Traffic accidents are a major concern worldwide, since they have a significant impact on people’s safety, health, and well-being, and thus, they constitute an important field of research on the use of state-of-the-art techniques and algorithms to analyze and predict them. The study of traffic accidents has been conducted using the information published by traffic entities and road police forces, but thanks to the ubiquity and availability of social media platforms, it is possible to have detailed and real-time information about road accidents in a given region, which allows for detailed studies that include unrecorded road accident events. The focus of this paper is to propose a model to predict traffic accidents using information gathered from social media and open data, applying an ensemble Deep Learning Model, composed of Gated Recurrent Units and Convolutional Neural Networks. The results obtained are compared with baseline algorithms and results published by other researchers. The results show promising outcomes, indicating that in the context of the problem, the proposed ensemble Deep Learning model outperforms the baseline algorithms and other Deep Learning models reported by literature. The information provided by the model can be valuable for traffic control agencies to plan road accident prevention activities.
The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace.
Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.
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