| Title |
Smart City Traffic Data Analysis and Prediction Based on Weighted K-means Clustering Algorithm |
| ID_Doc |
41746 |
| Authors |
Li, L |
| Title |
Smart City Traffic Data Analysis and Prediction Based on Weighted K-means Clustering Algorithm |
| Year |
2024 |
| Published |
International Journal Of Advanced Computer Science And Applications, 15, 6 |
| DOI |
|
| Abstract |
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. |
| Author Keywords |
K-means; smart cities; traffic flow; prediction; holt; weight |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001277729000001 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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