Abstract: With the advent of artificial intelligence 2.0 era, visual analysis methods have received more and more attention as an important human-machine coupling method. It is a powerful tool for big data analysis and a“navigator”for understanding data. It can effectively convert data in a ternary spatial structure (cyber-physical-human, CPH) into services and decision-making in a knowledge system, thereby further enhancing the intelligent level of transportation system. At the same time, a parallel intelligent transportation system that integrates artificial transportation system, computational experiment and parallel execution is proposed, which provides a new mechanism and new mode of manipulation in the field of intelligent transportation. Through the analysis of specific cases, we discuss the importance of visual analysis in the new generation of artificial intelligence, and the process transforming data or information into knowledge systems. It is proved that the seamless combination of visual analysis and parallel intelligent transportation system can better analyze large-scale traffic data, solve traffic problems more effectively, and achieve the enhancement effect of “1+1>2”. Based on this, a novel parallel intelligent transportation system framework enhanced by visual analysis is proposed. 

Key words: visual analytics, intelligent transportation system, artificial system, computational experiments, parallel execution system