Abstract
Road surface conditions have a direct effect on the quality of driving, which in turn affects overall traffic flow. Many studies have been conducted to accurately identify road surface conditions using diverse technologies. However, these previously proposed methods may still be insufficient to estimate actual risks along the roads because the exact road risk levels cannot be determined from only road surface damage data. The actual risk level of the road must be derived by considering both the road surface damage data as well as other factors such as speed. In this study, the road hazard index is proposed using smartphone-obtained pothole and traffic data to represent the level of risk due to road surface conditions. The relevant algorithm and its operating system are developed to produce the estimated index values that are classified into four levels of road risk. This road hazard index can assist road agencies in establishing road maintenance plans and budgets and will allow drivers to minimize the risk of accidents by adjusting their driving speeds in advance of dangerous road conditions. To demonstrate the proposed risk hazard assessment methodology, road hazards were assessed along specific test road sections based on observed pothole and historical travel speed data. It was found that the proposed methodology provides a rational method for improving traffic safety.
Highlights
With the recent shift from intelligent transport systems (ITSs) to cooperative ITSs (C-ITSs) in South Korea, the core of ITS is changing from traffic information to traffic safety information
Is article showed how positive benefits to traffic safety can be realized by considering both road surface damage and travel speed data. ese data can be used as a critical source for autonomous driving and to support future C-ITS implementations. e contribution of this study can be summarized as follows: (i) e results of this study promote the shift from using fixed roadside infrastructure to using mobile infrastructure to collect a variety of traffic information
In the past, fixed roadside infrastructure was used to collect diverse traffic information from specific points along a certain roadway that can identify hazards and accidents. e algorithm proposed in this study provides a risk level for each roadway section using pothole data detected by in-vehicle smartphone cameras and accelerometers as well as historical average travel speed data. is can potentially reduce fixed roadside infrastructure costs incurred by the public sector and enable data collection along the entire roadway with relatively low maintenance and management costs. is capability is applicable to more advanced vehicle-to-everything (V2X) technologies and has a high potential for commercialization
Summary
Us, after the PRD values are generated, they can be normalized using the above equation. Ese equations correspond to the normalized Z values in a series of intervals to define a 100-point scale [19, 20]. An interval scale ranging from 50 to 100 was established based on the converted scores. Is is because it is possible to make quick decisions about the level of road risk based on the score. E four hazard levels presented in Table 2 (i.e., caution, alert, warning, and danger) were taken from the classification scale found in the “Stage-Specific Road Emergency Response Manual (Heavy Snowfall)” currently used by the Korean government. Condition 1.645 ≤ Z ≤ Max (Z) 1.282 ≤ Z ≤ 1.645 1.038 ≤ Z ≤ 1.282 0.842 ≤ Z ≤ 1.038 0.676 ≤ Z ≤ 0.842 0.526 ≤ Z ≤ 0.676 0.387 ≤ Z ≤ 0.526 0.255 ≤ Z ≤ 0.387 −0.255 ≤ Z ≤ 0.255 −0.387 ≤ Z ≤ −0.255 −0.526 ≤ Z ≤ −0.387 −0.676 ≤ Z ≤ −0.526 −0.842 ≤ Z ≤ −0.676 −1.038 ≤ Z ≤ −0.842 −1.282 ≤ Z ≤ −1.038 −1.645 ≤ Z ≤ −1.282 Min(Z) ≤ Z ≤ −1.645
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