Wang, H. and Yue, P., 2020. Short-term traffic flow prediction of coastal cities based on entropy weight method. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 739–743. Coconut Creek (Florida), ISSN 0749-0208.

The traditional short-term traffic flow prediction method has a high prediction error, so a short-term traffic flow prediction method based on entropy weight method is designed. First, identify the lost data, use the weighted average value of historical trend data and measured data to modify, and standardize the original data to collect the short-term traffic flow data of coastal cities. Then, statistical analysis of the original data set, using the minimum maximum standardization method, linear transformation of the original data, and extract the characteristic factors that affect the traffic flow. Finally, based on the correlation of traffic flow at urban intersections, the short-term traffic flow prediction of coastal cities is completed by using entropy weight method. In the experimental part, the prediction errors of the two methods in the period of 7:00-9:00 and 22:00-24:00 are compared. The comparison results show that the prediction errors of the short-term traffic flow prediction method based on entropy weight method in the period of 7:00-9:00 and 22:00-24:00 are small, which proves that the prediction method of this design has practical application significance.