Toward Accident Prevention Through Machine Learning Analysis of Accident Reports

Occupational safety remains of interest in the construction sector. The frequency of accidents has decreased in Sweden but only to a level that remains constant over the last ten years. Although Sweden shows to be performing better in comparison to other European countries, the construction industry continues to contribute to a fifth of fatal accidents in Europe. The latter situation pushes towards the need for reducing the frequency and fatalities of occupational accident occurrences in the construction sector. In the Swedish context, several initiatives have been established for prevention and accident frequency reduction. However, risk analysis models and causal links have been found to be rare in this context.The continuous reporting of accidents and near-misses creates large datasets with potentially useful information about accidents and their causes. In addition to that, there has been an increased research interest in analysing this data through machine learning (ML). The state-of-art research efforts include applying ML to analyse the textual data within the accumulated accident reports, identifying contributing factors, and extracting accident information. However, solutions that are created by ML models can lead to changes for a company and the industry. ML modelling includes a prototype development that is accompanied by the industry’s and domain experts’ requirements. The aim of this thesis is to investigate how ML based methods and techniques could be used to develop a research-based prototype for occupational accident prevention in a contracting company. The thesis focus is on the exploration of a development processes that bridges ML data analysis technical part with the context of safety in a contracting company. The thesis builds on accident causation models (ACMs) and ML methods, utilising the Cross Industry Standard Process Development Method (CRISP-DM) as a method. These were employed to interpret and understand the empirical material of accident reports and interviews within the health and safety (H&S) unit.The results of the thesis showed that analysing accident reports via ML can lead to the discovery of knowledge about accidents. However, there were several challenges that were found to hinder the extraction of knowledge and the application of ML. The identified challenges mainly related to the standardization of the development process and, the feasibility of implementation and evaluation. Moreover, the tendency of the ML-related literature to focus on predicting severity was found not compatible either with the function of ML analysis or the findings of accident causation literature which considers severity as a stochastic element. The analysis further concluded that ACMs seemed to have reached a mature stage, where a new approach is needed to understand the rules that govern the relationships between emergent new risks – rather than the systemization of risks themselves. The analysis of accident reports by ML needs further research in systemized methods for such analysis in the domain of construction and in the context of contracting companies – as only few research efforts have focused on this area regarding ML evaluation metrics and data pre-processing

Criteria for Removing Traffic Signals, Technical Report

DOT-FH-11-9524The report presents the results of a study to develop criteria that may be adopted as warrants for the removal of existing traffic control signals. The development of the signal removal criteria was based largely, as in a legal argument, on precedent. Those cases where positive impacts were realized by removing signals served to identify the criteria and conditions under which other signals should be removed. Likewise, cases involving negative impacts or unsuccessful removal attempts were reviewed to identify those conditions where signal removal should not be pursued. The methodology employed in this research was to compile the traffic signal removal experiences at over 200 intersections in 31 political entities, and to summarize and analyze this information to provide an objective base for the development of signal removal criteria. The decision process is designed to allow the traffic engineer to predict the expected impacts that will result from the removal of a traffic signal at a particular intersection. Knowing these probable impacts on intersection safety, traffic flow, energy consumption and costs, the traffic engineer can then make a sound decision concerning the removal of a signal. This volume documents the details of the signal removal criteria. A User's Guide is presented in another volume