摘要
交通拥堵是当今各大城市的普遍现象.城市学校在上下学时间段对其周边的道路产生了明显的交通压力.本文将开学后某一周的浮动车数据与道路路网进行匹配,计算出道路的平均车速和道路拥堵级别.选取具有代表性的5所北京市小学作为研究对象,根据不同学校,不同时间的各种情况对学校周边道路进行拥堵分析.采用空间统计分析中的空间自相关性分析方法研究学校对道路的空间上的影响.在时间尺度上,采用统计分析和对比分析探讨学校对其周边道路的时间上的影响.研究结果表明:1学校对道路的影响具有空间自相关特征,且呈现距离扩散的特征;2学校对道路的影响时间上体现在上下学时间段;3人数越多的学校对周边道路的影响也越大,周边道路的等级越高其缓解拥堵的能力也越高.
Traffic congestion is a common problem of big cities. Schools in the city produce a significant traffic pressure on its surrounding roads on the time of going or leaving school. In this paper, the average roau speeu anu tt, au congestion level can be calculated by using floating car data of a week's time since school started. Selecting 5 representative primary school in Beijing as research objects, the traffic congestion around schools can be analyzed by depending on a variety of situations of different schools and different time. The method of spatial auto-correlation analysis in spatial statistical analysis can be used on the research of the spatial impact of the road around schools. On the time scale, using statistical analysis and comparative analysis we can explore the temporal impact of the road around schools. The results showed that: (1) the impact of school on the road is spatial auto-correlative, and the feature is diffused by distance; (2) the impact of school on the road is reflected in the time of going and leaving school; (3) the more the number of schools, the bigger the affection on the road is, and the ability of alleviate congestion is better if the level of the road is higher.
出处
《首都师范大学学报(自然科学版)》
2015年第2期93-98,共6页
Journal of Capital Normal University:Natural Science Edition
关键词
浮动车数据
学校
交通拥堵
数据挖掘.
floating car data, school, traffic congestion, data mining.