In order to understand the current study status and development trend of traffic volume forecast, based on the VOSviewer bibliometric tool, taking the Web of Science core collection and the literatures related to traffic volume prediction studies in recent 29 years (1993—2021) in CNKI core database as the data sources, the study trends in the field of traffic volume forecasting are analyzed in terms of article age, country and region, journal source and technical topics. Taking the "traffic volume forecast and" "traffic volume forecasting" as the search topics, 592 valid literatures covering 6438 keywords are searched. Based on the scientific knowledge mapping, the literatures in the field of traffic volume prediction are sorted out and analyzed. The result shows that (1) Traffic forecasting studies have been on the rise in the past 29 years, and the number of publications in China is the highest. (2) Transportation Research Part C: Emerging Technologies is not only the journal with the largest number of literatures, but also the journal with the largest number of citations, and Journal of Highway and Transportation Research and Development is the most cited journal among domestic journals. (3) The studies in the field of traffic volume forecasting are mainly conducted from the perspectives of highway traffic demand development prediction, traffic volume prediction method model, traffic events and real-time monitoring, focusing on the topics of highway traffic volume prediction, project construction feasibility study, short-time traffic volume prediction method modelling and accuracy improvement, traffic event monitoring and traffic spatial and temporal distribution characteristics. (4) The number of foreign study literatures on the relationship between highway traffic volume and construction investment, traffic volume prediction under atypical conditions and the studies on traffic volume prediction under adverse weather conditions such as rain and snows shown a significant increase in recent years. (5) Domestic studies on the improvement of the four-stage method, feasibility study of road traffic construction projects and road tourism traffic volume prediction are relatively abundant, but mainly from the macro perspective of economy, population and tourism industry, it is necessary to further increase the studies on the influence of seasonal changes, individual travel characteristics and travel preferences on road tourism traffic volume prediction.

This paper aims to solve the problem of slow convergence rate and large error of existing intelligent algorithms in the process of optimizing support vector machine to identify risky driving behavior. Firstly, Tent mapping was used to replace the random setting of population initialization of ASO algorithm to increase the diversity and quality of atomic population. Secondly, the hybrid mechanism of dimension-by-dimension pinhole imaging reverse learning and Cauchy mutation was used to improve the diversity of preferred positions of atomic individuals and overcome the problem that ASO algorithm is easy to fall into local optimum and premature convergence. Finally, the adaptive variable spiral search strategy was introduced to improve the atomic individual position update process,so as to improve the global search ability of ASO algorithm, realize the effective balance between global search and local development, and alleviate the problem that ASO algorithm is easy to fall into local optimum and lack of convergence accuracy. Taking the vehicle trajectory data of the exit ramp of Shanghai North Cross Channel as the input, the study used the hybrid strategy to improve the ASO algorithm so as to optimize the LSSVM parameters. And it constructed the classification and identification model of the risk driving behavior of the expressway exit ramp based on IASO-LSSVM. Numerical simulation results show that the average value, standard deviation, best fitness and worst fitness of the numerical simulation results of the IASO algorithm in 12 benchmark test functions are closer to the best optimization value. Compared with ASO-LSSVM and LSSVM, the accuracy, precision, recall and F1 value of risk driving behavior classification and identification results of IASO-LSSVM model increased by 11.5~24.5, 14.1~29.0, 15.1~28.6, 14.7~31.2 percentage points respectively, and the error range was the smallest in different types of risky driving behavior identification results. The accuracy and convergence rate of IASO algorithm are better than those of ASO algorithm, and the IASO-LSSVM model can be used for accurate identification of different types of risk driving behavior, which can provide data support and theoretical basis for reasonable discrimination of vehicle driving trajectory state and formulation of early warning and prevention measures of risk driving behavior.