| Summary |
Pin-cheng Huang，2007.06
Feng Chia University
Causing factors of traffic contingency were divided into 4 classes, which were people, cars, roads, and the environment. According to the traffic accident type statistical data over the years, more than 90% of the traffic contingencies happen which were due to the behavior factors of the driver. And the trouble-making reason counted over the years was not noticing the state in front of the car, and the reason was always ranking at the first place. The proportion accounted for trouble-making reasons was very high, and it was the noteworthy type to study for the traffic collision of the vehicle. This study was wanted to probe the driver's characteristics, in order to understand the influence of the driving behavior of the roads at all level causes.
At the present relevant discussion of the aesthesia of the driving behavior was focused on the reflection time. When the driver was unable to make the instant brake as perceive and reflect time were insufficiently, the traffic accident was caused. The aesthesia of the driver was affected a lot by people, cars, roads and the environment. For example, the drivers of different ages were reflected different aesthesia time of the traffic incident. On the other hand, domestic drivers who did not obey traffic rules and used to change lane recklessly would have to pay more attention to any situation that would also postpone the perceive time.
According to the practical data of car-following behaviors on various domestic road areas and relative research of the reaction time of braking and dangerous response time to the vehicle driver, this study planed to utilize ANOVA and regression methods to analyze the reaction models which cooperated with four kinds of different road types in five kinds of driving behavior to find out model equations of the combination of road types and driving type. By utilizing dummy variables and multiple regression method, this study also probed into the systematic design parameter of the vehicle collision warning application and analyzed the apparent influence parameter for car-following behaviors. |
