The formulation of expressway traffic policy needs to accurately predict the traffic volume. Based on this， LSTM machine learning model is selected to study it. Aiming at the problem of parameter determination in LSTM model, PSO algorithm is selected to improve it. At the same time, PSO algorithm is improved to solve the problem of search range, and the original static parameter value in PSO algorithm formula is changed into dynamic value that will change with the iteration times and particle position changes, so as to improve the search accuracy. Based on this, the improved PSO-LSTM model is constructed. Finally, through the calculation and analysis of an example, the working days and rest days of expressway are predicted respectively. The results show that the root mean square error of the evaluation index is increased by 12.19% and 10.97%, the average absolute error is increased by 17.06% and 15.17%, and the square absolute percentage error is increased by 24.56, respectively. The algorithm shows that the improved PSO-LSTM model plays a significant role in traffic volume forecasting，and has strong anti-interference ability. It can provide a more reliable basis for the rational formulation of policies.