Design of intelligent transportation system based on a genetic algorithm and distributed computing

With the development of society the Internet and artificial intelligence technology have been increasingly used in human life. When people use the Internet and artificial intelligence technology, the historical data generated is saved, which not only has a high scientific research value, but also has a high commercial value. By making full use of this data, the income of the enterprise can be effectively improved, the costs of the enterprise can be reduced, the competitiveness of the enterprise in the whole field can be improved, and most importantly the development potential of the enterprise can be improved. However, the biggest difficulty is that it is difficult to extract beneficial data to the development of enterprises quickly and accurately. Firstly, as the dataset is so large, and the amount of data with practical value for enterprises is scarce, there is also no rule to follow within the distribution. Therefore, it is very difficult to quickly extract the data that is beneficial to the development of enterprises. Secondly, the development of data extraction technology is not mature, and the efficiency and accuracy of data extraction are low. Current transportation big data has the characteristics of multi-source heterogeneous data, complex data types and high requirements for real-time data. Using traditional data extraction methods to extract useful traffic information from big data has been unable to keep up with the development of the times and social needs. This project aims to develop an intelligent transportation big data monitoring platform based on Ambari technology to improve the intensity of traffic supervision and the utilization of traffic data resources. ©2021 CRL Publishing Ltd.