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In recent years, the demand for road traffic has continued to increase, but the casualties and economic losses caused by traffic accidents have also remained high. Therefore, the use of social service robots to manage, supervise and warn real-time traffic information has become an inevitable trend of traffic safety management.

In order to explore the inherent objective development law of road traffic accidents, in this paper, the factor analysis (FA) is used to explore the main influencing factors of traffic accidents, then the random forest algorithm is applied to build an FA–RF-based road traffic accident severity prediction model to predict two- and three-category accidents.

By comprehensively comparing the classification results of the two- and the three-category accident prediction, it also finds that due to the intersection between injuries and fatalities and the lack of necessarily external environmental information, the FA–RF model has a large degree of misjudgment for injuries and fatalities. Therefore, it is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

(1) A fusion model of FA–RF is considered to predict traffic accidents, which can be applied in traffic service robot. (2) It is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

The aim of this paper is to review the structure of road accident data, the database framework within which it is stored, and the potential for exploiting a hierarchical structure using multilevel statistical models. Most national accident databases regard accidents as the primary units of observation, with other characteristics stored as attributes. But it is more natural to picture the network, accidents, and other variables as a collection of related objects within a hierarchical system, which can be achieved using object-oriented database technology within a Geographical Information System (GIS) framework. This would permit more efficient data capture and storage, facilitate analysis of accident frequencies as a function of road layout, and facilitate the development of multilevel statistical models.

The purpose of this paper is to propose a framework methodology for the assessment and improving accident data collection and analysis in the Kingdom of Saudi Arabia (KSA).

A proposed framework methodology for accident reduction by an integrated process which includes a risk identification, assessment and reduction process is proposed for Tabuk city in KSA.

Tabuk city has a high accident rate. The proposed framework can improve the process of accident data collection and analysis and identify hot spot locations and accident severity.

It has been estimated that the annual cost of traffic accidents in Saudi Arabia exceeds six billion US dollars, in addition to other social, health and economic impacts, such as disability, rehabilitation and unemployment due to traffic accident injuries. There are not many studies, if any, in Tabuk in KSA on the investigation and development of a framework for accident reduction.