Research of Application Guidelines for Speed Pads in Local Streets
Date Issued
2005
Date
2005
Author(s)
Lin, Che-Li
DOI
zh-TW
Abstract
In recent years, more and more traffic calming projects are implemented to improve the poor traffic condition of local roads. Among various kind of traffic calming measurements, speed pad in the most in common use. But there are nearly none studies about developing a method for establishing application guidelines for installing speed pads local streets. On the other hand, motorcycle which having a high ratio in the transportation system in Taiwan is not a main transportation mode in overseas countries and very few studies were focused in motorcycle traffic. Based on this understanding, this study tries to distinguish the motorcycle traffic from automobile traffic by building separately risk estimation model and speed prediction model.
By using the Fuzzy Theory, real pedestrian experience can be reflected form very threatened to very safe as the risk estimation model. Questionnaires are used in this study for data collection in the model-building process. Hence, we can tell from the model in what speed of which transportation mode is too much risk for a local road. Therefore, speed pads must be implemented once the level of traffic safety is unacceptable.
With the use of Neural Network method in building the speed prediction model, vehicle speed will be predicted according to several variables such as geometric conditions of a local road, the position of the vehicle in the road, types of traffic calming measures within the road, and the traffic or pedestrian flow within the local road. The whole model-building process with parameter calibration, model verification and model validation has been completed in this study.
Combining the two models, a complete method for estimating the local roads’ safety level and the effectiveness of implemented traffic calming measures is accomplished.
Subjects
減速墊
設置準則
模糊理論
風險評估模式
類神經網路
車速預測模式
Speed pad
application guidelines
Fuzzy Theory
risk estimation model
Neural Networks
speed prediction model
Type
thesis