| Summary: | 碩士 === 朝陽科技大學 === 資訊與通訊系 === 106 === Intelligent transport system is one of the indispensable systems in smart cities. Intelligent transport system is the key to a smooth operation of the city. Many countries have invested a lot of resources in developing intelligent transportation systems. However, Taiwan’s intelligent transport system is still in the preliminary stage. There is no set of feasible mechanisms for how to apply the information returned by the device.
This paper proposes a systematic approach to traffic assessment that can be analyzed and judged based on the data collection by the vehicle detector. Base on traffic flow, the relationship between road junctions can be analyzed. Utilizing these analysis results, it is possible to find the better cycle of traffic lights. This paper uses cluster analysis as method of data preprocessing. Cluster analysis can effectively group data based on the relationship between data that can improve the quality of data, and effectively reduce the time consuming of analysis. This paper will divide the city of Taipei into multiple small areas according to the collected data, and analyze the traffic flow patterns of these areas. The result of the analysis will as a consideration for the configuration of traffic lights. The experiment result shows that after more comprehensive considerations, it is possible to reduce the vehicle delay rate at the intersection and reduce the degree of traffic congestion. In addition, through deep learning as a classification model, it is possible to effectively classify intersection congestion levels with simple data without the need for tedious operations.
|
|---|
