Identifying primary public transit corridors using multi-source big transit data

ABSTRACT

Effective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management.     

签到位置数据的密度峰值快速搜索与聚类方法

位置签到数据蕴含了城市居民活动变化。由于客户端位置候选问题,不同的签到行为以同一候选位置签到时会产生位置重复现象。针对现有密度聚类方法在签到数据聚类上存在的问题,以快速搜索和查找密度峰值聚类算法(CFSFDP)为基础,提出了签到位置数据的密度峰值快速搜索与聚类方法。首先,引入位置重复频率来表达签到位置重复,然后,对原始签到位置数据点统计位置重复频率并重新设计数据结构,以新的空间点要素为研究对象寻找密度峰值点;最后,构建了峰值点密度簇聚类算法,在点要素集聚类过程中考虑密度连通性来保证峰值密度簇的连续与完整。试验表明,所提出的聚类方法有效避免了重复度较高的离群位置对象选为峰值并聚类的情况,并具有良好的空间适应性。所提取的密度峰值点不仅可以用来表示热区的中心,还能够反映热区的集中趋势,进而可以帮助探索热区的动态变化情况。     

利用轨迹大数据进行城市道路交叉口识别及结构提取

交叉口是城市交通路网生成、更新的重要组成部分。本文基于车辆时空轨迹大数据,提出了一种城市交叉口自动识别方法。该方法首先通过轨迹跟踪识别轨迹数据中包含的车辆转向点对;然后基于距离和角度的生长聚类方法进行转向点对的空间聚类,并采用基于局部点连通性的聚类方法识别交叉口;最后利用交叉口范围圆和转向点对提取城市各级别路网下的交叉口结构。以武汉市出租车轨迹大数据为例,对武汉市城区内189个交叉口进行了探测。试验结果表明,本文所提方法可以准确地从轨迹大数据中识别出城市交叉口及其结构。     

Geographic Patterns of Craft Breweries at the Intraurban Scale

This article examines the intraurban geography of craft breweries in ten cities across the United States. First, through an exhaustive literature review, we outline both supply- and demand-side factors that might cause craft breweries to cluster. Second, we empirically test whether these establishments tend to cluster within cities using spatial statistical techniques. Many communities are attempting to support the establishment of more craft breweries as a way to boost tourism and economic development. The findings from this article aid in this discussion by providing insights into how craft brewers locate and the factors that could influence their location decision behavior. Our findings suggest that craft brewers do in fact cluster. There are both supply and demand factors responsible. On the supply side, the collaborative environment within the industry and the artisan nature of the industry's products allows for benefits of clustering to outweigh the costs associated with this behavior. On the demand side, the emergence of “brewery districts” allows individual brewers to enjoy the reputation benefits associated with the district in terms of increased foot traffic from locals and visitors looking to sample a variety of beers.     

Generating urban road intersection models from low-frequency GPS trajectory data

Detailed real-time road data are an important prerequisite for navigation and intelligent transportation systems. As accident-prone areas, road intersections play a critical role in route guidance and traffic management. Ubiquitous trajectory data have led to a recent surge in road map reconstruction. However, it is still challenging to automatically generate detailed structural models for road intersections, especially from low-frequency trajectory data. We propose a novel three-step approach to extract the structural and semantic information of road intersections from low-frequency trajectories. The spatial coverage of road intersections is first detected based on hotspot analysis and triangulation-based point clustering. Next, an improved hierarchical trajectory clustering algorithm is designed to adaptively extract the turning modes and traffic rules of road intersections. Finally, structural models are generated via K-segment fitting and common subsequence merging. Experimental results demonstrate that the proposed method can efficiently handle low-frequency, unstable trajectory data and accurately extract the structural and semantic features of road intersections. Therefore, the proposed method provides a promising solution for enriching and updating routable road data.     

基于尺度空间的分层聚类方法及其在遥感影像分类中的应用

基于尺度空间的分层聚类方法（ＳＳＨＣ）是一种以热力学非线性动力机制为理论基础的新型聚类算法,是视觉松驰化过程的模拟。与传统基于统计方法的聚类算法相比较,ＳＳＨＣ具有样本空间可服从自由分布、通过规则可获取最优聚类中心点及类别、可在聚类过程中融合后验知识等优点。本文从聚类和热力学运动机制和视觉模拟过程出发,对ＳＳＨＣ聚类算法进行综合分析,并对如何生成聚类树的过程进行详细描述邮通过融合点的部分自由能进行     

模糊聚类分析方法在地震预报中的应用

总结了1993～1997年应用模糊聚类分析方法对甘肃省地震预报的效果,通过评价得到对河西走廊地区和南北地震带北段地区的预报能力分别为0．55和0．61,扣除随机概率的预报成功率分别为0．5和0．58。     

基于模糊推理的空间聚类方法研究

提出了基于模糊推理的空间聚类方法,给出了其实现步骤,并以实例验证了其可行性和科学性。     

一种利用邻近原则的聚类方法

根据散列式居民地中独立房的分布特点,提出了一种适合此类居民地的聚类方法。此法利用邻近原则,将符合聚类条件的独立房快速找出,并聚类在一起,并且本方法不存在过于复杂的运算,速度较快。同时,本文还分析了顾及障碍物的情况下独立房聚类的条件。     

有序聚类分析法的改进及其在水文序列突变点识别中的应用

有序聚类分析法是水文学中识别突变点的有效方法,但该法只考虑了同类之间的离差较小原则,忽略了类与类之间的离差较大原则。基于此,提出了改进的有序聚类分析法,改进法同时考虑了同类之间的离差较小和类类间的离差较大原则。将改进的有序聚类分析法应用于年平均流量序列突变点识别中,并与传统有序聚类分析法进行对比分析,研究结果表明,改进的有序聚类分析法原理明确,识别突变点更加有效。