面向地理路网的交通信号智能协同控制方法

城市交通运行效率是影响城市生产力发展的重要因素之一,也是智慧城市建设过程中的重要研究课题.随着计算机技术的发展,人工智能特别是强化学习在交通信号控制中发挥重要作用.目前,基于强化学习的交通信号控制主要针对单路口或城市干道进行优化,面向城市地理路网区域协调控制研究较少.本文结合马尔可夫序列决策,提出一种基于强化学习的双层智能体协同控制方法.第1层,针对单个路口实现粗调训练,智能体通过观察路口每一车道的排队长度调控信号配时,实现单个路口不堵塞;第2层,将多个粗调训练后的智能体模型放入地理网络中,实现多路口的协同微调训练.本文以宁波某中学片区的交通协调为优化目标展开试验.结果表明,调控方法与原有固定配时方案相比,具有更高的通行效率.     

道路网约束下的城市兴趣点选取

针对道路对城市内部兴趣点数据分布有密切的约束关系,故城市内POI数据往往呈现出沿着道路分布的特征这一现象,提出一种在道路网约束下城市内部POI数据的选取算法。通过建立道路网的拓扑结构,根据POI数据的分布位置分为道路周边分布的和远离道路分布的两大类,对道路周边的点使用"间苗法"选取,对远离道路分布的点根据其分布模式决定采用"随机法"或其他方法选取。算法采用实际数据进行实验,切实可行,并得到了合理的结果。     

基于对偶拓扑结构的路网路段重要性评估方法

现有的路网路段重要性评估方法考虑的是路网中的路段的统计特性或路网的局部结构对重要性的影响。在路段的重要性与路网的全体路段相关联的基础上,提出m阶邻居节点的复杂路网路段重要度评估方法。为验证算法的有效性,实验仿真采用成都市路网的对偶拓扑结构,在1 484个路段中提取10条关键路径对评估方法进行验证。评估结果显示:与度值法、介数法相比,该方法能显著地区分复杂路网中路段之间的重要性差异,准确地确定网络中的关键路径,具有更高的评估准确性。     

脉冲耦合神经网络的遥感影像城市道路提取

针对高分辨率遥感影像中阴影对道路提取产生较大干扰的问题,提出了一种基于脉冲耦合神经网络(PCNN)的城市道路提取方法。该方法首先在近红外波段检测并消除阴影和水体的影响,并使用PCNN对消除阴影后的灰度图像进行分割处理;然后使用形态学建筑物指数(MBI)和归一化差分植被指数(NDVI)分别提取出建筑物和植被信息,消除建筑物和植被的影响;最后提取受行道树影响较大的道路,并对处理后的图像作数学形态学法的处理。该文以深圳市SPOT-7高分辨率影像进行实验。实验表明,该方法能保留原始的道路边缘细节信息,并对阴影具有很好的抗干扰作用,提取的道路信息具有很好的连续性和完整性。     

北京市城区不同等级道路网对可吸入颗粒物的浓度影响研究

本文就北京市内不同等级道路网对可吸入颗粒物的浓度影响进行了研究,选取大气污染物中可吸入颗粒物PM10(包括PM0.3、PM0.5、PM1.0、PM3.0、PM5.0)为研究对象,采用半自动与目视解译相结合的方法提取北京市城区不同等级道路网,于2008年的采暖期与非采暖期在有代表意义的路面上选择42个采样点,分析对比不同等级路面点的可吸入颗粒物的个数和浓度,运用统计学以及GIS和RS等技术手段,进行不同等级道路网对可吸入颗粒物的浓度影响分析.     

城市路网变化信息提取及其对土地利用的影响研究

城市路网作为一种特殊的土地覆盖类型,与土地利用相互作用。现代城市系统中道路变化越来越频繁,如何快速精确的对城市路网的变化信息进行提取不仅可以支撑路网基础测绘数据的更新需要,也可以为城市土地利用提供辅助支持。论文基于遥感和矢量数据进行道路网变化信息提取,分析城市路网结构和变化与土地利用的关联关系。     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

基于特征基元的高分辨率遥感影像道路网自动提取技术

高分辨率遥感道路网络的自动提取在城市信息更新等方面具有非常重要的意义。在综述国内外道路信息提取进展的基础上,本文提出一套基于特征基元的道路网提取方法体系。即采取自下而上的研究路线("影像像元—特征基元—道路单元—道路网络"):首先通过影像大尺度的区域划分获取道路区域,在此基础上进行小尺度分割,提取出特征基元;然后根据基元的形态、走向、亮度、纹理等特征对基元进行模式分类,识别出道路单元;最后根据道路网语义规则将道路单元进行形态学处理及拓扑连接,形成道路网络。     

西安市路网时空演变与城市空间变化关系研究

为深入研究路网变化与城市空间变化的相互关系,基于扩展空间句法(sDNA)理论,以路网数据为数据源,通过道路接近度变化定性描述路网结构时空演变,利用道路接近度与城市边界变化、土地利用类型变化的相关系数定量探究相互影响关系,最后以道路密度为比较对象,反映道路接近度在表征城市空间变化方面的优势.结果 表明:2010-2018年间,西安市路网结构发生显著变化,前期路网骨架整体向外扩张,主要集中在东北部和西南部;后期路网骨架变化趋于稳定,变化主要体现在路网内部结构逐渐丰富;相比道路密度,道路接近度在反映路网变化时更直观、更准确、更全面,其与城市边界变化、土地利用类型变化之间的相关性更强.该研究为扩展空间句法在城市研究中的应用提供了新的参考方向.     

The network K‐function in context: examining the effects of network structure on the network K‐function

The flaws of using traditional planar point‐pattern analysis techniques with network constrained points have been thoroughly explored in the literature. Because of this, new network‐based measures have been introduced for their planar analogues, including the network based K‐function. These new measures involve the calculation of network distances between point events rather than traditional Euclidean distances. Some have suggested that the underlying structure of a network, such as whether it includes directional constraints or speed limits, may be considered when applying these methods. How different network structures might affect the results of the network spatial statistics is not well understood. This article examines the results of network K‐functions when taking into consideration network distances for three different types of networks: the original road network, topologically correct networks, and directionally constrained networks. For this aim, four scenarios using road networks from Tampa, Florida and New York City, New York were used to test how network constraints affected the network K‐function. Depending on which network is under consideration, the underlying network structure could impact the interpretation. In particular, directional constraints showed reduced clustering across the different scenarios. Caution should be used when selecting the road network, and constraints, for a network K‐function analysis.     

A novel framework for detecting non-recurrent road traffic anomalies by combining a temporal graph convolutional network and hierarchical time memory detector

A non-recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non-recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non-recurrent anomalies for their potential ability to distinguish non-recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non-recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T-GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the “normal” traffic speeds. The hierarchical time memory detector (HTM-detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non-recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T-GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM-detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection.     

复杂网络理论的武汉市路网结构特征

针对武汉市路网的基本结构特征,该文以道路stroke作为路网的基本结构单元,以对偶拓扑图作为路网几何拓扑结构的表达形式,基于复杂网络理论的相关定量分析指标,进行了研究分析。通过对路网节点度分布的统计分析表明:武汉市路网属于无标度网络;通过对网络的平均聚类参数和平均网络距离的计算分析表明:武汉市路网属于小世界网络;以度中心性、中介中心性、接近中心性和长度为基本评价指标,通过加权综合评价,研究了武汉市路网的层次结构特征,表明武汉市路网的结构构成符合"二八分率"的规律。     

千兆网支持下的城市公众GIS

当前GIS技术的发展已经进入到一个新的阶段,呈现出网络化、专业化、智能化、大众化等特点。公众GIS是近年来新兴的GIS应用领域,它主要的目的是面向公众提供信息服务。介绍了城市公众GIS的基本概念、特点和系统功能,在分析了当前公众GIS存在的问题之后,继而提出一种适合宽带网络(千兆以太网)支持下的公众GIS系统结构,并进一步分析了它的功能和用途。     

基于MO的城市公交查询系统的设计与开发

文章从城市公交信息管理的现状出发,分析城市公交查询系统建立的必要性。研究利用GIS的组件技术和计算机技术——MapObjects组件与可视化高级编程语言VC相结合,进行城市公交查询系统的开发研究,阐述了系统的设计过程及其功能特点。系统的实现充分证明组件GIS的高效无缝、扩展性强、开发成本低等优点,从而实现公交信息的数字化管理,为城市居民和外地游客的出行提供极大的便利。在功能应用上,相对于基于Web技术的查询系统具有更好的通用性和推广价值。     

基于GIS的城市交通网络要素可视化表达方法研究

城市交通网络要素的可视化表达是GIS—T研究和应用领域中的一个难题,本文在对目前流行的交通网络数据模型进行系统研究的基础上,选择了动态分段技术下的结点-弧段模型,对交通网络复杂要素进行可视化表达,并对城市交通网络要素的可视化表达方法和形式进行了分析,最后结合交通网络复杂要素的可视化表达,设计和创建了具体的数据结构表和数据结构图。     

Modeling relationships between the network distance and travel time dynamics for assessing equity of accessibility to urban parks

Static models of accessibility are usually based on the fixed distance or Average Travel Time (ATT) models. Because of ignoring the traffic as a dynamic process affecting the accessibility through the change of Travel Time (TT), these models lead to unperceived temporal inequities. In contrast to the consideration of the temporal Variation of TT (VTT) in the previous studies, the variation of traffic- related TT and its relations with network distance has not been considered. In this study, relations between VTT and network distance to access urban parks in Tehran megacity has been modeled. Traffic maps at five times of day are used to produce TT maps of Traffic Analysis Zones (TAZs) to their 3-closest parks. Comparison of the Gini coefficients of accessibility show significant inequities of accessibility at different times of day. Relations between the distance, ATT, and TTmax are modeled by statistical analysis. Results show both TT and TTmax have significant positive relations with distance and traffic and reach their maximum at 6 p.m. Observation of significant relations between distance, ATT, TTmax, and VTT provides interesting knowledge for the conversion of temporal measures of equity (TT) to a physical measure of equity (distance). A simple application of these findings for effective management of the spatiotemporal inequities is the definition of critical distances from public services. As an example, to decrease the TTmax of TAZs to less than 12 min, their maximum distance to the closest parks should be less than 4 km. The developed approach can be adopted for the accessibility evaluation of the other public services, particularly the health and education centers.     

对陡坡陡崖引发地质灾害的潜在影响范围进行建模分析

陡坡陡崖作为一种特殊的地形地貌,是造成滑坡、泥石流等各种地质灾害的重要因素之一.由陡坡陡崖引发的地质灾害会破坏人造地物,对人类的生命财产安全造成严重危害.本文利用DEM数据提取了浙江省温州市的陡坡陡崖,对温州市陡坡陡崖的影响范围进行系统研究,构建了温州市陡坡陡崖的潜在滑坡方向模型,并确定其对人造地物(如道路、房屋)的影...     

城市防空人口疏散网络模型研究

人口疏散是现代城市防空的基础工程,是一个庞大的系统工程。本文从地理网络的视角,并根据人口疏散网络建模的思想,对疏散网络元素进行了系统分析,构建了疏散网络的概念模型;利用动态分段技术下的结点—弧段模型对网络元素进行了逻辑表达分析,在此基础上设计了疏散网络的逻辑模型。     

Detection of clusters in traffic networks based on spatio‐temporal flow modeling

Spatio‐temporal clustering is a highly active research topic and a challenging issue in spatio‐temporal data mining. Many spatio‐temporal clustering methods have been designed for geo‐referenced time series. Under some special circumstances, such as monitoring traffic flow on roads, existing methods cannot handle the temporally dynamic and spatially heterogeneous correlations among road segments when detecting clusters. Therefore, this article develops a spatio‐temporal flow‐based approach to detect clusters in traffic networks. First, a spatio‐temporal flow process is modeled by combining network topology relations with real‐time traffic status. On this basis, spatio‐temporal neighborhoods are captured by considering traffic time‐series similarity in spatio‐temporal flows. Spatio‐temporal clusters are further formed by successive connection of spatio‐temporal neighbors. Experiments on traffic time series of central London's road network on both weekdays and weekends are performed to demonstrate the effectiveness and practicality of the proposed method.