Historical road crash data are the main indicator for measuring road safety outcomes. Over the past few decades, significant efforts have been made in obtaining and exploiting Surrogate Safety Measures (SSMs). SSMs have the potential to provide excellent sustainable road safety indicators and proxy measurements which can complement traditional historical crash analyses or even substitute them. By using SSMs, crash data collection demands can be bypassed and areas can be investigated before crashes occur. Due to such advantages, the objective of the present research is to provide a review of the scientific literature regarding studies exploiting SSMs for historical crash record investigations. Specifically, 34 studies were examined, providing insights on the different types of SSMs collected under real road environment conditions, the way they are collected, their connection with specific road crash types, and the type of the developed statistical models are examined and discussed. Particular focus is also placed on the temporal dimension of the collection period of both SSMs and road crashes. Finally, the overall trends deriving from the reviewed studies are summarized and future research directions are provided.

Objective: More and more pedestrians use mobile phones in their daily traffic activities by the roadside or even when crossing the street. The objective of this research is to examine pedestrians’ traffic and safety behavior while texting or web-surfing, when crossing signalized intersections. Methods: In order to compare the behavior of distracted and non-distracted pedestrians, an experimental process through video recording was carried out in real road conditions, in three signalized intersections in the center of Athens in Greece. Demographic and behavioral characteristics were observed, including use of mobile device. For the statistical analysis, two multiple linear regression models were developed to investigate the association of pedestrians’ speed and distraction caused by mobile phone use. Additionally, binary logistic regression models were developed in order to determine the influence of distraction on pedestrians’ safety characteristics and more specifically on near misses with oncoming vehicles. Results: Observers recorded crossing behaviors for 2,280 pedestrians and noticed that nearly one-fifth (16.6%) of them performed a phone-distracting activity while crossing. Distractions included texting or web-surfing (6.3%), listening to music (5.4%) and using a handheld phone (4.9%). Τhis research indicated that distraction caused by texting or web-surfing had a negative impact on pedestrians’ main traffic and safety characteristics. Results pointed out that in high pedestrian traffic, distracted pedestrians who were texting or web-surfing on their mobile phone present lower speed than non-distracted pedestrians, regardless of their age, as they may be not aware of traffic conditions due to distraction and therefore, they have higher crossing times. Furthermore, their probability of a near miss increases with increasing pedestrian volume as the more pedestrians who occupy the pedestrian crossing the more difficult is for them to observe carefully the rest traffic. Conclusions: Mobile phones are integral to contemporary daily life and their use and penetration is increasing rapidly as well. For this reason, it is crucial to investigate the impacts of distracted walking on pedestrians’ traffic and safety behavior. Various measures and strategies should be implemented and further research should be conducted as texting and web-surfing distraction is associated with a rather high risk.

Motorways are typically the safest road environment in terms of injury crashes per million vehicle kilometres; however, given the high severity of crashes occurring therein, there is still space for road safety improvements. The objective of this study is to compare the classification performance of five machine learning techniques for predictions of crash risk levels of motorway segments. To that end, data on crash risk levels, driving behaviour metrics, and road geometry characteristics of 668 motorway segments were exploited. The utilized dataset was divided into training and test subsets, with a proportion of 75% and 25%, respectively. The training subset was used to train the models, whereas the test subset was used for the evaluation of their performance. The response variable of the models was the crash risk level of the considered motorway segments, while the predictors were various road design characteristics and naturalistic driving behaviour metrics. The techniques considered were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Among the five techniques, the Random Forest model achieved the best classification performance (overall accuracy: 89.3%, macro-averaged precision: 89.0%, macro-averaged recall: 88.4%, macro-averaged F1 score: 88.6%). Moreover, the Shapley additive explanations were calculated in order to assist with the interpretation of the model’s outcomes. The findings of this study are particularly useful as the Random Forest model could be used as a highly promising proactive road safety tool for identifying potentially hazardous motorway segments.

High quality data on road crashes, road design characteristics, and traffic are typically required to predict crash frequency. Surrogate Safety Measures (SSMs) are an alternative category of indicators that can be used in road safety analyses in order to quantify various unsafe traffic events. The objective of this research is to exploit road geometry data and SSMs toward various road crash investigations in motorway segments. To that end, for this analysis, a database containing data on injury and property-damage-only crashes, road design characteristics, and SSMs of 668 segments was compiled and utilized. The results of the developed negative binomial regression model revealed that crash frequency is positively correlated with the average annual daily traffic volume, the length of the segment, harsh accelerations, and harsh braking. Moreover, four distinct clusters representing crash risk levels of the examined segments emerged from the hierarchical clustering procedure, ranging from more risk-prone, potentially unsafe locations to more safe locations. These four clusters also formed the response variable classes of a random forest model. This classification model used various road geometry data and SSMs as predictors and achieved high classification performance for all classes, averaging more than 88% correct classification rates.

The risk of being involved in a road crash is typically influenced by mobility, which in turn is influenced by various socioeconomic indicators. This study aims to investigate the impact of socioeconomic and transport indicators on road safety during the economic crisis period in Europe. A database containing Human Development Index (HDI), suicides, passenger-kilometers and road fatalities per population was developed. Linear Mixed Models were applied for all the examined countries and the different groups that were selected for the period 2006-2015. The results led to the conclusion that HDI has the most important impact and its increase leads to road fatalities decrease. Moreover, the evolution of human development affects the outcomes of road crashes more than suicides and passenger-kilometers travelled. After the end of the crisis, the impact of human development is even higher. Concerning passenger-kilometers travelled, there is an increase in the relative impact on road fatalities after the end of the crisis.