基于机器学习的网约车拼车需求预测研究

doi:10.3969/j.issn.1674-8484.2024.05.010

摘要/Abstract

摘要：

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。

关键词: 共享出行, 拼车需求, 机器学习, 决策树, Kepler优化算法

Abstract:

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.

Key words: shared mobility, carpooling demand, machine learning, decision tree, Kepler optimization algorithm

中图分类号:

U121

王迪, 李颖, 胡宇娇, 孙昊程. 基于机器学习的网约车拼车需求预测研究[J]. 汽车安全与节能学报, 2024, 15(5): 723-731.

WANG Di, LI Ying, HU Yujiao, SUN Haocheng. Research on carpooling demand prediction study based on machine learning[J]. Journal of Automotive Safety and Energy, 2024, 15(5): 723-731.

图/表 17

参考文献 18

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字段	字段说明	示例数据
AreaNo	区域编号	1
Name	名称	Rogers Park
Crowded%	拥挤占比/ %	7.7
16+Unemployed%	16岁及以上失业人口占比/ %	8.7
25+NoHighSchoolDiploma%	25岁以上无高中文凭人口占比/ %	18.2
Under18Over64%	18岁以下64岁以上人口占比/ %	27.5
IncomeMean	人均收入/ $	23939
HardshipIndex	贫困指数/ %	39

字段	字段说明	示例数据
AreaNo	区域编号	1
Name	名称	Rogers Park
Crowded%	拥挤占比/ %	7.7
16+Unemployed%	16岁及以上失业人口占比/ %	8.7
25+NoHighSchoolDiploma%	25岁以上无高中文凭人口占比/ %	18.2
Under18Over64%	18岁以下64岁以上人口占比/ %	27.5
IncomeMean	人均收入/ $	23939
HardshipIndex	贫困指数/ %	39

字段	字段说明	示例数据
Area	上车的区域	77
Date	时间段起始时间	2019/1/26 14∶15
Trip_Seconds_Median	行驶时间中位数/ s	705
Trip_Miles_Median	行驶里程中位数	2.95
Fare_Median	订单价格中位数	6.25
Shared_Trip_Authorized_Sum	拼车订单数	5
Total_Count	总订单数	20
Shared_Trip_Authorized_Ratio	拼车率	0.25
Temperature(°F)	温度/ (°F)	2.2
Bad_weather	是否下雨或下雪	1
Humidity / %	湿度/ %	89

字段	字段说明	示例数据
Area	上车的区域	77
Date	时间段起始时间	2019/1/26 14∶15
Trip_Seconds_Median	行驶时间中位数/ s	705
Trip_Miles_Median	行驶里程中位数	2.95
Fare_Median	订单价格中位数	6.25
Shared_Trip_Authorized_Sum	拼车订单数	5
Total_Count	总订单数	20
Shared_Trip_Authorized_Ratio	拼车率	0.25
Temperature(°F)	温度/ (°F)	2.2
Bad_weather	是否下雨或下雪	1
Humidity / %	湿度/ %	89

模型	MAE	RMSE
随机森林	0.107 12	0.154 58
XGBoost	0.084 38	0.101 23
LightGBM	0.085 47	0.110 68
CatBoost	0.075 46	0.098 95