| 降雨天气下城市道路速度变化的差异及其影响因素 |
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| 引用本文： | 李秋萍,刘慧敏,卓莉,陶海燕,栾学晨.降雨天气下城市道路速度变化的差异及其影响因素[J].热带地理,2020,40(4):744-751. |
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| 作者姓名： | 李秋萍 刘慧敏 卓莉 陶海燕 栾学晨 |
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| 作者单位： | 1.，中山大学 地理科学与规划学院 综合地理信息研究中心，广州 510275;2.，广东省城市化与地理环境空间模拟重点实验室，广州 510275 |
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| 基金项目： | 国家自然科学基金(41971345);广东省自然科学基金(2020A1515010695) |
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| 摘 要： | 以深圳市雨量监测数据和大规模出租车GPS数据为数据源，计算城市各道路在雨天相对于晴天同时段速度的变化率。结果发现，降雨并不总是造成道路速度下降，研究区中约30%道路的速度会有不同程度的上升。运用多元线性回归模型分析降雨量、道路等级、车流量、晴天同时段的速度等影响因素在速度下降和速度上升两类道路上的作用差异。结果表明，对于速度下降的路段，降雨量、晴天同时段的速度、雨天车速的变异系数及车流量越大，道路速度越易受影响，下降幅度越大。公交站点的数量增加有助于缓解道路速度下降的幅度。道路等级不显著而道路在晴天同时段的速度显著，表明降雨天道路速度下降的程度与其在正常交通条件下的实际通行速度之间的关系更紧密。对于速度上升的路段，降雨量、雨天车速变异系数越大，道路速度上升幅度反而越大。晴天同时段的速度越低的道路，其速度有更大的上升幅度。
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| 关 键 词： | 降雨天气 城市交通 道路速度 多元回归 深圳市 |
| 收稿时间： | 2019-08-03 |
Differences in Urban Road Speed Change and Their Influencing Factors during Rainfall |
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| Qiuping Li,Huimin Liu,Li Zhuo,Haiyan Tao,Xuechen Luan.Differences in Urban Road Speed Change and Their Influencing Factors during Rainfall[J].Tropical Geography,2020,40(4):744-751. |
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| Authors: | Qiuping Li Huimin Liu Li Zhuo Haiyan Tao Xuechen Luan |
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| Institution: | 1.Center of Integrated Geographic Information Analysis, School of Geography and Planning, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;2.Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China |
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| Abstract: | Rainfall leads to significant impacts on urban road traffic, such as traffic congestion and traffic accidents, resulting in detrimental effects on traffic efficiency. Exploring the changes in road traffic efficiency and their influencing factors in rainy weather is of great significance for urban traffic planning, management, and traffic information release. Due to the complexity of urban road network and diversity of transportation demands, rainfall, on one hand, causes flooding on the road and reduces visibility, both of which reduce the driving speed; on the other hand, rainfall also reduces unnecessary trips, resulting in reduced traffic flow, which increases road speed. However, most previous studies focused on changes in the average traffic efficiency of all roads and ignored the differences among different road types. Moreover, there is a lack of related literature analyzing the influencing factors of road speed changes on rainy days. Therefore, this study uses the rainfall monitoring data and large-scale taxi GPS data of Shenzhen as the data source to analyze the differences in speed change on large-scale urban roads and their influencing factors. First, the percentage difference in road speed between sunny days and rainy days was calculated. It was found that rainfall did not always reduce road speeds: approximately 30% of the road speeds in the study area increased. Then, a multivariate linear regression model was applied to analyze the different effects of certain influencing factors, such as rainfall intensity, traffic volume, road grade, and the speed on sunny days, on two types of roads (i.e., speed decrease and speed increase). The results show that for the roads with reduced speed, the rainfall intensity, traffic volume, speed on sunny days, and coefficient of vehicle speed variation on rainy days have significant negative correlations with the speed decline rate. For example, the heavier the rainfall is, the faster the road speed drops. The number of bus stops has a positive correlation with the speed decline rate. For roads with increased speed, the rainfall intensity and coefficient of vehicle speed variation on rainy days have significant positive correlations with the speed increase rate, while the speed on sunny days is negatively related to the speed increase rate. For the two types of roads, the road grade is not a significant influencing factor. This research expands our understanding of the impact of rainfall on urban road traffic efficiency and can provide useful insights for the operation and evaluation of various road facilities in urban areas. |
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| Keywords: | rainfall weather urban traffic road speed multiple linear regression Shenzhen city |
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