arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1602.00994cs.SIphysics.soc-ph
收藏

面向城市路径的城市区域自动分析

Automatic City Region Analysis for Urban Routing

Kai Zhao, C Mohan Prasath, Sasu Tarkoma

摘要:

在城市中有不同的功能区域,如旅游景点,购物中心,工作场所和居住场所。不同功能区的人员流动模式不同,例如,人们通常在工作日白天上班,下班后去购物中心。本文分析了三个城市的人口流动模式,并推断了三个城市的区域功能。该分析基于罗马、旧金山和北京的三个大型出租车GPS数据集,分别包含2100万、1100万和1700万个GPS点。我们将城市区域划分为四类场所,即工作场所、娱乐场所、居住场所和其他场所。首先,我们提出了一种新的基于出租车访问量的四叉树区域划分方法。其次,我们使用关联规则来推断功能区在这三个城市根据时间的人的流动模式。第三,我们表明,这些确定的功能区域可以帮助我们在网络应用中,如城市延迟容忍网络(DTNs),更有效地提供数据。新的基于功能区域的DTNs算法在交付率方面实现了高达183%的改进。

英文摘要:

There are different functional regions in cities such as tourist attractions, shopping centers, workplaces and residential places. The human mobility patterns for different functional regions are different, e.g., people usually go to work during daytime on weekdays, and visit shopping centers after work. In this paper, we analyse urban human mobility patterns and infer the functions of the regions in three cities. The analysis is based on three large taxi GPS datasets in Rome, San Francisco and Beijing containing 21 million, 11 million and 17 million GPS points respectively. We categorized the city regions into four kinds of places, workplaces, entertainment places, residential places and other places. First, we provide a new quad-tree region division method based on the taxi visits. Second, we use the association rule to infer the functional regions in these three cities according to temporal human mobility patterns. Third, we show that these identified functional regions can help us deliver data in network applications, such as urban Delay Tolerant Networks (DTNs), more efficiently. The new functional-regions-based DTNs algorithm achieves up to 183% improvement in terms of delivery ratio.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑