Traffic conflict assessment using macroscopic traffic flow variables: A novel framework for real-time applications.

The present study develops a comprehensive traffic conflict assessment framework using macroscopic traffic state variables. To this end, vehicular trajectories extracted for a midblock section of a ten-lane divided Western Urban Expressway in India are used. A macroscopic indicator termed "time spent in conflict (TSC)" is adopted to evaluate traffic conflicts. The proportion of Stopping distance (PSD) is adopted as a suitable traffic conflict indicator. Vehicle-to-vehicle interactions in a traffic stream are two-dimensional, highlighting that the vehicles interact simultaneously in lateral and longitudinal dimensions. Therefore, a two-dimensional framework based on the influence zone of the subject vehicle is proposed and employed to evaluate TSCs. The TSCs are modeled as a function of macroscopic traffic flow variables, namely, traffic density, speed, the standard deviation in speed, and traffic composition, under a two-step modeling framework. In the first step, the TSCs are modeled using a grouped random parameter Tobit (GRP-Tobit) model. In the second step, data-driven machine learning models are employed to model TSCs. The results revealed that intermediately congested traffic flow conditions are critical for traffic safety. Furthermore, macroscopic traffic variables positively influence the value of TSC, highlighting that the TSC increases with an increase in the value of any independent variable. Among different machine learning models, the random forest (RF) model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. The developed machine learning model facilitates traffic safety monitoring in real-time.

Effect of environment and season on acute decompensated heart failure: Data from low-to middle-income country.

OBJECTIVES: The environmental effect in heart failure (HF) patients is well established. However, the data is limited from low-to middle-income countries like India. This study determined the impact of environment on acute decompensated HF (ADHF) admissions and mortality in India. METHODS: Retrospectively, the data of all HF patients admitted between April 2017 and March 2019 was accessed through electronic hospital records. Simultaneously, the environmental-related data was collected from the central pollution control board. RESULTS: The study included 4561 patients of ADHF. The peak of monthly ADHF events (admission and mortality) was observed during the chilly month (January) while the lowest rates were observed in summer months (May-June). The most significant factor correlating inversely with the monthly ADHF admission (r = -0.78, p = 0.003) and mortality (r = -0.65, p = 0.004) was the maximum air temperature, and it was found to be the independent predictor for both ADHF mortality [t = -2.78, ß = -0.84; 95%CI(-6.0 to -0.6), p = 0.021] and admission [t = -4.83, ß = -0.91; 95%CI(-19.8 to -6.9), p = 0.001]. The above correlation was better seen in the elderly subset and male gender. Humidity and the air pollution attributes did not have a significant correlation with ADHF admission or mortality. CONCLUSION: In conclusion, even in low-to middle-income country like India, a periodic effect of season was demonstrated for ADHF mortality and admission, with a peak in ADHF events noted during winter months especially in the regions having extremes of seasons. Air pollution could not affect the ADHF outcome for which further studies are needed.