基于百度地图的降水影响驾车出行用时预报模型

doi:10.3969/j.issn.1673-503X.2025.03.013

气象与环境学报 ›› 2025, Vol. 41 ›› Issue (3): 101-107.doi: 10.3969/j.issn.1673-503X.2025.03.013

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基于百度地图的降水影响驾车出行用时预报模型

张烨方¹, 冯真祯¹, 黄慧琳², 刘冰¹

1. 福建省气象灾害防御技术中心, 福建福州 350008;
2. 福建省气候中心, 福建福州 350008

收稿日期:2023-10-25 修回日期:2024-10-17 发布日期:2025-09-29
通讯作者: 冯真祯,女,高级工程师,E-mail:fzz1219@163.com。 E-mail:fzz1219@163.com
作者简介:张烨方,男,1983年生,高级工程师,主要研究气象人工智能与影响预报,E-mail:228532148@qq.com。
基金资助:
福建省自然科学基金项目(2022J01444)和福建省科技重大专项(2024YZ040025)共同资助。

Precipitation influence driving time forecast model based on Baidu Map

ZHANG Yefang¹, FENG Zhenzhen¹, HUANG Huilin², LIU Bing¹

1. Fujian Provincial Meteorological Disaster Prevention Technology Center, Fuzhou 350008, China;
2. Fujian Provincial Climate Center, Fuzhou 350008, China

Received:2023-10-25 Revised:2024-10-17 Published:2025-09-29

摘要/Abstract

摘要： 以研究降水对驾车出行用时的影响规律及其预报模型为目的,以当前时刻路况值、降水量、历史路况变化趋势、当前驾车导航出行用时为自变量,未来1 h后驾车导航用时的增加倍率为因变量,通过网络爬虫采集福州市辖区内2021年1月至2022年12月共13 142对气象和交通数据,分析了降水对驾车出行用时的影响,采用多元线性回归法和随机森林回归法分别建立了两个预测模型,以福州市辖区2023年1—5月为例对两种模型的预测效果进行了检验。结果表明:降水量对驾车出行用时呈现非线性的正影响关系;雨强为0.0～0.7 mm·h^-1时,降水量每增加1 mm,出行用时增加0.33倍,雨强超过0.7 mm·h^-1以后,每增加1 mm降水,出行用时增加0.06倍;不同时段的出行用时增加倍率上升速度略有不同,其中上下班时段较大;无论使用预报的降水数据还是实况降水数据,随机森林比多元线性回归模型在样本训练、检验时都有更低的损失或偏差,降水预报的准确率对出行用时的预报有较大影响。

关键词: 路况, 出行用时, 影响预报, 大数据挖掘

Abstract: This article aims to study the impact of precipitation on driving travel time and its forecasting model.The current road condition value,precipitation amount,historical road condition changing trend,and current driving navigation travel time are used as independent variables,and the increase rate of driving navigation travel time in the next hour is used as the dependent variable.A total of 13 142 pairs of meteorological and traffic data from January 2021 to December 2022 in the jurisdiction of Fuzhou City were collected through web crawlers to analyze the impact of precipitation on driving travel time.Multiple linear regression and random forest regression were used to develop two forecasting models,and the prediction effects of the two models were tested using the Fuzhou City jurisdiction from January to May 2023 as an example.The results indicate that precipitation has a non-linear positive impact on driving travel time.Within the rainfall intensity range of 0.0-0.7 mm·h^-1,for every 1mm increase in rainfall,the average travel time increases by 0.33 times.Beyond 0.7 mm,for every 1mm increase in rainfall,the average travel time increases by 0.06 times.The rate of increase in travel time varies slightly across different time periods,with a higher increase during commuting hours.Whether using forecast precipitation data or actual precipitation data,the random forests have lower losses or biases in sample training and testing compared with the multiple linear regression models; and the accuracy of precipitation forecasts has a significant impact on travel time forecasting.

Key words: Road conditions, Travel time, Impact forecasting, Big data mining

中图分类号:

P426.615

张烨方, 冯真祯, 黄慧琳, 刘冰. 基于百度地图的降水影响驾车出行用时预报模型[J]. 气象与环境学报, 2025, 41(3): 101-107.

ZHANG Yefang, FENG Zhenzhen, HUANG Huilin, LIU Bing. Precipitation influence driving time forecast model based on Baidu Map[J]. Journal of Meteorology and Environment, 2025, 41(3): 101-107.

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基于百度地图的降水影响驾车出行用时预报模型

Precipitation influence driving time forecast model based on Baidu Map

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