Aiming at the problem that the characteristic and evaluation indexes for reasearch of traffic behavior spectrum are incompleted both at home and abroad and quantitative analysis cannot be performed in the research, the corresponding characteristic and evaluation indexes were defined to establish a complete traffic behavior spectrum system with quantitative analysis of regional traffic behavior data. Firstly, based on the characteristics of traffic behavior, an improved Analytic Hierarchy Process (AHP) was used to classify the traffic order types. Secondly, Real-Time System Integration (RTSI) algorithm with multi-data fusion was used to comprehensively evaluate the traffic safety of a certain road. Finally, a traffic behavior spectrum analysis tool was developed, calculating traffic safety index of a road section according to the traffic live data, and analyzing traffic behavior in the section more completely.

Focusing on the issue that our country's driver behavior spectrum research is still not perfect, and there is no corresponding behavioral spectrum analysis tool in the professional field, a set of complete driver behavior spectrum system for commercial motor vehicle of passenger transport was proposed and an analyzing tool was designed. Firstly, the characteristic indexes and the evaluation indexes of driver behavior spectrum were designed and defined. Secondly, the characteristic indexes analysis method and algorithm of driver behavior spectrum were given, the improved K-means algorithm based on Markov chain Monte Carlo sampling and outlier removing was used to analyze driving styles of drivers, and regression learning was used to analyze driving skills of drivers. Then, the basic data acquisition scheme and preprocessing methods of driver behavior spectrum based on car networking and big data were designed and proposed. Finally, Java language and the Spring MVC (Model View Controller) architecture were used to develop the profiling tool of driver behavior spectrum. Data mining and data analysis methods in machine learning were combined with traffic safety, which has theoretical significance for perfecting the driver behavior spectrum framework. It provides a scientific and quantitative analysis tool for our country's driver behavior spectrum analysis work. It also provides guiding significance for traffic management department to standardize the driving behaviors of drivers, improves the road safety index and makes reasonable traffic safety management strategies.