Big data analytics and visualization with spatio-temporal correlations for traffic accidents

Big data analytics for traffic accidents is a hot topic and has significant values for a smart and safe traffic in the city. Based on the massive traffic accident data from October 2014 to March 2015 in Xiamen, China, we propose a novel accident occurrences analytics method in both spatial and temporal dimensions to predict when and where an accident with a specific crash type will occur consequentially by whom. Firstly, we analyze and visualize accident occurrences in both temporal and spatial view. Second, we illustrate spatio-temporal visualization results through two case studies in multiple road segments, and the impact of weather on crash types. These findings of accident occurrences analysis and visualization would not only help traffic police department implement instant personnel assignments among simultaneous accidents, but also inform individual drivers about accident-prone sections and the time span which requires their most attention.

Accident avoidance importance for building demolition

Building demolition, as compared to building construction is always carried out as quickly and cheaply as possible. The nature of limited time and resources of the demolition project sometimes translate into poor work planning and safety precautions. In recent years, demolition work has become more complicated due to the high diversity of building types and there are various demolition techniques and strategies. It is important to have a clear understanding of the type of building to be demolished, the method to be used and risks involved to ensure proper work planning. Using historical data on demolition related accidents; this paper discusses the classification of injuries and causes of the accidents. To conclude, strategies for better understanding of demolition work and good practices of site safety are recommended.