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Abstract The safety hazard and the additional costs on transportation due to road accidents invite the necessity to minimize their impact. In this paper, we study the spatial-clustering behavior and hazard vulnerability of car accidents that occurred in Lebanon between 2015 and 2018. Assessment of spatial clustering of accidents and hot spots densities were examined using the Global G method of spatial autocorrelation and Getis–Ord G i ∗ statistics. A novel Road Hazard Index (H i) was proposed to assess hazard vulnerability of road networks and to develop a road hazard prediction model. Highlights • Analysis of accidents' types distribution is provided. • Spatial autocorrelation in the Lebanese accidents dataset and high clustering accidents areas is detected. • Hot spots variation between the summer and winter seasons is inspected. • Road hazard index is proposed to measure road segments risk analysis. [ABSTRACT FROM AUTHOR]

Abstract This manuscript analyzes the temporal properties of sequences of car accidents in Lebanon from 2015 to 2018, using data from the Lebanese Road Accident Platform (LRAP). The Allan Factor that is a well suited methodology for investigating the time-clustering in point processes, revealed that the car accidents occurred over the whole Lebanese territory are characterized by a significant scaling behavior for timescales larger than about 2 weeks, with a clear cyclic component at 1 day. The analysis performed on car accident sequences in some specific areas of Lebanon revealed the presence of cycles varying from 1 day to 2 weeks as a function of the distance from Beirut, the capital city of Lebanon. Highlights • Temporal properties of car accidents in selected Lebanese cities were analyzed. • One-day cycles characterize the whole Lebanese dataset. • Significant scaling behavior characterize the whole Lebanese and Greater Beirut dataset. • Cycles of increasing period feature cities with increasing distance from Beirut. [ABSTRACT FROM AUTHOR]