With the acceleration of urbanization, cities in the future will encounter various kinds of problems such as providing adequate living space, water and transportation. To address these issues which cities may face in the future, the concepts of Intelligent Transportation System (ITS) and Smart City come into being. Since both two areas aim to address possible urban issues in the future, it’s essential to study the relationship between ITS and Smart city. In this article, researchers will initially introduce serval reasons to conduct the study. Later, in the main body part, 3 possible roles are presented to illustrate the possible relationship between ITS and Smart City through reviewing other researchers’ articles. Finally, this article will give serval evaluations and suggestions about those 3 roles above. The whole article aims to state that ITS is an essential part in the future development of smart city, though it is not mature now and may encounter many problems in the process of development.
Nowadays, the data generated by expressway operation is large in scale and various in types. When analyzing expressway safety risk, the traditional methods are easily subject to the subjective limitations of analysts, as well as the limitations of experience or knowledge, making it impossible to accurately predict traffic risk, and the traditional attribution theory model cannot simultaneously process and analyze multiple heterogeneous data. The data warehouse and data mining technology based on big data drive can analyze the operation data of different ranges and regions in a unified way, mine the spatio-temporal distribution characteristics, improve the scientific utilization efficiency of traffic data resources, and improve the information service level for traffic safety and early warning. This paper starts with the characteristics of road traffic accident information collection data and the key problems of data analysis and application in China point, reduce the occurrence of road traffic accidents through multi angle and all-round grey clustering evaluation method analysis.
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