| 基于XGBoost的短时出租车速度预测模型 |
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| 引用本文： | 肖宇,赵建有,叱干都,刘清云.基于XGBoost的短时出租车速度预测模型[J].交通信息与安全,2022,40(3):163-170. |
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| 作者姓名： | 肖宇 赵建有 叱干都 刘清云 |
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| 作者单位： | 1.长安大学运输工程学院 西安 710064 |
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| 基金项目： | 国家重点研发计划项目2020YFB1600400 |
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| 摘 要： | 准确预测短时出租车速度是识别驾驶员异常加减速行为的前提，有助于提升乘客的安全与舒适。以城市中出租车实时移动速度为研究对象，研究了基于XGBoost的短时出租车速度预测模型。将出租车的移动速度数据集划分为训练集和测试集，构造滑动时间窗口，以时间窗口内的出租车历史移动速度的时间序列为输入变量，以出租车当前时间的移动速度为输出变量，采用前向验证的方法进行模型评估。利用基于贝叶斯算法的hyperopt模块实现模型参数的快速优化，得到模型最优参数组合，并基于深圳市2013年10月22日的出租车GPS轨迹数据集进行算例分析，将模型的预测结果与非参数回归模型、神经网络模型预测结果进行比较。研究表明:所构建的短时出租车速度预测模型的平均绝对误差（MAE）为9.841，均方根误差（RMSE）为12.711，均低于非参数回归模型和神经网络模型，提高了出租车速度的预测精度；由于出租车速度序列缺乏规律性，调整后的R2（R2 _adjusted）为0.592，且相较于其他2个模型，XGBoost模型在出租车速度发生急剧变化的时间点附近具有更优的拟合效果，避免了过拟合造成的预测精度下降。
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| 关 键 词： | 城市交通 出租车速度 短时预测 XGBoost |
| 收稿时间： | 2022-01-14 |
A Short-term Prediction Model for Taxi Speed Based on XGBoost |
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| Affiliation: | 1.College of Transportation Engineering, Chang'an University, Xi'an 710064, China2.School of Automobile, Chang'an University, Xi'an 710064, China |
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| Abstract: | An accurate short-term prediction for taxi speed is the premise of identifying abnormal driving behaviors of acceleration and deceleration in advance, which helps to enhance passengers'comfort and safety. A short-term prediction model is proposed to forecast real-time speed of taxis with an Extreme Gradient Boosting(XGBoost) model. The dataset of taxi speeds is divided into a training set and a test set, where a sequence of historical speed data in a time window are taken as an input variable, and the current speed data is taken as an output variable. The accuracy of the model is evaluated by a method called walk-forward validation. Based on the Bayesian algorithm, a hyperopt module is used to optimize model parameters, and a combination of optimal parameters can be obtained in a timely fashion. Experiments are carried out based on a data set of taxi GPS trajectory, which was collected in the City of Shenzhen on October 22, 2013, and the results of the proposed model are compared with those of two other models, including a non-parametric regression model and a neural network model. The results shows that the mean absolute error(MAE)and the root mean square error(RMSE)of the proposed model is 9.841 and 12.711. respectively. Due to the lack of regularity in the taxi speed sequence, the corrected R2(R2 _adjusted)is 0.592, which outperform those of the non-parametric regression model and the neural network model. Besides, compared with the two other models, the proposed model has a better goodness of fit under the scenario that a taxi suddenly changes its speed in a significant way, which can be used to avoid degraded accuracy due to model overfitting. |
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