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车流量作为衡量路况信息的重要因素,需要一个低成本且高效的可视化监控系统。本文对视频监控中的车流量信息进行了提取分析,采用背景差分的方法对视频中有关的车辆因子进行提取,包括车流量信息提取、车辆类型提取、车速提取,并将分析得到的数据通过Web GIS发布到网络上支持在线预览,结合GIS和视频监控的特点,解决了传统视频监控系统空间位置感较差的问题。 相似文献
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测绘技术硬件、软件技术的发展为三维地理空间数据的高效获取提供了有效便捷,随着测绘新技术的发展与成熟,为实景三维中国、新型基础设施建设等提供重要支撑。基于此,本文基于移动车载激光扫描点云数据,研究并提出一种道路交通指示标志检测方法,提升道路交通指示标志检测效果,探索智能化测绘的实际应用。首先,按照车载激光扫描系统采集车载点云数据时,存储的扫描点反射角度,构造双向扫描线索引,按照扫描线上车载点云数据的空间分布特征,通过移动动态窗口分类交通指示标志与其余地物车载点云数据;其次,通过Canny边缘检测算法,在交通指示标志车载点云数据内,提取交通指示标志边缘信息;最后,在双线性卷积神经网络内输入交通指示标志边缘信息,提取交通指示标志特征,结合支持向量机,输出交通指示标志检测结果。实验证明:该方法可有效采集道路交通环境的车载点云数据;可有效分类交通指示标志与其余物体车载点云数据,并完整提取交通指示标志边缘信息,完成道路交通指示标志检测;在不同光照条件下,该方法的道路交通指示标志检测的一个用来评价二分类模型优劣的常用指标AUC值均接近1,检测精度较高。 相似文献
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应用地理信息系统平台,选用组件式GIS技术,利用MapX5.0组件和高级语言VB6.0开发建立"开封市交通信息查询系统".阐述了该系统的功能、总体结构,实现了对开封市主要设施的空间查询和空间分析及获取最佳交通线路的方法,从而满足用户的出行要求. 相似文献
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被动式GPS技术能最大程度地减轻受访者的负担,因而成为收集个体交通行为信息的理想方法。但是被动式GPS技术仅能提供交通行为的时空、移动速度等信息,而无法直接获取个人出行和活动的详细信息,如出行的起始时间、出行目的、交通方式、活动起始时间、时长、同伴等。如何从GPS轨迹数据中准确获取这些关键的交通行为信息成为被动式GPS应用于个体时空间行为数据采集的难点所在。对该研究领域的发展现状和趋势进行了总体回顾和分析。首先,追溯了交通行为数据收集方法的发展历程,并重点概述近年来基于GPS原始数据的后续处理方法的发展现状。然后逐一分析和讨论了现有的基于被动式GPS数据的交通行为信息提取方法的优缺点和存在的问题。最后,对该领域的前景和潜在的研究问题以及对运用现代信息与通信技术(ICT)收集交通行为信息方法的发展方向提出了相关建议。 相似文献
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针对当今交通应用系统信息交换共享困难、系统建设成本高企等问题,本文提出了建设交通物联网中间件以降低智能交通应用系统建设的成本和周期。交通状况信息交换系统是物联网中间件的重要组成部分,负责大规模交通传感数据的汇聚及转发。通过消息服务系统、企业服务总线、路由规则引擎以及反向代理等技术,实现了交通状况信息交换系统,支持大规模交通状况实时信息的接入、汇聚、处理、转发以及大规模客户端的高并发访问,实现了交通状况信息高效稳定地交换共享。 相似文献
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主要介绍了基于ArcGIS Engine开发交通地理信息系统(GIS-T)--湖北省交通规划信息支持系统的经验,从系统的总体技术体系、软件架构、关键技术问题的解决方案等方面.本系统通过引入GIS-T思想和技术,提高了交通规划业务工作的效率,简化了业务工作流程.交通规划信息支持系统的建立,不仅解决了业务工作中存在的问题,... 相似文献
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通过对交通信息服务系统的现状分析,本文提出了基于浮动车数据的城市道路交通状态信息服务系统设计方案,阐述了系统的交通工程理论和系统技术基本原理,结合需求分析设计了系统的功能结构,重点研究了一种高效的数据存储管理设计方法,最后结合目前流行的富客户端技术研究了系统实现问题,提出了未来的发展趋势。 相似文献
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传统交通信息获取方法较难获取大范围、全覆盖的实时动态交通流信息,提出一种利用城市天际线作为观测平台的城市遥感视频生产与交通信息提取方法。首先,在超高层建筑物拍摄对地观测数据;然后,对原始倾斜视频观测数据进行正射校正,与卫星影像融合生成大范围城市遥感视频数据;最后,训练深度学习模型进行车辆分类识别,基于识别结果计算区域车辆数目及密度。在深圳平安大厦观光层开展数据采集并处理分析,结果表明,所提方法可生产低成本、长时间、大范围、高质量的城市遥感视频,基于该遥感视频开展的车辆检测误识别率和漏识别率低,车辆计数准确率高,可对区域交通流量进行有效监控,准确实时地获取城市内部区域交通情况。 相似文献
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基于稀疏浮动车数据的城市路网交通流速度估计 总被引:1,自引:1,他引:0
浮动车数据在时空维度呈现较强的稀疏性,是其应用于城市路网交通流估计所面临的主要难题之一。本文通过分析路网交通流速度的时空特征,构建了一种基于朴素贝叶斯法的估计模型,实现对路网中未被样本覆盖路段交通流速度的估计。时间特征主要考虑目标路段相邻时段的交通流速度,空间特征根据路段间交通流相似关系进行分析,突破了传统基于欧氏空间或拓扑关系的度量方式。结果显示,模型能有效地估计出样本缺失路段的交通流速度,且在精度方面相对传统基于拓扑关系的算法优势显著,较好地解决了数据时空稀疏性问题,对基于浮动车数据的交通应用具有较强的实践意义。 相似文献
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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. 相似文献
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灾后城市交通运输能力下降,原有的流量分配方案不再适用。为保障正常的经济社会活动,本文提出了一种基于改进蚁群算法的交通流量分配方法。首先评估路网通行能力影响因素并建立道路质量评价体系,利用路段质量改进蚁群算法中的启发式因子;然后为扩大蚁群搜索范围加入随机节点并改进信息素的更新机制;最后应用改进算法对城市交通总量进行分批分配并得到流量分配图。结果表明,改进算法综合考虑了出行距离和道路质量,较改进前更符合交通流量分配要求,具有较好的路径寻优性,可为灾后救援工作和灾后路网交通分配决策提供建议和支持。 相似文献
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Yan Shi Min Deng Jianya Gong Chang‐Tien Lu Xuexi Yang Huimin Liu 《Transactions in GIS》2019,23(2):312-333
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. 相似文献
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基于GPS浮动车采集交通信息的路段划分方法 总被引:1,自引:0,他引:1
针对目前GPS浮动车采集交通信息的路段划分方法大多忽略交叉口不同行驶方向车流运行条件的差别,且假设路段不同位置的交通状态均衡,从而导致交通信息质量偏低,无法有效满足交通状态判别和车辆动态导航系统数据需求的问题,设计了能够区分车流不同行驶方向统计交通数据的方向路段划分方法与区分路段不同位置统计交通数据的子路段划分方法,以便从路网空间数据结构方面改善交通状态判别和车辆动态导航系统的信息基础。 相似文献
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Yan Zhou Yanxi Li Qing Zhu Fen Chen Junming Shao Yunxin Luo Yeting Zhang Pengcheng Zhang Weijun Yang 《Transactions in GIS》2019,23(5):1125-1151
Bike‐sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike‐sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike‐sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non‐negative tensor decomposition and time‐series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across‐clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike‐sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow. 相似文献