Smart City Traffic Data Analysis and Prediction Based on Weighted K-means Clustering Algorithm

Urban traffic congestion is becoming a more serious issue as urbanization picks up speed. This study improved the conventional K-means method to create a new traffic flow prediction algorithm that can more accurately estimate the city's traffic flow. Firstly, the traditional K-means algorithm is given different weights by weighting, so as to analyze the traffic congestion in five urban areas of Chengdu by changing the weight values, and based on this, a traffic flow prediction model is further designed by combining with Holt's exponential smoothing algorithm. The findings showed that the weighted K-means method is capable of accurately identifying the patterns of traffic congestion in Chengdu's five urban regions and the prediction model combined with Holt's exponential smoothing algorithm had a better prediction performance. Under the environmental conditions of high traffic flow, when the time was close to 12:00, the designed model was able to obtain a prediction value of 9.81 pcu/h, which was consistent with the actual situation. This shows that this study not only provides new ideas and methods for traffic management in smart cities but also provides a reference value for the design of traffic prediction models.