scholarly journals Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model

2020 ◽  
Vol 17 (4) ◽  
pp. 1259 ◽  
Author(s):  
Changxi Ma ◽  
Jibiao Zhou ◽  
Dong Yang
Keyword(s):  
Influence Factors ◽  
Hazardous Materials ◽  
Model Parameters ◽  
Ordered Logit ◽  
Road Transportation ◽  
Hazardous Materials Transportation ◽  
Logit Regression ◽  
Environmental Properties ◽  
The Impact ◽  
Causation Analysis

Understanding the influence factors and related causation of hazardous materials can improve hazardous materials drivers’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates the statistical distribution characteristics, such as types of hazardous materials transportation accidents, driver properties, vehicle properties, environmental properties, road properties. In total, 343 data regarding hazardous materials accidents were collected from the chemical accident information network of China. An ordered logit regression (OLR) model is proposed to account for the unobserved heterogeneity across observations. Four independent variables, such as hazardous materials drivers’ properties, vehicle properties, environmental properties, and road properties are employed based on the OLR model, an ordered multinomial logistic regression (MLR) is estimated the OLR model parameters. Both parameter estimates and odds ratio (OR) are employed to interpret the impact of influence factors on the severity of hazardous materials accidents. The model estimation results show that 10 factors such as violations, unsafe driving behaviors, vehicle faults, and so on are closely related to accidents severity of hazardous materials transportation. Furthermore, three enforcement countermeasures are proposed to prevent accidents when transporting hazardous materials.

scholarly journals Risk Assessment of Hazmat Road Transportation Considering Environmental Risk under Time-Varying Conditions

2021 ◽  
Vol 18 (18) ◽  
pp. 9780
Author(s):  
Liping Liu ◽  
Qing Wu ◽  
Shuxia Li ◽  
Ying Li ◽  
Tijun Fan
Keyword(s):  
Risk Assessment ◽  
Environmental Pollution ◽  
Environmental Risk ◽  
Hazardous Materials ◽  
Assessment Model ◽  
Population Exposure ◽  
Model Parameters ◽  
Time Varying ◽  
Road Transportation ◽  
Hazardous Materials Transportation

Hazardous materials shipments are integral to the development of industrial countries. Significant casualties and severe environmental pollution quickly ensue when accidents occur. Currently, relevant research on risk assessment of hazardous materials’ road transportation remains limited when both the population exposure risk and environmental risk are considered, especially in regard to analyzing the differences of accident impacts in different populations and environments. This paper adopts a Gaussian plume model to simulate dynamic areas at three levels of population exposure and assesses the pollution scope of air, groundwater, lakes, and rivers with a variety of diffusion models. Then, we utilize various costs to analyze the differences of accident impacts in population exposure and environmental pollution. Finally, a risk assessment model of hazardous materials road transportation under time-varying conditions is presented by considering the bearing capacity of the assessed area. Furthermore, this model is applied to a case study involving a risk assessment of hazardous materials transportation of a highly populated metropolitan area of Shanghai, China. The resulting analyses reveal that the safety of hazardous materials transportation could be effectively improved by controlling certain model parameters and avoiding road segments with a high risk of catastrophic accident consequences.

The influence of daily localization during treatment of prostate cancer on acute toxicity.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 214-214
Author(s):  
Peter john Rossi ◽  
Sherrie Cooper ◽  
Ashesh B. Jani
Keyword(s):  
Prostate Cancer ◽  
Intensity Modulated Radiotherapy ◽  
Definitive Treatment ◽  
Gleason Grade ◽  
Ordered Logit ◽  
Chi Square ◽  
Logit Regression ◽  
Gu Toxicity ◽  
Gi Toxicity ◽  
The Impact

214 Background: Various localization techniques are used in clinical practice but little comparative effectivess data exists. The purpose of this investigation is to assess the impact of daily localization (L) of the prostate on toxicity during definitive treatment of prostate cancer with intensity modulated radiotherapy (IMRT). Methods: This study details an IRB approved retrospective review of three cohorts of patients treated for prostate cancer utilizing IMRT and different L technologies at four academic hospitals. Patients were treated with no technique (N), gold markers (G), or electromagnetic beacons [Calypso] (C) for daily L. Acute genitourinary (GU) and gastrointestinal (GI) toxicity was assessed according to RTOG toxicity criteria; toxicity rates between the groups were compared using the chi-square test. Ordered logit regression of all major demographic (age, race), disease (PSA, Gleason grade, and T-stage) and treatment (hormones, pelvic nodal treatment, total dose, and prior TURP) characteristics on GU and on GI toxicity were performed. Results: One hundred fifty four men who received definitive dose escalated IMRT (no brachytherapy or post-prostatectomy patients) for prostate cancer from 2006 to 2010 and were included (G: n=47; C: n=47; N: n=60). The cohorts were balanced except race (higher percentage of African Americans in the N group, due to hospital demographics) and higher grade and use of pelvic nodal treatment (which were more common in the G and C groups). Bladder V40 and V70 were higher in the N group (45.4%/24.3%) compared to C (27.9%/12.3%) and G (37.4%/18.4%) groups; however, rectal V40 and V70 were similar among all 3 groups. Toxicity results are shown in the Table; as shown the C and G groups had lower GU toxicity (p <0.001) respectively compared to the N group; no significant differences in GI toxicity were seen among the 3 groups. Ordered logit regression showed only daily L (p=0.041) reached significance in lowering GU toxicity, and only pelvic RT use (p =0.008) reached significance in increasing GI toxicity. Conclusions: In patients treated with IMRT, daily L was associated with lower acute GU toxicity but not acute GI toxicity.

scholarly journals Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression

2019 ◽  
Vol 11 (7) ◽  
pp. 2071 ◽  
Author(s):  
Yanyong Guo ◽  
Yao Wu ◽  
Jian Lu ◽  
Jibiao Zhou
Keyword(s):  
Risk Factors ◽  
Unobserved Heterogeneity ◽  
Multinomial Logit ◽  
Model Parameters ◽  
License Plate ◽  
Speed Limit ◽  
Random Parameters ◽  
Logit Regression ◽  
Multinomial Logit Regression ◽  
The Impact

Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

scholarly journals Electric Vehicle Integration into Road Transportation, Intelligent Transportation, and Electric Power Systems: An Abu Dhabi Case Study

Smart Cities ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 1039-1057
Author(s):  
Amro M. Farid ◽  
Asha Viswanath ◽  
Reem Al-Junaibi ◽  
Deema Allan ◽  
Thomas J. T. Van der Van der Wardt
Keyword(s):  
Electric Power ◽  
Intelligent Transportation ◽  
Energy System ◽  
Transportation System ◽  
Electric Power System ◽  
Abu Dhabi ◽  
Road Transportation ◽  
Unit Distance ◽  
The Impact

Recently, electric vehicles (EV) have gained much attention as a potential enabling technology to support CO2 emissions reduction targets. Relative to their internal combustion vehicle counterparts, EVs consume less energy per unit distance, and add the benefit of not emitting any carbon dioxide in operation and instead shift their emissions to the existing local fleet of power generation. However, the true success of EVs depends on their successful integration with the supporting infrastructure systems. Building upon the recently published methodology for the same purpose, this paper presents a “systems-of-systems” case study assessing the impacts of EVs on these three systems in the context of Abu Dhabi. For the physical transportation system, a microscopic discrete-time traffic operations simulator is used to predict the kinematic state of the EV fleet over the duration of one day. For the impact on the intelligent transportation system (ITS), the integration of EVs into Abu Dhabi is studied using a multi-domain matrix (MDM) of the Abu Dhabi Department of Transportation ITS. Finally, for the impact on the electric power system, the EV traffic flow patterns from the CMS are used to calculate the timing and magnitude of charging loads. The paper concludes with the need for an intelligent transportation-energy system (ITES) which would coordinate traffic and energy management functionality.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

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