基于机器学习的网约车拼车需求预测研究

Research on carpooling demand prediction study based on machine learning

为了提高拼车需求预测的准确性,提高网约车拼车服务效率,进一步有效缓解交通拥堵问题,该文利用时间特征提取和Kepler优化算法对传统的决策树机器学习模型进行优化,提出了一种区域拼车概率预测模型。基于芝加哥网约车拼车概率数据集进行拼车需求预测的实验,将该模型与传统决策树模型进行比较。结果表明:优化后的模型在预测精度方面优于传统决策树模型,平均绝对误差(MAE)降低了0.044,均方根误差(RMSE)降低了0.054。优化后的模型相较于传统决策树模型在预测拼车需求方面具有更高的准确性。

In order to improve the accuracy of carpooling demand prediction,thereby enhancing the efficiency of ride hailing services and effectively alleviating traffic congestion,a regional carpooling probability prediction model was proposed by optimizing the traditional decision tree machine learning model using time feature extraction and Kepler optimization algorithm.An experiment was conducted to predict carpooling demand based on the Chicago ride hailing probability dataset,and the model was compared with traditional decision tree models.The experimental results show that the optimized model outperforms traditional decision tree models in terms of prediction accuracy,with a decrease of 0.044 in mean absolute error(MAE)and 0.054 in root mean squared error(RMSE).The optimized model has higher accuracy in predicting carpooling demand compared to traditional decision tree models.