Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Impact of data aggregation approaches on the relationships between operating speed and traffic safety

Background:Provider profiling involves comparing the performance of hospitals on indicators of quality of care. Typically, provider profiling examines the performance of hospitals on each quality indicator in isolation. Consequently, one cannot formally examine whether hospitals that have poor performance on one indicator also have poor performance on a second indicator.Methods:We used Bayesian multivariate response random effects logistic regression model to simultaneously examine variation and covariation in multiple binary indicators across hospitals. We considered 7 binary patient-level indicators of quality of care for patients presenting to hospital with a diagnosis of acute stroke. We examined between-hospital variation in these 7 indicators across 86 hospitals in Ontario, Canada.Results:The number of patients eligible for each indicator ranged from 1321 to 14 079. There were 7 pairs of indicators for which there was a strong correlation between a hospital’s performance on each of the 2 indicators. Twenty-nine of the 86 hospitals had a probability higher than 0.90 of having worse performance than average on at least 4 of the 7 indicators. Seven of the 86 of hospitals had a probability higher than 0.90 of having worse performance than average on at least 5 indicators. Fourteen of the 86 of hospitals had a probability higher than 0.50 of having worse performance than average on at least 6 indicators. No hospitals had a probability higher than 0.50 of having worse performance than average on all 7 indicators.Conclusions:These findings suggest that there are a small number of hospitals that perform poorly on at least half of the quality indicators, and that certain indicators tend to cluster together. The described methods allow for targeting quality improvement initiatives at these hospitals.

The urban expressway system plays an important role in the metropolitan transportation system. However, frequent crash occurrences have significantly influence the traffic operation and travel reliability. It is vital to understand the crash occurrence mechanisms and further improve traffic safety. Statistical models such as logistic models have been conducted to unveil the crash contributing factors. However, crash occurrence is concluded to be a result of complex interactions between various contributing factors, with the only correlation effects are not sufficient to explain crash occurrence mechanisms. Thus, in this study, we aim at analyzing the confounding impacts of the multi-crash contributing factors and their causal relationships with crash occurrence through a Structure Equation Model (SEM). Potential crash contributing factors were firstly extracted from the Shanghai urban expressway system data. Then, two Confirmatory Factor Analysis (CFA) models were performed and two latent variables were obtained through well fitted CFA analyses: Geometry complexity latent variable and traffic flow complexity latent variable. Finally, different structures of SEM were established and the best model structure was identified. The model contains two latent variables derived from CFAs, two observed variables including average speed and standard deviation of speed, and one crash occurrence indicator. The modelling results indicate that the geometry complexity negatively and traffic flow complexity positively influences the standard deviation of speed; Standard deviation of speed negatively affects the average speed; both average speed and traffic flow complexity have the positive effects on the crash occurrence. Furthermore, analyses of crash occurrence mechanism based on the modelling results were proposed at the end of this paper.

Understanding crash mechanism on urban expressways using high-resolution traffic data

Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper.

BackgroundCluster randomized trials (CRTs) are increasingly used to assess the effectiveness of interventions to improve health outcomes or prevent diseases. However, the efficiency and consistency of using different analytical methods in the analysis of binary outcome have received little attention. We described and compared various statistical approaches in the analysis of CRTs using the Community Hypertension Assessment Trial (CHAT) as an example. The CHAT study was a cluster randomized controlled trial aimed at investigating the effectiveness of pharmacy-based blood pressure clinics led by peer health educators, with feedback to family physicians (CHAT intervention) against Usual Practice model (Control), on the monitoring and management of BP among older adults.MethodsWe compared three cluster-level and six individual-level statistical analysis methods in the analysis of binary outcomes from the CHAT study. The three cluster-level analysis methods were: i) un-weighted linear regression, ii) weighted linear regression, and iii) random-effects meta-regression. The six individual level analysis methods were: i) standard logistic regression, ii) robust standard errors approach, iii) generalized estimating equations, iv) random-effects meta-analytic approach, v) random-effects logistic regression, and vi) Bayesian random-effects regression. We also investigated the robustness of the estimates after the adjustment for the cluster and individual level covariates.ResultsAmong all the statistical methods assessed, the Bayesian random-effects logistic regression method yielded the widest 95% interval estimate for the odds ratio and consequently led to the most conservative conclusion. However, the results remained robust under all methods – showing sufficient evidence in support of the hypothesis of no effect for the CHAT intervention against Usual Practice control model for management of blood pressure among seniors in primary care. The individual-level standard logistic regression is the least appropriate method in the analysis of CRTs because it ignores the correlation of the outcomes for the individuals within the same cluster.ConclusionWe used data from the CHAT trial to compare different methods for analysing data from CRTs. Using different methods to analyse CRTs provides a good approach to assess the sensitivity of the results to enhance interpretation.

Urban crashes in the signalized intersections have become a worsening road safety problem and result in unacceptably high socioeconomic toll placing a heavy burden on health and economics. Few studies have been conducted to incorporate the nongeometric attributes in generating crash prediction model for developing countries. Hence, this research investigates the effects of such attributes, that is, traffic volume, bus passenger’s activities, on-street parking, establishments nearby intersection, on the probability of crashes in urban signalized intersections of developing countries through crash prediction models (CPMs) under Empirical Bayesian (EB) framework. Mean absolute error, root mean square error, average accuracy, and statistical correlation of crash count have been utilized for the validation of the proposed models. Total road crashes are likely to be increased by around 5.3% for 10% increase in traffic volume at the intersection whereas presence of on-street parking on major roads is causing a 34.2% reduction in predicted total collision. Low bus passenger activity has been found as a relatively higher effect on crashes than the high bus passenger activity. Such findings have the potential to provide new insights in the occurrences of crashes and quantify the effects of several unique nongeometric attribute at urban intersections.

In the context of aging population and social transformation, employees also put forward higher demands on corporate culture and values, which also increases the risk of employee turnover. This paper explores the correlation between various indicators and leavers through statistical analysis and visualization of various data indicators and the distribution of leavers and non-leavers on various indicators by using whether they left the company as a classification criterion. In addition, this paper also carries out Pearson correlation analysis for each indicator and draws a correlation heat map to quantitatively explore the correlation between indicators. In order to predict whether employees will leave the company, this paper uses random forest model, support vector machine model, KNN model, plain Bayesian model and logistic regression model for training and testing. The results show that the best prediction in terms of employee turnover is the Random Forest model with a prediction accuracy of 98.8%. This was followed by the Support Vector Machine model with an accuracy of 95.1%. In addition, the KNN model also achieved an accuracy of 94.8%. Ordinary Bayesian model and logistic regression model have lower accuracy rates of 80.4% and 77.2% only. This is of great significance for enterprises to realize sustainable development, and is worthy of in-depth study and practice by enterprise managers.

PurposeThis study was conducted to evaluate colic pain as a prognostic pretreatment factor that can influence ureter stone clearance and to estimate the probability of stone-free status in shock wave lithotripsy (SWL) patients with a ureter stone.Materials and MethodsWe retrospectively reviewed the medical records of 1,418 patients who underwent their first SWL between 2005 and 2013. Among these patients, 551 had a ureter stone measuring 4–20 mm and were thus eligible for our analyses. The colic pain as the chief complaint was defined as either subjective flank pain during history taking and physical examination. Propensity-scores for established for colic pain was calculated for each patient using multivariate logistic regression based upon the following covariates: age, maximal stone length (MSL), and mean stone density (MSD). Each factor was evaluated as predictor for stone-free status by Bayesian and non-Bayesian logistic regression model.ResultsAfter propensity-score matching, 217 patients were extracted in each group from the total patient cohort. There were no statistical differences in variables used in propensity- score matching. One-session success and stone-free rate were also higher in the painful group (73.7% and 71.0%, respectively) than in the painless group (63.6% and 60.4%, respectively). In multivariate non-Bayesian and Bayesian logistic regression models, a painful stone, shorter MSL, and lower MSD were significant factors for one-session stone-free status in patients who underwent SWL.ConclusionsColic pain in patients with ureter calculi was one of the significant predicting factors including MSL and MSD for one-session stone-free status of SWL.

This paper employs classical bivariate, slab-and-spike variable selection, Bayesian semi-parametric shrinkage, and factor-augmented predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983Q1 to 2005Q4, based on in-sample estimates for 1963Q1–1982Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) slab-and-spike variable selection, and Bayesian semi-parametric shrinkage, and factor-augmented predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex post out-of-sample forecast performance of the 26 models using the relative average mean square error for one-, two-, four-, and eight-quarter-ahead forecasts and test their significance based on the McCracken (2004, J Econom 140:719–752, 2007) mean-square-error F statistic. We find that, on average, the slab-and-spike variable selection and Bayesian semi-parametric shrinkage models with 188 variables provides the best forecasts among all the models. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex ante forecast exercise from 2006Q1 to 2012Q4. The 188 variable slab-and-spike variable selection and Bayesian semi-parametric shrinkage models perform quite similarly in their accuracy of forecasting the turning points. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.

Analysis of real-time crash risk for expressway ramps using traffic, geometric, trip generation, and socio-demographic predictors

Exclusive Motorcycle Lane (EML) is defined as a roadway meant exclusively for motorcycles or can be stated as motorcyclists who are compelled by law to use it and other vehicles are prohibited by law from using it. It is physically separated from the mainstreams carriageway and is grade separated from the main carriageway at intersections, interchanges or points of conflicts. Merging is one of the parts of way that motorcycle deceleration the speed from main stream lane to enter the Exclusive Motorcycle Lane (EML) with safe. The urban expressways in Malaysia have serious problems regarding the safety, efficiency and comfort ability at merging sections of an EML because of the insufficient geometric due to limited land and the right of way design configuration condition. The increase of traffic volume and demand lead towards the degradation of motorcyclist performance while on their wheels. This research is performed in order to achieve the objectives, i.e. to obtain and analyze local empirical data, such as flow rate, headway and speed on exclusive motorcycle lane at merging section that reflects on the local traffic condition on Federal Highway Route 2, and to obtain regression modeling that relates to Flow Rate with regards to the Headway 1-3, Time Mean Speed and Length of Merging Ramp section, Lm on the merging section of EML and merging ramp section. Two regression models have successfully been performed with R-Sq 94.8 % for EML section and 31.1 % for merging ramp section. Its also highlighted the attributes of EML.

Key features of Beijing's urban expressways include a short distance between on-ramp and off-ramp, large traffic flow and high ratio of short-distance vehicles. An expressway control strategy should be based on these features. Accordingly, the authors selected three different sites on the Beijing Ring Road where the distance between on-ramp and off-ramp is about 300m. For each site they shot traffic videos on different dates and times including peak and non-peak hour sites. From the videos, they extracted traffic data, such as traffic flow, density and lane-changing behaviors. On the basis of these data, the authors analyzed the distribution characteristics of flow-density and lane-changing frequency. Finally, they compare the three sites and identify their common features. This will provide support for a future control strategy study on Beijing expressways.

Abstract. We propose a Bayesian semiparametric model for survival data with a cure fraction. We explicitly consider a finite cure time in the model, which allows us to separate the cured and the uncured populations. We take a mixture prior of a Markov gamma process and a point mass at zero to model the baseline hazard rate function of the entire population. We focus on estimating the cure threshold after which subjects are considered cured. We can incorporate covariates through a structure similar to the proportional hazards model and allow the cure threshold also to depend on the covariates. For illustration, we undertake simulation studies and a full Bayesian analysis of a bone marrow transplant data set.

In the near future, traditional or low-automation vehicles will share the roads with Connected and Autonomous Vehicles (CAVs) over many years. Yet, this complexity may impose new unknowns on the real-time crash risk evaluation. Consequently, it is important to explore crash risk analysis in such kind of mixed traffic flow environments. This paper constructed several special traffic variables in mixed traffic flow environments and proposed the kernel logistic regression (KLR) model to evaluate the crash risk in real-time. A simulated urban expressway corridor based on the North-South Elevated Road in Shanghai, China, is developed in SUMO, for the purpose of collecting the traffic safety data and traffic data (i.e., virtual detector data and Global Navigation Satellite System (GNSS) data) in mixed traffic flow environments. The prediction performance of KLR models was tested and analyzed with the simulated data, and is also compared with that of support vector machines (SVM) models. The results show that KLR has a good prediction performance like SVM. Considering KLR can provide probability estimates directly and can naturally extend to multi-class classification, priority should be given to KLR in similar problems, especially when crash risk is classified into multiple levels. The proposed KLR model is therefore recommended and has the potential to evaluate the real-time crash risk in the mixed traffic flow environment.