Bus Travel Time Prediction Based on Light Gradient Boosting Machine Algorithm

被引：0
|
作者：
Wang F.-J. [1 ]
Wang F.-J. [1 ]
Wang Y.-C. [2 ]
Bian C. [2 ]
机构：
[1] School of Marine Engineering, Zhejiang International Maritime College, Zhoushan, 316021, Zhejiang
[2] College of Civil Engineering and Architecture, Zhejiang University, Hangzhou
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2019年 / 19卷 / 02期
关键词：
Bus travel time; GPS data; LightGBM algorithm; Prediction; Urban traffic;
D O I：
10.16097/j.cnki.1009-6744.2019.02.017
中图分类号：
学科分类号：
摘要：
In the operation of urban public transport networks, the travel time between stations of a bus is affected by road and environmental conditions. This paper analyzes the bus speed characteristics, road characteristics and weather characteristics during bus operation, and a feature-based LightGBM bus travel time prediction model is established. By adjusting the relevant parameters in the LightGBM algorithm, the weights of the influencing features and factors are assigned. Then the model is trained and verified by using the 24-day bus GPS data of one bus line in Tianjin, and compared with the travel time prediction model based on historical mean and Kalman filter. The comparison results show that the LightGBM model is superior to the other two models in MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error), indicating that the LightGBM model has good stability and application prospects in bus travel time prediction. Copyright © 2019 by Science Press.
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页码：116 / 121
页数：5
相关论文
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