Review and ranking of crash risk factors related to the road infrastructure

Abstract

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as "hot topics" of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings-and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 'Synopses' (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors).

A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety

“…total duration, total distance driven per trip, driving duration, risky hours driving per trip, harsh accelerations per 100 km, harsh braking per 100 km, speeding duration/driving duration, average speeding, average total speed, average driving speed and mobile phone usage duration/driving duration) are captured through a specially developed smartphone application provided by OSeven Telematics and transmitted to a back-end platform. Taking into account that recently published studies ( Papadimitriou et al, 2019 ; Stavrakaki et al, 2020 ; Tselentis et al, 2019 ) have used the aforementioned indicators in order to estimate driving behavior, these can form a representative set of indicators to reflect the effect of COVID-19.…”

Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making

“…In case of countries, exposure was measured as passenger kilometers traveled, population and number of registered vehicles [14,15] but in case of highways, it has been considered as volume/capacity, vehicle miles traveled and vehicles hours traveled [94]. Some have explained exposure as traffic flow and traffic composition [54]. However, road safety outcome indicates the number of accidents, number of fatalities and injuries, and property damages [98].…”

“…Motorways and highways remained a major target of analysis of safety improvements for safety scientists. Researchers [45][46][47][48][49][50][51] not only explored the road accidents model development for prediction and analysis, but also discussed the low and higher cost treatments to solve the safety problems [52][53][54][55][56]. This study focuses on the identification of accident-prone segments of roads for safety analysis.…”

Driving simulator for evaluating the effects of road geometric design on driver behavior

“…A traditional approach in road safety is to identify risk factors that contribute to the frequency or severity of crashes (De Oña, López, Mujalli, & Calvo, 2013;Dingus et al, 2016;Gibson, Heaton, & Tass, 2018;Papadimitriou et al, 2019;Papadimitriou & Theofilatos, 2017;Zhang, Yau, & Chen, 2013). Many distinct agents play a role in crash occurrence and crash outcome, simultaneously.…”

“…Among the road environment causes, road geometric design deficiencies represent a risk for crash occurrence. In the analysis made within the Safety Cube Project there were identified 59 specific risk factors related to road environment that potentially play a role in accidents frequency and severity (Papadimitriou et al, 2019). Between them, low curve radius was considered the riskiest deficiency related to road alignment.…”