Modelling follow up time at a single-lane roundabout

The inverse Gaussian regression model (IGRM) is applied when the response variable is continuous, positively skewed and well fitted to the inverse Gaussian distribution. In the presence of multicollinearity, the maximum likelihood estimation (MLE) is not a right choice. Therefore, we proposed a new estimator called the principal component ridge estimator for the IGRM which combines the principal component estimator and the ridge estimator. We also consider a two-parameter estimator (TPE) and other biased estimators to see a clear image of our proposed estimator. A Monte Carlo simulation study is also presented to examine the performance of the proposed estimators. Furthermore, we analysed a dataset to assess the superiority of the proposed estimator. Based on the simulation and application results, it is evident that the proposed estimator dominates the classical MLE, and other considered biased estimation methods.

Inverse Gaussian regression models are useful for regression data where both variables are nonnegative and the variance of the dependent variable depends on the independent variable, Zero intercept inverse Gaussian regression models are presented with non-constant variance, constant ratio of variance to the mean and constant coefficient of variation, For purposes of calibration, the prediction band is used to give point and interval estimators for the independent variable, The results are illustrated with a real data set.

ABSTRACTThe Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.

Background: Dengue Hemorrhagic Fever (DHF) is a major health problem in East Java Province due to population density, sanitation, availability of medical personnel, and altitude. DHF count data often experiences overdispersion, challenging the Poisson regression assumption in the Generalized Linear Model (GLM). Objective: To compare the performance of Poisson, Negative Binomial (NBR), and Poisson Inverse Gaussian (PIG) ​​models in modeling DHF cases. Method: A cross-sectional quantitative study analyzed 38 districts/cities in East Java (saturation sampling) using secondary data processed with . Analysis: Descriptive statistics, multicollinearity test (VIF<10), overdispersion test, and model comparison via AIC/deviance. Results: NBR achieved the lowest AIC (412.10) compared to Poisson (1466.60) and PIG (412.41), confirming significant overdispersion (deviance/df ≈37.16). Population density and altitude had a significant effect (p<0.05). Conclusion: NBR is optimal for DHF modeling, supporting targeted environmental and health interventions in East Java.

We define a new four-parameter model called the odd log-logistic generalized inverse Gaussian distribution which extends the generalized inverse Gaussian and inverse Gaussian distributions. We obtain some structural properties of the new distribution. We construct an extended regression model based on this distribution with two systematic structures, which can provide more realistic fits to real data than other special regression models. We adopt the method of maximum likelihood to estimate the model parameters. In addition, various simulations are performed for different parameter settings and sample sizes to check the accuracy of the maximum likelihood estimators. We provide a diagnostics analysis based on case-deletion and quantile residuals. Finally, the potentiality of the new regression model to predict price of urban property is illustrated by means of real data.

One of the basic parameter used for roundabout entry capacity calculation is follow-up headway. This parameter beyond critical headway and traffic volume on roundabout roadway major for vehicle drivers from entry determines roundabout entry capacity. The comparative analysis of models and studies on follow-up headway estimation have been presented in this article. The analysis were made for single lane, two lane and turbo roundabouts. The largest number of models and studies of follow-up headway parameter refers to single lane roundabouts. The least studies were devoted to values of follow-up headway parameter at turbo roundabouts due to the fact that this is still relatively a new type of roundabout in compare with single lane or two lane roundabouts.

Periodicity correlations between roadside PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration and traffic volume are difficult to explore due to the complicated process of production and diffusion of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> : a reaction of vehicle emission with atmosphere, dependence with wind speed, pavement width, vehicle speed, vehicle type, etc. We propose a framework to explore the periodicity correlation between roadside PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration and traffic volume using wavelet transform. The framework utilizes wavelet spectrum to obtain the characteristic periods of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration and traffic volume, and utilizes cross-wavelet spectrum to explore the hysteresis response of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration with respect to traffic volume. Different from traditional research work which handle pollutants and traffic data of macro scales, our study examines the micro-scale relationship of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration and traffic volume. We conduct experiments from four urban streets of Beijing, and reveal two micro-scale rules: (a) the characteristic period of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration approximates the characteristic period of traffic volume, and both of them are significantly affected by the traffic signal cycle; (b) PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration exhibits a time delay within 0.3-0.9 minutes with respect to traffic volume. The research will benefit the further study on the evolution of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> concentration and traffic volume, and provide a reference for the mitigation of PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> pollution by traffic control as well as practical traffic pollutant regulations in metropolises.

In Latin America and elsewhere, large-scale infrastructure projects are routinely justified in terms of economic development and criticized for their ecological and social consequences. Debates over the wisdom of infrastructure thus focus on indirect impacts and often overlook direct impacts such as traffic volumes. We take up the case of the Inter-Oceanic Highway, a major infrastructure project in the Southwestern Amazon under the Initiative for the Integration of Regional Infrastructure in South America. We report traffic volumes of various types of vehicles among several road segments during and after paving, between 2006 and 2019. The analysis involves statistical models of traffic volumes featuring the effect of year of observation, controlling for a suite of other indicators of the conditions under which traffic was observed. The results show modest increases in traffic volumes over time, which confirms a positive effect of paving. This raises questions about the cost-effectiveness of such projects in promoting regional development in Latin America, despite the ecological and social consequences.

Traffic Modeling and Safety Analysis on Motorway Ramps

It is well known from past studies that inclement winter weather conditions such as heavy snowfall and harsh temperature affect the variation of traffic volume. In order to quantify the change in traffic volume caused by winter weather conditions, a winter weather model explaining the relationship between weather conditions and traffic volume has been generally developed. However, none of the studies in the past have evaluated the performance of the developed model in terms of temporal transferability. In addition, there is no research showing that model specification for predicting correct traffic under winter weather conditions can be optimized for different types of vehicles. This paper presents an application of dummy-variable regression to develop winter weather traffic models. In an effort to fine-tune winter weather model specification by the vehicle class, two model validation tests were conducted. First, a temporal transferability test is conducted to determine whether the model developed is suitable to be used regardless of time. Second, a model specification test is conducted to determine whether the winter weather model specification initially structured is the most appropriate format or not. To pursue this, 6 years of traffic and weather data sets from weigh-in-motion (WIM) and weather stations were collected at Highway 2A in Alberta, Canada. The study results indicate that a dummy-variable winter weather traffic model that includes a temperature variable shows good performance in terms of accuracy and temporal transferability for passenger cars. In contrast, the naïve winter weather model specification that excludes a temperature variable is preferable in predicting truck traffic volume. It is very interesting to note that even if vehicles are traveling in the same traffic stream under the same environmental conditions, different model specifications are preferable for different vehicle types to predict more accurate traffic in winter season.

&lt;p&gt;Supplementary Table S1. Analyses of MSI, DNA methylation, KRAS, BRAF, and PIK3CA mutations, and neoantigen load. Supplementary Table S2. List of antibodies and fluorophores used in the immunofluorescence procedure. Supplementary Table S3. Statistical methods. Supplementary Table S4. Macrophage density, M1-like macrophage density, M2-like macrophage density, and M1:M2 macrophage density ratio in overall tissue regions and patient survival with inverse probability weighting (IPW). Supplementary Table S5. Densities of macrophage populations defined by positivity for single polarization marker in tumor intraepithelial and stromal regions and patient survival with inverse probability weighting (IPW). Supplementary Table S6. Clinical, pathological, and molecular characteristics of colorectal cancer cases according to year of diagnosis. Supplementary Table S7. Macrophage densities and M1:M2 macrophage density ratio in tumor intraepithelial and stromal regions and patient survival in strata of MSI status with inverse probability weighting (IPW). Supplementary Table S8. Macrophage densities and M1:M2 macrophage density ratio in tumor intraepithelial and stromal regions and patient survival in strata of year of diagnosis with inverse probability weighting (IPW). Figure S1. Comparison of staining patterns between multiplex immunofluorescence and standard immunohistochemistry. Figure S2. Flowchart of the cyclic immunofluorescence procedure. Figure S3. Analysis of immunofluorescence slides to quantify tumor intraepithelial and stromal macrophage densities. Figure S4. Macrophage densities across 10 TMAs. Figure S5. Fluorescence signal intensities across 10 TMAs. Figure S6. t-SNE analysis of a random sample of 0.5% of all cells based on fluorophore signal intensities shows no clear clustering according to the TMAs. Figure S7. Core-to-core correlation of macrophage densities in two randomly chosen cores of tumors with two or more cores. Figure S8. Inverse probability weighting-adjusted Kaplan-Meier survival curves of colorectal cancer survival according to the ordinal quartile categories (Q1-Q4) of intraepithelial (A) and stromal (B) macrophage densities. Figure S9. Forest plots of inverse probability weighting-adjusted Cox regression models of colorectal cancer specific survival according to the densities of intraepithelial and stromal M1-like polarized macrophages, with M1-like macrophages defined using different cut-offs (10-50%) of the M1-end of the macrophage polarization (M1:M2) index distribution. Figure S10. Forest plots of inverse probability weighting-adjusted Cox regression models of colorectal cancer specific survival according to the densities of intraepithelial and stromal M2-like polarized macrophages, with M2-like macrophages defined using different cut-offs (10-50%) of the M2-end of the macrophage polarization (M1:M2) index distribution. Figure S11. Forest plots of inverse probability weighting-adjusted Cox regression models of colorectal cancer specific survival according to the intraepithelial and stromal M1:M2-density ratio with M1-like and M2-like macrophages defined using different cut-offs (10-50%) of both tails of the macrophage polarization (M1:M2) index distribution. Figure S12. Densities of M1-like and M2-like macrophages in relation to histologic lymphocytic reaction patterns.&lt;/p&gt;

Traffic safety is of great significance, especially at urban expressway where traffic volume is large and traffic conflicts are highlighted. But little research up to date has discussed in detail how these factors impact the TTC characteristics. In this paper, field Beijing expressway data were collected by video with different locations, lanes, traffic conditions and following vehicle types. Accordingly, some basic descriptive statistics of total TTC samples were shown and analyzed. We then used T-test to analyze the effect of road environments, traffic conditions, and vehicle types on TTC statistically. The results implied three main findings. Firstly, TTC was found to change according to road environments (i.e. TTC on weaving segment is smaller than other locations), secondly, some evidence supported this hypothesis that traffic conditions (especially uncongested traffic condition) affect TTC significantly, and lastly, little correlation was found between TTC means and vehicle types.

Traffic information is an important tool in the planning, maintenance, and control of any modern transport system. Of special interest to traffic engineers are parameters of traffic flow such as volume, speed, type of vehicle, queue parameters, traffic movements at junctions, etc. Various algorithms, mainly based on background differencing techniques, have been applied for this purpose. Since background-based algorithms are very sensitive to ambient lighting conditions, they have not yielded the expectative results. In this paper, we describe a novel approach to measure traffic parameters. This approach is based on applying edge-detection techniques to the key regions or windows. This method of measuring road traffic parameters eliminates the need of a background frame, which is an essential, but unreliable technique for background-based image-detection methods. A dynamic threshold selection technique has also been introduced to select the threshold value automatically. The image process algorithm has been applied to measure basic traffic parameters such a traffic volume, types of vehicles, as well as the complex traffic parameters such as queue parameters and movements of vehicles at a traffic junction.

S ome P oints of D iscussion • Why hasn't the PIG model been widely used before this? • What types of data are best modeled using a PIG regression? • How do we model data with a very high initial peak and long right skew? • How do we know whether a PIG is a better-fitted model than negative binomial or Poisson models? P oisson I nverse G aussian M odel A ssumptions The Poisson inverse Gaussian (PIG) model is similar to the negative binomial model in that both are mixture models. The negative binomial model is a mixture of Poisson and gamma distributions , whereas the inverse Gaussian model is a mixture of Poisson and inverse Gaussian distributions . Those of you who are familiar with generalized linear models will notice that there are three GLM continuous distributions: normal, gamma, and inverse Gaussian. The normal distribution is typically parameterized to a lognormal distribution when associated with count models, presumably because the log link forces the distribution to have only nonnegative values. The Poisson and negative binomial (both NB2 and NB1) models have log links. Recall that the negative binomial is a mixture of the Poisson and gamma distributions, with variances of μ and μ 2 / v , respectively. We inverted v so that there is a direct relationship between the mean, dispersion, and variance function. Likewise, the inverse Gaussian is a mixture of Poisson and inverse Gaussian distributions, with an inverse Gaussian variance of μ 3 Φ.

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model’s potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.