Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers

Bortey, Loretta and Edwards, David J. and Roberts, Chris and Rille, Iain (2024) Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers. Buildings, 14 (11). p. 3509. ISSN 2075-5309

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Abstract

Highway traffic officers (HTOs) are often exposed to life-threatening workplace incidents while performing their duties. However, scant research has been undertaken to address these safety concerns. This research explores case study data from highway incident reports (held by National Highways, a UK government company) and employs deep neural network (DNN) in unearthing patterns which inform safety decision makers on pertinent safety challenges confronting HTOs. A mixed philosophical stance of positivism and interpretivism was adopted to synthesise the findings made. A four-phase sequential method was implemented to evaluate the validity of the research viz.: (i) architectural design; (ii) data exploration; (iii) predictive modelling; and (iv) performance evaluation. The DNN model’s predictive performance is benchmarked against three other machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes (NB). The DNN model outperformed the other three models. Findings from the data exploration also show that most work operations undertaken by HTOs have a medium risk level with night shifts posing the greatest risk challenges. Carriageways and traffic management enclosures had the highest incident occurrence. This is the first study to uncover such hidden patterns and predict risk levels using a database specifically for HTOs. This study presents evidence-based information for proactive risk management for HTOs.

Item Type:	Article
Identification Number:	10.3390/buildings14113509
Dates:	Date Event 31 October 2024 Accepted 2 November 2024 Published Online
Uncontrolled Keywords:	health and safety, artificial intelligence, data exploration, risk prevention, predictive modelling
Subjects:	CAH13 - architecture, building and planning > CAH13-01 - architecture, building and planning > CAH13-01-02 - building
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Built Environment
Depositing User:	Gemma Tonks
Date Deposited:	05 Mar 2025 10:47
Last Modified:	05 Mar 2025 10:47
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16203

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