Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network

With the help of traffic detectors widely deployed along arterial roads and intersections, real-time traffic data are collected and updated in a very short time period, which makes it possible to conduct real-time analysis at signalized intersections. Among them, real-time crash risk prediction is one of the most promising and challenging research topics. This study attempts to predict real-time crash risk by considering time series dependency with the employment of a long short-term memory recurrent neural network (LSTM-RNN) algorithm. Also, the synthetic minority over-sampling technique (SMOTE) was utilized in this study to generate a balanced training dataset for algorithm training. In comparison, a conditional logistic model was developed based on matched case control design. Both models were evaluated based on the real-world unbalanced test dataset rather than an artificially balanced dataset. The comparison results indicate that the LSTM-RNN with SMOTE outperforms the conditional logistic model. The methods and findings of this study attempt to verify the feasibility of real-time crash risk prediction by using LSTM-RNN with over-sampled dataset (SMOTE).

Cluster of Salmonella Enteritidis in Sweden 2005-2006 – suspected source: almonds

Previous outbreaks of Salmonella Enteritidis in Canada and the United States have been associated with the consumption of almonds. From December 2005 to August 2006 a cluster of 15 cases of Salmonella Enteritidis NST 3+ was reported in Sweden. A case-control study was performed to identify the source of transmission. Three controls per case were randomly selected, matched on sex, age and place of residence. Cases and controls were interviewed by telephone and data were analysed with a conditional logistic model. The results showed that eating almonds was a risk factor for infection with Salmonella Enteritidis NST3+ (unmatched odds ratio 45.0, 95% confidence interval: 4.8-421.8). No Salmonella was isolated from almonds tested in the study. In conclusion, almonds could be the source of the outbreak and should be considered when investigating outbreaks as well as sporadic cases of Salmonella Enteritidis.

Continuous Covariates in Genetic Association Studies of Case-Parent Triads: Gene and Gene-Environment Interaction Effects, Population Stratification, and Power Analysis

We propose a multinomial logistic regression method which permits estimation and likelihood ratio tests for allele effects, their interactions with continuous covariates, and assessment of the degree of population stratification in genetic association studies of case-parent triads. Our approach overcomes the constraint imposed by the categorical nature of explanatory variables in the log-linear model. We also demonstrate that the multinomial logistic method can yield efficient inference in the presence of missing parental genotype data via the use of the Expectation-Maximization (EM) algorithm. We performed simulations to compare the multinomial logistic model with the case-pseudosibling conditional logistic model approach, both of which permit the incorporation of continuous covariates. Simulation results indicate that the multinomial logistic model and the conditional logistic model lead to similar estimates in large samples. A simulation-based method of sample size estimation is also used to show that the two models are approximately equivalent in sample size requirements. When parental genotype data are missing, either completely at random or dependent on covariates, the use of the EM algorithm gives multinomial logistic model greater power. Since the multinomial logistic model offers the possibility of assessing the degree of population stratification in the sample and can also provide efficient inference in the presence of missing parental genotypes, the proposed model has an important application in epidemiological family-based association studies.