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日本語

Traffic safety has been recognized as a key global issue, as reflected in Target 3.6 of the 3rd Goal of SDGs, which is to reduce the number of traffic accidents by half. ISA is a traffic safety management technology that recognizes the speed limit of the road section based on the current position of the vehicle and provides speed information, over-speed warnings, mandatory speed control, and speed compliance incentives to prevent drivers from over-speeding. There has been active research on ISA since the 2000s, mainly in Europe. Most of the previous studies have measured the restrictive effects of excessive speeding by comparing the driving behavior of subjects before and after the introduction of an ISA with driving simulator experiments and field studies for various types of ISAs.

However, driving behavior varies greatly from driver to driver. In addition, the structural environment of the road is different in the field experiment for each driver. Therefore, it is important to measure the effects of various factors, such as the individual and driving environment, in order to spread the technology appropriately.

To resolve this issue, the research team has developed a method to accurately measure the effect of ISA by using a single statistical model to simultaneously estimate each driver's unique driving behavior, the effect of various external factors such as road structure, and the effect of ISA on excessive speeding.

"The foremost challenge to be solved was how to represent and demonstrate the hypothesis that ‘the difference in the driver's tendency to exceed the speed limit also affects the impact of ISA in the model. This method solves the problem by estimating a model that takes into account the correlation between the parameters that define the driver's tendency to exceed speed and those that define the effect of the ISA. Recent developments in the field of data science, such as Bayesian statistics and improvements in computer performance, have made it possible to apply this method with slightly more complex models to real-world problems." explains Associate Professor Kojiro Matsuo, leader of the research team.

Associate Professor Matsuo also says, "ISA research began as a graduation research topic by a student under my guidance. The student performed an in-depth analysis of the data obtained from an ISA field experiment conducted with our collaborator, Toyota Transportation Research Institute. As a result, it was found that in some cases, there is an association between the subjects' regular speeding tendency and the effectiveness of ISA, but in other cases, there is no association between the two. Therefore, we started to consider measuring the effect of using a statistical model instead of a simple comparative analysis before and after the introduction of ISA. As a result, we were able to represent the seemingly disparate driving behavior of different drivers in a single model and to find a law in it. It was extremely interesting work."

The research team believes that this method can be applied not only to measure the impact of ISA in reducing speeding, but also to assess the effectiveness of traffic safety management technologies in reducing the rate of dangerous driving, such as running a red light, not stopping at an intersection without traffic lights, and obstructing pedestrians from crossing at a crosswalk. We are hoping to contribute to the reduction of traffic accidents worldwide by further developing traffic safety management technologies and measuring their effectiveness.