Occupational Road Safety Management: A Preliminary Insight for a Landfill Remediation Site

Abstract

Road crashes have been internationally recognized as one of the main causes of death. On one hand, in Europe, many governments are struggling with the ambitious target of zero road deaths by 2050. On the other hand, they are facing remediation of illegal waste dumps, subject to European infringement procedures and involving a lot of workers and heavy materials transportation. With the aim to further explore occupational health and safety (OHS) issues related to the remediation of such contaminated sites close to urban areas, we decided to focus our attention on road crashes involving people while working in the transport of materials and goods (i.e., occupational road safety). In the scientific literature, it is considered an emerging matter of concern, but no significant contribution nor specific procedures have been provided in this research field for workers in charge of contaminated sites. With the aim to fill such a gap, we decided to, first, investigate the impacts of a landfill remediation site (Malagrotta landfill, near Rome—Italy) on road safety in the surrounding context. Then, road safety management measures for workers driving heavy vehicles from and toward the reference site were suggested through the means of cluster analysis. The main road accident determinants (road safety signs and traffic conditions) for heavy vehicles in the Rome municipality, derived from a sample of 166 events, occurred in the period 2017–2021 on target road infrastructures for the case study. The events were finally grouped with a k-means three-centroid solution. Overall, despite the intrinsic limits related to the data’s details, this paper provides a specific and data-driven methodology to address occupational road safety near a landfill remediation site and encourages further research in this field.

Using Citizen Science to Manage Odour Emissions in National IED Plants: A Systematic Review of the Scientific Literature

“…Hence, after a proper organization of the answers, they could be elaborated through data mining techniques [81]. Considering the type of data, clustering or natural language processing (NLP) techniques could be applied, as already reported in scientific literature for other purposes (e.g., the sentiment analysis of perceptions about cultural heritage [82], occupational risk assessment [83,84]).…”