry weathering.The polymer soil stabilizer also mitigated the swelling of the expansive clay.For clay,the observations indicated that the sequence of adding water and liquid polymer had great influence on the gained UCS of stabilized specimens.However,this was shown to be of little importance for sand.Furthermore,sand samples showed incremental gains in UCS when they were submerged in water.This increase was significant for up to 4 days of soaking in water after 4 days of ambient air curing.Conversely,the clay samples lost a large fraction of their UCS when soaked in water;however,their remaining strength was still considerable.The stabilized specimens showed acceptable endurance under weathering action,although sample yellowing due to ultraviolet radiation was evident on the surface of the specimens.Except for moisture susceptibility of the clay specimens,the results of this study suggested the liquid stabilizer could be successfully utilized to provide acceptable strength,durability and mitigated swelling.展开更多
Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of ...Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.展开更多
文摘State departments of transportation’s (DOTs) decisions to invest resources to expand or implement intelligent transportation systems (ITS) programs or even retire existing infrastructure need to be based on performance evaluations. Nonetheless, an apparent gap exists between the need for ITS performance measurements and the actual implementation. The evidence available points to challenges in the ITS performance measurement processes. This paper evaluated the state of practice of performance measurement for ITS across the US and provided insights. A comprehensive literature review assessed the use of performance measures by DOTs for monitoring implemented ITS programs. Based on the gaps identified through the literature review, a nationwide qualitative survey was used to gather insights from key stakeholders on the subject matter and presented in this paper. From the data gathered, performance measurement of ITS is fairly integrated into ITS programs by DOTs, with most agencies considering the process beneficial. There, however, exist reasons that prevent agencies from measuring ITS performance to greater detail and quality. These include lack of data, fragmented or incomparable data formats, the complexity of the endeavor, lack of data scientists, and difficulty assigning responsibilities when inter-agency collaboration is required. Additionally, DOTs do not benchmark or compare their ITS performance with others for reasons that include lack of data, lack of guidance or best practices, and incomparable data formats. This paper is relevant as it provides insights expected to guide DOTs and other agencies in developing or reevaluating their ITS performance measurement processes.
文摘Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.
文摘Hydrologic modeling is a popular tool for estimating the hydrological response of a watershed. However, modeling processes are becoming more complex due to land-use changes such as urbanization, industrialization, and the expansion of agricultural activities. The primary goal of the research was to use the HEC-HMS model to evaluate the impact of impervious soil layers and the increase in rainfall-runoff processes on hydrologic processes. For these purposes, the Watershed Modelling System (WMS) and Hydrologic Engineering Center’s-Hydrologic Modeling System (HEC-HMS) models were used in this study to simulate the rainfall-runoff process. To compute runoff rate, runoff volume, base flow, and flow routing methods SCS curve number, SCS unit hydrograph, recession, and loss routing methods were selected for the research, respectively. To reduce the processing time and computational complexity, a small section of the Pipestem Creek Watershed was selected to understand the methods and concepts associated with the hydrologic simulation model building. A DEM along with other required data such as land use land cover data, soil type data, and meteorological data was utilized to delineate the watershed in WMS. The output of WMS was utilized to run the HEC-HMS model for five different scenario analyses. All the relevant data were plugged in to the model to get the desired map. Subsequently, outlets at appropriate locations were selected for the sub-basin delineation for further analysis. Finally, the model was parametrized to get successful simulation results. Overall, peak discharges and runoff volumes were increased with increasing storm depths and impervious areas. Peak discharges were increased to 36% and 51% when rainfall depths were increased by 10% and 20% from the initial rainfall depth, respectively. Runoff volumes were also increased to 35% and 49% for the same scenarios, respectively. Peak discharges were increased to 12% and 78% with a 10% and 20%, respectively, increase in impervious areas. The runoff volumes were increased by 12% and 76% when impervious areas were increased by 10% and 20%, respectively. The simulation models responded well, and the peak discharges and runoff volumes increased with increasing storm depths and impervious areas.
文摘Soil infiltration is a very important concept in hydrology as well as irrigation, which plays a vital role in estimating surface runoff and groundwater recharge. It is a complicated process that varies with numerous factors. Accurate estimation of soil infiltration is required for future irrigation, and many other purposes. To estimate the infiltration process, there are numerous models. The majority of them have some presumptions, a unique calculation method, and some limitations. The purpose of the paper was to assess the model’s performance for a similar hypothetical scenario involving soil infiltration. It compared the infiltration rate, runoff rate, and incremental infiltration versus time for three different infiltration models: the Green-Ampt model (GA), the Horton model and the Modified Green-Ampt (MGA) model. A spreadsheet was used to calculate the Horton model, and HYDROL-INF (V 5.03) was used to simulate the other two models. Among those three models, the MGA model outperformed those three models, while the GA model produced greater infiltration rate than rainfall, which was insensible. The study showed that the MGA model, which provides useful infiltration predictions, outperformed the other two infiltration models. Since the Horton model does not consider ponding conditions, it is only applicable when the effective rainfall intensity exceeds the final infiltration capacity. Moreover, the GA model’s initial infiltration rate is irrational because it disregards the intensity of the rainfall. The results of this study will assist in selecting the most accurate method for estimating soil infiltration for agricultural purposes.
文摘Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.
文摘Greenhouse gas monitoring on a broader scale is necessary to ensure that a cap-and-trade system is effective, reduces measurement uncertainty, and detects fraudulent or illegal activities. The recent strict air quality regulation in livestock production facilities has accelerated the need for accurate on-farm determination of greenhouse gas (GHG) emission rates (ERs) from livestock operations in the United States under a wide range of production, management, and climate conditions. The estimation of GHG emissions from different ground-level sources or at a property line is a very complicated process, and such measurements require multidirectional expertise including engineering, micrometeorology, agronomy, applied physics, and chemistry. Accurate measurement of gaseous concentration from an emitting source is a prerequisite and of paramount importance for estimating emissions rates (ERs) using any micro-meteorological and sampling device-based method. This paper provides an overview of the state-of-the-art sensors and analyzers used to measure GHG concentrations. Sensor and analyzer selection and their performance in the laboratory and field were discussed. In addition, protocols for data quality control (QC) and quality assurance (QA) when deploying sensors in the area for long-term use were also discussed. In addition, the preparation of measurement systems, coupling of air samplers with sensing systems for measuring gaseous concentrations, and uncertainties inherent to such measurement methods as a whole to estimate ERs were discussed in this paper.
基金Alchemy Polymers Company,LLC for their financial support
文摘A series of tests were carried out on sulfate rich,high-plasticity clay and poorly-graded natural sand to study the effectiveness of a methylene diphenyl diisocyanate based liquid polymer soil stabilizer in improving the unconfined compressive strength(UCS) of freshly stabilized soils and aged sand specimens.The aged specimens were prepared by exposing the specimens to ultraviolet radiation,freeze-thaw,and wet-dry weathering.The polymer soil stabilizer also mitigated the swelling of the expansive clay.For clay,the observations indicated that the sequence of adding water and liquid polymer had great influence on the gained UCS of stabilized specimens.However,this was shown to be of little importance for sand.Furthermore,sand samples showed incremental gains in UCS when they were submerged in water.This increase was significant for up to 4 days of soaking in water after 4 days of ambient air curing.Conversely,the clay samples lost a large fraction of their UCS when soaked in water;however,their remaining strength was still considerable.The stabilized specimens showed acceptable endurance under weathering action,although sample yellowing due to ultraviolet radiation was evident on the surface of the specimens.Except for moisture susceptibility of the clay specimens,the results of this study suggested the liquid stabilizer could be successfully utilized to provide acceptable strength,durability and mitigated swelling.
文摘Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.
